{ "fetched_at": "2026-10-24T16:06:07Z", "source_url": "https://www.kaggle.com/competitions/cafa-5-protein-function-prediction/data", "pages": [ { "name": "rules", "content": "### One account per participant\n\nYou cannot sign up to Kaggle from multiple accounts and therefore you cannot submit from multiple accounts.\n\n### No private sharing outside teams\n\nPrivately sharing code or data outside of teams is not permitted. It's okay to share code if made available to all participants on the forums.\n\n### Team Mergers\n\nTeam mergers are allowed and can be performed by the team leader. In order to merge, the combined team must have a total submission count less than or equal to the maximum allowed as of the Team Merger Deadline. The maximum allowed is the number of submissions per day multiplied by the number of days the competition has been running.\n\n### Team Limits\n\nThe maximum team size is 5.\n\n### Submission Limits\n\nYou may submit a maximum of 5 entries per day.\n\nYou may select up to 2 final submissions for judging.\n\n### Competition Timeline\n\nCompetition Timeline dates (including Entry Deadline, Final Submission Deadline, Start Date, and Team Merger Deadline, as applicable) are reflected on the competition’s Overview > Timeline page.\n\n### COMPETITION-SPECIFIC TERMS\n\n**COMPETITION TITLE**: CAFA 5 Protein Function Prediction\n\n**COMPETITION HOST**: Critical Assessment of Functional Annotation (CAFA)\n\n**COMPETITION SPONSOR**: Iowa State University\n\n**COMPETITION SPONSOR ADDRESS**: 1805 Collaboration Place, Suite 2100, Ames, IA 50010\n\n**COMPETITION WEBSITE**: https://www.kaggle.com/competitions/cafa-5-protein-function-prediction\n\n**TOTAL PRIZES AVAILABLE: $50,000**\n\n* First Place - $ 15,000\n* Second Place - $ 10,000\n* Third Place - $ 8,000\n* Fourth Place - $ 7,000\n* Fifth Place - $ 5,000\n* Sixth Place - $ 5,000\n\n\n**WINNER LICENSE TYPE**: Open Source\n\n**DATA ACCESS AND USE**: CC-BY 4.0 : Creative Commons with Attribution\n\nCompetitions are open to residents of the United States and worldwide, except that if you are a resident of Crimea, so-called Donetsk People's Republic (DNR) or Luhansk People's Republic (LNR), Cuba, Iran, Syria, North Korea, or are subject to U.S. export controls or sanctions, you may not enter the Competition. Other local rules and regulations may apply to you, so please check your local laws to ensure that you are eligible to participate in skills-based competitions. The Competition Host reserves the right to forego or award alternative Prizes where needed to comply with local laws. If a winner is located in a country where prizes cannot be awarded then they are not eligible to receive a prize.\n\n**ENTRY IN THIS COMPETITION CONSTITUTES YOUR ACCEPTANCE OF THESE OFFICIAL COMPETITION RULES.**\n\nThe Competition named above is a skills-based competition to promote and further the field of data science. You must register via the Competition Website to enter. Your competition submissions (\"Submissions\") must conform to the requirements stated on the Competition Website. Your Submissions will be scored based on the evaluation metric described on the Competition Website. Subject to compliance with the Competition Rules, Prizes, if any, will be awarded to participants with the best scores, based on the merits of the data science models submitted. See below for the complete Competition Rules.\n\n### A. COMPETITION-SPECIFIC RULES\n\nIn addition to the provisions of the General Competition Rules below, you understand and agree to these Competition-Specific Rules required by the Competition Sponsor:\n\n\n#### **1. WINNER LICENSE.** \n\nUnder Section 11 (Winners Obligations) of the General Rules below, you hereby grant and will grant Competition Sponsor the following license(s) with respect to your Submission if you are a Competition winner:\n\n*Open Source*: You hereby license and will license your winning Submission and the source code used to generate the Submission under an Open Source Initiative-approved license (see [www.opensource.org](http://www.opensource.org)) that in no event limits commercial use of such code or model containing or depending on such code. To the extent your Submission makes use of generally commercially available software not owned by you that you used to generate your submission, but that can be procured by the Competition Sponsor without undue expense, you do not grant the license in the preceding sentence to that software.\n\n#### **2. CHANGES TO PREVIOUS CAFA RULES**\nAny previous [CAFA rules](https://www.synapse.org/#!Synapse:syn21035948/wiki/597157) that conflict with rules listed here do not apply. For example, no individual can participate in more than one team.\n\n \n\n#### **3. AUTOMATED MACHINE LEARNING TOOLS (AMLT).** \n\nIndividual participants and Teams may use automated machine learning tool(s) (“AMLT”) (e.g., Google AutoML, H2O Driverless AI, etc.) to create a Submission, provided that the participant or Team ensures that they have an appropriate license to the AMLT such that they are able to comply with the Competition Rules. \n \nThe following provision is added to the end of Section 11 of the General Competition Rules below: “Individual participants and Teams who create a Submission using an AMLT may win a Prize. However, for clarity, the potential winner’s Submission must still meet the requirements of these Rules, including but not limited to Section A.1 (Winners License), Section B.11 (Winners Obligations), and Section B.17 (Warranty, Indemnity, and Release).” \n\n### B. GENERAL COMPETITION RULES\n#### **1.\tBINDING AGREEMENT.** \n\nTo enter the Competition, you must agree to these Official Competition Rules, which incorporate by reference the provisions and content of the Competition Website and any Specific Competition Rules above (collectively, the \"Rules\"). Please read these Rules carefully before entry to ensure you understand and agree. You further agree that submission of an entry in the Competition constitutes agreement to these Rules. You may not submit an entry to the Competition and are not eligible to receive the prizes associated with this Competition (“Prizes”) unless you agree to these Rules. These Rules form a binding legal agreement between you and the Competition Sponsor with respect to the Competition.\n\n#### **2.\tELIGIBILITY.** \n\nA. To be eligible to enter the Competition, you must be:\n\t(i) a registered account holder at Kaggle.com; \n\t(ii) the older of 18 years old or the age of majority in your jurisdiction of residence (unless otherwise agreed to by Competition Sponsor and appropriate parental/guardian consents have been obtained by Competition Sponsor); \n\t(iii) not a resident of Crimea, so-called Donetsk People's Republic (DNR) or Luhansk People's Republic (LNR), Cuba, Iran, Syria, or North Korea; and\n\t(iv) not a person or representative of an entity under U.S. export controls or sanctions (see [https://www.treasury.gov/resource-center/sanctions/Programs/Pages/Programs.aspx][1]).\n\nIf you are entering as a representative of a company, educational institution or other legal entity, or on behalf of your employer, these rules are binding on you, individually, and the entity you represent or are an employee. If you are acting within the scope of your employment, as an employee, contractor, or agent of another party, you warrant that such party has full knowledge of your actions and has consented thereto, including your potential receipt of a Prize. You further warrant that your actions do not violate your employer's or entity's policies and procedures. \n\nThe Competition Sponsor reserves the right to verify eligibility and to adjudicate on any dispute at any time. If you provide any false information relating to the Competition concerning your identity, residency, mailing address, telephone number, email address, ownership of right, or information required for entering the Competition, you may be immediately disqualified from the Competition.\n\nB. Unless otherwise stated in the Specific Competition Rules above or prohibited by internal policies of the Competition Entities, employees, interns, contractors, officers and directors of Competition Entities may enter and participate in the Competition, but are not eligible to win any Prizes. \"Competition Entities\" means the CAFA competition organizers and all members of their respective research teams, data providers, and Kaggle Inc. If you are such a participant from a Competition Entity, you are subject to all applicable internal policies of your employer with respect to your participation.\n\n#### **3.\tSPONSOR AND HOSTING PLATFORM.** \n\nThe Competition is sponsored by Competition Sponsor named above. The Competition is hosted on behalf of Competition Sponsor by Kaggle Inc. (\"Kaggle\"). Kaggle is an independent contractor of Competition Sponsor, and is not a party to this or any agreement between you and Competition Sponsor. You understand that Kaggle has no responsibility with respect to selecting the potential Competition winner(s) or awarding any Prizes. Kaggle will perform certain administrative functions relating to hosting the Competition, and you agree to abide by the provisions relating to Kaggle under these Rules. As a Kaggle.com account holder and user of the Kaggle competition platform, remember you have accepted and are subject to the Kaggle Terms of Service at [www.kaggle.com/terms][2] in addition to these Rules.\n\n#### **4.\tCOMPETITION PERIOD.** \n\nFor the purposes of Prizes, the Competition will run from the Start Date and time to the Final Submission Deadline (such duration the “Competition Period”). The Competition Timeline is subject to change, and Competition Sponsor may introduce additional hurdle deadlines during the Competition Period. Any updated or additional deadlines will be publicized on the Competition Website. It is your responsibility to check the Competition Website regularly to stay informed of any deadline changes. *YOU ARE RESPONSIBLE FOR DETERMINING THE CORRESPONDING TIME ZONE IN YOUR LOCATION.* \n\n#### **5.\tCOMPETITION ENTRY.** \n\nNO PURCHASE NECESSARY TO ENTER OR WIN. To enter the Competition, you must register on the Competition Website prior to the Entry Deadline, and follow the instructions for developing and entering your Submission through the Competition Website. Your Submissions must be made in the manner and format, and in compliance with all other requirements, stated on the Competition Website (the \"Requirements\"). Submissions must be received before any Submission deadlines stated on the Competition Website. Submissions not received by the stated deadlines will not be eligible to receive a Prize.\n\nSubmissions may not use or incorporate information from hand labeling or human prediction of the validation dataset or test data records.\n\nIf the Competition is a multi-stage competition with temporally separate training and/or test data, one or more valid Submissions may be required during each Competition stage in the manner described on the Competition Website in order for the Submissions to be Prize eligible.\n\nSubmissions are void if they are in whole or part illegible, incomplete, damaged, altered, counterfeit, obtained through fraud, or late. Competition Sponsor reserves the right to disqualify any entrant who does not follow these Rules, including making a Submission that does not meet the Requirements. \n\n#### **6.\tINDIVIDUALS AND TEAMS.** \n\nA. *Individual Account*. You may make Submissions only under one, unique Kaggle.com account. You will be disqualified if you make Submissions through more than one Kaggle account, or attempt to falsify an account to act as your proxy. You may submit up to the maximum number of Submissions per day as specified on the Competition Website. \n\nB. *Teams*. If permitted under the Competition Website guidelines, multiple individuals may collaborate as a team (a \"Team\"); however, you may join or form only one Team. Each Team member must be a single individual with a separate Kaggle account. You must register individually for the Competition before joining a Team. You must confirm your Team membership to make it official by responding to the Team notification message sent to your Kaggle account. Team membership may not exceed the Maximum Team Size stated on the Competition Website.\n\nC. *Team Merger*. Teams may request to merge via the Competition Website. Team mergers may be allowed provided that: (i) the combined Team does not exceed the Maximum Team Size; (ii) the number of Submissions made by the merging Teams does not exceed the number of Submissions permissible for one Team at the date of the merger request; (iii) the merger is completed before the earlier of: any merger deadline or the Competition deadline; and (iv) the proposed combined Team otherwise meets all the requirements of these Rules. \n\n#### **7.\tCOMPETITION DATA.** \n\n\"Competition Data\" means the data or datasets available from the Competition Website for the purpose of use in the Competition, including any prototype or executable code provided on the Competition Website. The Competition Data will contain private and public test sets. Which data belongs to which set will not be made available to participants. \n\nA. *Data Access and Use*. \n \n*Competition Use and Non-Commercial & Academic Research*: You may access and use the Competition Data for non-commercial purposes only, including for participating in the Competition and on Kaggle.com forums, and for academic research and education. The Competition Sponsor reserves the right to disqualify any participant who uses the Competition Data other than as permitted by the Competition Website and these Rules.\n \nThe data are licensed under a Creative Commons Attribution 4.0 International License. You are free to share and adapt the data for any purpose, as long as you attribute the CAFA 5 Kaggle competition and include this URL as part of the credit: [https://www.kaggle.com/competitions/cafa-5-protein-function-prediction/](https://www.kaggle.com/competitions/cafa-5-protein-function-prediction/) You must also provide a link to the license ([https://creativecommons.org/licenses/by/4.0/](https://creativecommons.org/licenses/by/4.0/) ), and indicate if changes were made. You may do so in any reasonable manner, but not in any way that suggests the licensor endorses you or your use.\n\n \n\nFor full licensing terms see: [https://creativecommons.org/licenses/by/4.0/](https://creativecommons.org/licenses/by/4.0/)\n\nB. *Data Security*. You agree to use reasonable and suitable measures to prevent persons who have not formally agreed to these Rules from gaining access to the Competition Data. You agree not to transmit, duplicate, publish, redistribute or otherwise provide or make available the Competition Data to any party not participating in the Competition. You agree to notify Kaggle immediately upon learning of any possible unauthorized transmission of or unauthorized access to the Competition Data and agree to work with Kaggle to rectify any unauthorized transmission or access.\n\nC. *External Data*. You may use data other than the Competition Data (“External Data”) to develop and test your Submissions. However, you will ensure the External Data is publicly available and equally accessible to use by all participants of the Competition for purposes of the competition at no cost to the other participants. The ability to use External Data under this Section 7.C (External Data) does not limit your other obligations under these Competition Rules, including but not limited to Section 11 (Winners Obligations). \n\n#### **8.\tSUBMISSION CODE REQUIREMENTS.**\n\nA. *Private Code Sharing*. Unless otherwise specifically permitted under the Competition Website or Competition Specific Rules above, during the Competition Period, you are not allowed to privately share source or executable code developed in connection with or based upon the Competition Data or other source or executable code relevant to the Competition (“Competition Code”). This prohibition includes sharing Competition Code between separate Teams, unless a Team merger occurs. Any such sharing of Competition Code is a breach of these Competition Rules and may result in disqualification.\n\nB. *Public Code Sharing*. You are permitted to publicly share Competition Code, provided that such public sharing does not violate the intellectual property rights of any third party. If you do choose to share Competition Code or other such code, you are required to share it on Kaggle.com on the discussion forum or notebooks associated specifically with the Competition for the benefit of all competitors. By so sharing, you are deemed to have licensed the shared code under an Open Source Initiative-approved license (see [www.opensource.org][3]) that in no event limits commercial use of such Competition Code or model containing or depending on such Competition Code.\n\nC. *Use of Open Source*. Unless otherwise stated in the Specific Competition Rules above, if open source code is used in the model to generate the Submission, then you must only use open source code licensed under an Open Source Initiative-approved license (see [www.opensource.org][4]) that in no event limits commercial use of such code or model containing or depending on such code.\n\n#### **9.\tDETERMINING WINNERS.** \n\nEach Submission will be scored and ranked by the evaluation metric stated on the Competition Website. During the Competition Period, the current ranking will be visible on the Competition Website's public leaderboard. The potential winner(s) are determined solely by the leaderboard ranking on the private leaderboard, subject to compliance with these Rules. The public leaderboard will be based on the public test set and the private leaderboard will be based on the private test set.\n\nIn the event of a tie, the Submission that was entered first to the Competition will be the winner. In the event a potential winner is disqualified for any reason, the Submission that received the next highest score rank will be chosen as the potential winner.\n\n#### **10.\tNOTIFICATION OF WINNERS & DISQUALIFICATION.**\n\nThe potential winner(s) will be notified by email. \n \nIf a potential winner (i) does not respond to the notification attempt within one (1) week from the first notification attempt or (ii) notifies Kaggle within one week after the Final Submission Deadline that the potential winner does not want to be nominated as a winner or does not want to receive a Prize, then, in each case (i) and (ii) such potential winner will not receive any Prize, and an alternate potential winner will be selected from among all eligible entries received based on the Competition’s judging criteria.\n \nIn case (i) and (ii) above Kaggle may disqualify the participant. However, in case (ii) above, if requested by Kaggle, such potential winner may provide code and documentation to verify the participant’s compliance with these Rules. If the potential winner provides code and documentation to the satisfaction of Kaggle, the participant will not be disqualified pursuant to this paragraph.\n \nCompetition Sponsor reserves the right to disqualify any participant from the Competition if the Competition Sponsor reasonably believes that the participant has attempted to undermine the legitimate operation of the Competition by cheating, deception, or other unfair playing practices or abuses, threatens or harasses any other participants, Competition Sponsor or Kaggle.\n \nA disqualified participant may be removed from the Competition leaderboard, at Kaggle's sole discretion. If a Participant is removed from the Competition Leaderboard, additional winning features associated with the Kaggle competition platform, for example Kaggle points or medals, may also not be awarded.\n \nThe final leaderboard list will be publicly displayed at Kaggle.com. Determinations of Competition Sponsor are final and binding.\n\n#### **11.\tWINNERS OBLIGATIONS.**\n\nAs a condition to being awarded a Prize, a Prize winner must fulfill the following obligations:\n\n(a) deliver to the Competition Sponsor the final model's software code as used to generate the winning Submission and associated documentation. The delivered software code should follow [these documentation guidelines][5], must be capable of generating the winning Submission, and contain a description of resources required to build and/or run the executable code successfully. To the extent that the final model’s software code includes generally commercially available software that is not owned by you, but that can be procured by the Competition Sponsor without undue expense, then instead of delivering the code for that software to the Competition Sponsor, you must identify that software, method for procuring it, and any parameters or other information necessary to replicate the winning Submission;\n\n(b) grant to the Competition Sponsor the license to the winning Submission stated in the Competition Specific Rules above, and represent that you have the unrestricted right to grant that license; \n\n(c) sign and return all Prize acceptance documents as may be required by Competition Sponsor or Kaggle, including without limitation: (i) eligibility certifications; (ii) licenses, releases and other agreements required under the Rules; and (iii) U.S. tax forms (such as IRS Form W-9 if U.S. resident, IRS Form W-8BEN if foreign resident, or future equivalents).\n\n#### **12.\tPRIZES.** \n\nPrize(s) are as described on the Competition Website and are only available for winning during the time period described on the Competition Website. The odds of winning any Prize depends on the number of eligible Submissions received during the Competition Period and the skill of the participants. \n\nAll Prizes are subject to Competition Sponsor's review and verification of the participant’s eligibility and compliance with these Rules, and the compliance of the winning Submissions with the Submissions Requirements. In the event that the Submission demonstrates non-compliance with these Competition Rules, Competition Sponsor may at its discretion take either of the following actions: (i) disqualify the Submission(s); or (ii) require the potential winner to remediate within one week after notice all issues identified in the Submission(s) (including, without limitation, the resolution of license conflicts, the fulfillment of all obligations required by software licenses, and the removal of any software that violates the software restrictions).\n\nA potential winner may decline to be nominated as a Competition winner in accordance with Section 10.\n\nPotential winners must return all required Prize acceptance documents within two (2) weeks following notification of such required documents, or such potential winner will be deemed to have forfeited the prize and another potential winner will be selected. Prize(s) will be awarded within approximately 30 days after receipt by Competition Sponsor or Kaggle of the required Prize acceptance documents. Transfer or assignment of a Prize is not allowed. \n\nYou are not eligible to receive any Prize if you do not meet the Eligibility requirements in Section 2 above.\n\nIf a Team wins a monetary Prize, the Prize money will be allocated in even shares between the eligible Team members, unless the Team unanimously opts for a different Prize split and notifies Kaggle before Prizes are issued.\n\n#### **13.\tTAXES.** \n\nALL TAXES IMPOSED ON PRIZES ARE THE SOLE RESPONSIBILITY OF THE WINNERS. Payments to potential winners are subject to the express requirement that they submit all documentation requested by Competition Sponsor or Kaggle for compliance with applicable state, federal, local and foreign (including provincial) tax reporting and withholding requirements. Prizes will be net of any taxes that Competition Sponsor is required by law to withhold. If a potential winner fails to provide any required documentation or comply with applicable laws, the Prize may be forfeited and Competition Sponsor may select an alternative potential winner. Any winners who are U.S. residents will receive an IRS Form-1099 in the amount of their Prize.\n\n#### **14.\tGENERAL CONDITIONS.**  \n\nAll federal, state, provincial and local laws and regulations apply. \n\n#### **15.\tPUBLICITY.**\n\nYou agree that Competition Sponsor, Kaggle and its affiliates may use your name and likeness for advertising and promotional purposes without additional compensation, unless prohibited by law.\n\n#### **16.\tPRIVACY.**\n\nYou acknowledge and agree that Competition Sponsor and Kaggle may collect, store, share and otherwise use personally identifiable information provided by you during the Kaggle account registration process and the Competition, including but not limited to, name, mailing address, phone number, and email address (“Personal Information”). Kaggle acts as an independent controller with regard to its collection, storage, sharing, and other use of this Personal Information, and will use this Personal Information in accordance with its Privacy Policy <[www.kaggle.com/privacy][6]>, including for administering the Competition. As a Kaggle.com account holder, you have the right to request access to, review, rectification, portability or deletion of any personal data held by Kaggle about you by logging into your account and/or contacting Kaggle Support at <[www.kaggle.com/contact][7]>.\n\n As part of Competition Sponsor performing this contract between you and the Competition Sponsor, Kaggle will transfer your Personal Information to Competition Sponsor, which acts as an independent controller with regard to this Personal Information. As a controller of such Personal Information, Competition Sponsor agrees to comply with all U.S. and foreign data protection obligations with regard to your Personal Information. Kaggle will transfer your Personal Information to Competition Sponsor in the country specified in the Competition Sponsor Address listed above, which may be a country outside the country of your residence. Such country may not have privacy laws and regulations similar to those of the country of your residence.\n\n#### **17.\tWARRANTY, INDEMNITY AND RELEASE.** \n\nYou warrant that your Submission is your own original work and, as such, you are the sole and exclusive owner and rights holder of the Submission, and you have the right to make the Submission and grant all required licenses. You agree not to make any Submission that: (i) infringes any third party proprietary rights, intellectual property rights, industrial property rights, personal or moral rights or any other rights, including without limitation, copyright, trademark, patent, trade secret, privacy, publicity or confidentiality obligations, or defames any person; or (ii) otherwise violates any applicable U.S. or foreign state or federal law.\n\nTo the maximum extent permitted by law, you indemnify and agree to keep indemnified Competition Entities at all times from and against any liability, claims, demands, losses, damages, costs and expenses resulting from any of your acts, defaults or omissions and/or a breach of any warranty set forth herein. To the maximum extent permitted by law, you agree to defend, indemnify and hold harmless the Competition Entities from and against any and all claims, actions, suits or proceedings, as well as any and all losses, liabilities, damages, costs and expenses (including reasonable attorneys fees) arising out of or accruing from: (a) your Submission or other material uploaded or otherwise provided by you that infringes any third party proprietary rights, intellectual property rights, industrial property rights, personal or moral rights or any other rights, including without limitation, copyright, trademark, patent, trade secret, privacy, publicity or confidentiality obligations, or defames any person; (b) any misrepresentation made by you in connection with the Competition; (c) any non-compliance by you with these Rules or any applicable U.S. or foreign state or federal law; (d) claims brought by persons or entities other than the parties to these Rules arising from or related to your involvement with the Competition; and (e) your acceptance, possession, misuse or use of any Prize, or your participation in the Competition and any Competition-related activity.\n\nYou hereby release Competition Entities from any liability associated with: (a) any malfunction or other problem with the Competition Website; (b) any error in the collection, processing, or retention of any Submission; or (c) any typographical or other error in the printing, offering or announcement of any Prize or winners.\n\n#### **18.\tINTERNET.** \n\nCompetition Entities are not responsible for any malfunction of the Competition Website or any late, lost, damaged, misdirected, incomplete, illegible, undeliverable, or destroyed Submissions or entry materials due to system errors, failed, incomplete or garbled computer or other telecommunication transmission malfunctions, hardware or software failures of any kind, lost or unavailable network connections, typographical or system/human errors and failures, technical malfunction(s) of any telephone network or lines, cable connections, satellite transmissions, servers or providers, or computer equipment, traffic congestion on the Internet or at the Competition Website, or any combination thereof, which may limit a participant’s ability to participate.\n\n#### **19.\tRIGHT TO CANCEL, MODIFY OR DISQUALIFY.**\n\nIf for any reason the Competition is not capable of running as planned, including infection by computer virus, bugs, tampering, unauthorized intervention, fraud, technical failures, or any other causes which corrupt or affect the administration, security, fairness, integrity, or proper conduct of the Competition, Competition Sponsor reserves the right to cancel, terminate, modify or suspend the Competition. Competition Sponsor further reserves the right to disqualify any participant who tampers with the submission process or any other part of the Competition or Competition Website. Any attempt by a participant to deliberately damage any website, including the Competition Website, or undermine the legitimate operation of the Competition is a violation of criminal and civil laws. Should such an attempt be made, Competition Sponsor and Kaggle each reserves the right to seek damages from any such participant to the fullest extent of the applicable law.\n\n#### **20.\tNOT AN OFFER OR CONTRACT OF EMPLOYMENT.**\n\nUnder no circumstances will the entry of a Submission, the awarding of a Prize, or anything in these Rules be construed as an offer or contract of employment with Competition Sponsor or any of the Competition Entities. You acknowledge that you have submitted your Submission voluntarily and not in confidence or in trust. You acknowledge that no confidential, fiduciary, agency, employment or other similar relationship is created between you and Competition Sponsor or any of the Competition Entities by your acceptance of these Rules or your entry of your Submission.\n\n#### **21.\tGOVERNING LAW.** \n\nUnless otherwise provided in the Competition Specific Rules above, all claims arising out of or relating to these Rules will be governed by Iowa law, excluding its conflict of laws rules, and will be litigated exclusively in the Federal or State courts of Story County, Iowa, USA. The parties consent to personal jurisdiction in those courts. If any provision of these Rules is held to be invalid or unenforceable, all remaining provisions of the Rules will remain in full force and effect.\n\n\n [1]: https://www.treasury.gov/resource-center/sanctions/Programs/Pages/Programs.aspx\n [2]: http://www.kaggle.com/terms\n [3]: http://www.opensource.org\n [4]: http://www.opensource.org\n [5]: https://www.kaggle.com/WinningModelDocumentationGuidelines\n [6]: http://www.kaggle.com/privacy\n [7]: http://www.kaggle.com/contact", "isPublished": false, "hasIsPublished": false, "postTitle": "", "hasPostTitle": false, "mimeType": "", "hasMimeType": false }, { "name": "Description", "content": "## Goal of the Competition\n\nThe goal of this competition is to predict the function of a set of proteins. You will develop a model trained on the amino-acid sequences of the proteins and on other data. Your work will help ​​researchers better understand the function of proteins, which is important for discovering how cells, tissues, and organs work. This may also aid in the development of new drugs and therapies for various diseases.\n\n## Context\n\nProteins are responsible for many activities in our tissues, organs, and bodies and they also play a central role in the structure and function of cells. Proteins are large molecules composed of 20 types of building-blocks known as amino acids. The human body makes tens of thousands of different proteins, and each protein is composed of dozens or hundreds of amino acids that are linked sequentially. This amino-acid sequence determines the 3D structure and conformational dynamics of the protein, and that, in turn, determines its biological function. Due to ongoing genome sequencing projects, we are inundated with large amounts of genomic sequence data from thousands of species, which informs us of the amino-acid sequence data of proteins for which these genes code. The accurate assignment of biological function to the protein is key to understanding life at the molecular level. However, assigning function to any specific protein can be made difficult due to the multiple functions many proteins have, along with their ability to interact with multiple partners. More knowledge of the functions assigned to proteins—potentially aided by data science—could lead to curing diseases and improving human and animal health and wellness in areas as varied as medicine and agriculture.\n\nResearch groups have developed many ways to determine the function of proteins, including numerous methods based on comparing unsolved sequences with databases of proteins whose functions are known. Other efforts aim to mine the scientific literature associated with some of these proteins, while even more methods combine sophisticated machine-learning algorithms with an understanding of biological processes to decipher what these proteins do. However, there are still many challenges in this field, which are driven by ambiguity, complexity, and data integration.\n\n## Competition Host\n\nThe Function Community of Special Interest (Function-COSI) brings together computational biologists, experimental biologists, and biocurators who are dealing with the important problem of gene and gene product function prediction, to share ideas and create collaborations. The Function-COSI holds annual meetings at the Intelligent Systems for Molecular Biology ([ISMB](https://www.iscb.org/ismbeccb2023)) conference and conducts the multi-year Critical Assessment of protein Function Annotation ([CAFA](https://biofunctionprediction.org)) experiment, an ongoing, global, community-driven effort to evaluate and improve the computational annotation of protein function.\n\n\nCAFA is co-chaired by [Iddo Friedberg](https://friedberglab.net/) (Iowa State University) and [Predrag Radivojac](https://www.ccs.neu.edu/home/radivojac/) (Northeastern University). Additional academic co-organizers of this Kaggle competition include [M. Clara De Paolis Kaluza](http://claradepaolis.com) (Northeastern University), [Parnal Joshi](https://www.bcb.iastate.edu/people/parnal-joshi) (Iowa State University), UniProt (European Bioinformatics Institute), and Damiano Piovesan (University of Padova). \n\n\n## Acknowledgments\n\n\n\n\n\n\n\nWe gratefully acknowledge the support of Iowa State University who is hosting this competition. We also acknowledge the support of Northeastern University, University of Padova, UniProt, and the International Society for Computational Biology\n\n\n\n\n\n\n\n\n\n
\n> **This is a Code Competition. Refer to [Code Requirements](https://www.kaggle.com/c/cafa-5-protein-function-prediction/overview/code-requirements) for details.**", "isPublished": false, "hasIsPublished": false, "postTitle": "", "hasPostTitle": false, "mimeType": "", "hasMimeType": false }, { "name": "Evaluation", "content": "## Important Note\nThis is a prospective (i.e., future) data competition. Many proteins in the Test data do not currently have any assigned functions. Proteins having one or more of their functions published by researchers during the curation phase of the competition will comprise the future test set. Final leaderboard scores will be calculated after the curation phase of the competition.\n\n## Background\nThe organizers provide a set of protein sequences on which the participants are asked to predict Gene Ontology (GO) terms in each of the three subontologies: Molecular Function (MF), Biological Process (BP), and Cellular Component (CC). This set of sequences is referred to as *test superset*.\n\nThe proteins from the test superset that originally had no experimentally assigned functions in a particular subontology and accumulate experimental annotations between the submission deadline and the time of evaluation in that subontology, are referred as the *test set* for that subontology. There will be three different test sets, one for each subontology, and the participants will be scored on each. The final performance accuracy will be computed by combining the three scores, as described below under Evaluation Metrics.\n\nThe organizers also provide the *training set* containing protein sequences that have at least one experimentally determined GO term in at least one subontology, together with those experimental annotations. These proteins may also appear in the test superset. Of course, they will not be used for evaluation in a particular subontology unless they had 0 experimentally determined terms in that subontology before the submission deadline. \n\n## Evaluation Metrics\nSubmissions will be evaluated on proteins (test set) that did not have experimentally determined functional annotations in at least one subontology of the Gene Ontology (GO) before the submission deadline and have accumulated experimentally-validated functional annotations in that subontology between the submission deadline and the time of evaluation. For example, a protein that had no experimental terms in say Molecular Function (MF) subontology of GO and has accumulated experimental annotations in MF after the submission deadline will be included in the test set for evaluating the MF term predictions. The same holds for the Biological Process (BP) or Cellular Component (CC) subontologies of GO. The proteins that qualify will create three different test sets, one for each subontology of GO. The same protein can appear in more than one test set if it accumulates experimentally-validated annotations in more than a single subontology.\n\nThe maximum F-measure based on the weighted precision and recall will be calculated on each of the three test sets and the final performance measure will be an arithmetic mean of the three maximum F-measures (for MF, BP, and CC). The formulas for computing weighted F-measures are provided in the [supplement](https://ndownloader.figstatic.com/files/7128245) (page 31) of the following [paper](https://genomebiology.biomedcentral.com/articles/10.1186/s13059-016-1037-6)\n\nJiang Y, *et al*. An expanded evaluation of protein function prediction methods shows an improvement in accuracy. *Genome Biol*. (2016) 17(1): 184. \n\nin the full evaluation mode. The weights (i.e., information content *ic*(*f*), where *f* is a term in any subontology) for each term *f* of each subontology are provided by the challenge organizers. Note that we equivalently refer to those weights as *ia*(*f*), called information accretion for the functional term *f*. The rationale for using weighted precision and recall is that GO is hierarchical and thus, the terms on top of the hierarchy are implied by their descendants. The weight for a term is determined by the logarithm of the frequency of occurrence of that term in a large pool of proteins. The root terms appear in every protein's annotation and thus, their weights are 0. Terms deep in the ontology tend to appear less frequently, be harder to predict, and thus their weights are larger (Clark & Radivojac, 2013). This does not always hold true however, as highlighted in the [following discussion](https://www.kaggle.com/competitions/cafa-5-protein-function-prediction/discussion/405237).\n\nUsing the terminology from Jiang et al. (2016), the evaluation will be carried out for no-knowledge and limited-knowledge protein targets combined, in the full evaluation mode, using maximum F-measures of information-accretion weighted precision and recall, one for each subontology. The three maximum F-measures will be combined as an arithmetic mean to compute the final performance. The code used for evaluation is publicly available [here](https://github.com/BioComputingUP/CAFA-evaluator).\n\nClark WT, Radivojac P. Information-theoretic evaluation of predicted ontological annotations. *Bioinformatics* (2013) 29(13): i53-i61.\n\n## Leaderboard \nThe participants are cautioned that the leaderboard was designed to display method performance on a relatively small selection of proteins from the test superset (see Data), provided to us by the UniProtKB team but not available in UniProtKB or other public databases. These proteins will not be included in the test set for the subontologies used for the leaderboard evaluation. The final test set will consist of proteins that will have accumulated functional terms after the submission deadline and therefore, some distribution shift between the sample of proteins used for the leaderboard and the final evaluation sample is to be expected. Overall, the participants are encouraged to maximize the generalization performance and use the leaderboard only as a rough indicator of their model's performance.\n\n## Submission File\nThe list of predictions contains a list of pairs between protein targets and GO terms, followed by the probabilistic estimate of the relationship (one association per line). The target name must correspond to the target ID listed in the test set (in the FASTA header for each sequence). The GO ID must correspond to valid terms in GO's version listed in the Data section---invalid terms are automatically excluded from evaluation. Molecular Function (MF), Biological Process (BP), and Cellular Component (CC) subontologies of GO are to be combined in the prediction files, but they will be evaluated independently and combined at the end as described above. The score must be in the interval (0, 1.000] and contain up to 3 (three) significant figures. A score of 0 is not allowed; that is, the team should simply not list such pairs. In case the predictions in the submitted files are not propagated to the root of ontology, the predictions will be recursively propagated by assigning each parent term a score that is the maximum score among its children's scores. Finally, to limit prediction file sizes, one target cannot be associated with more than 1500 terms for MF, BP, and CC subontologies combined. \n\nFor any protein ID in the test superset, you must list a set of GO terms and assign your estimated probability. If a protein ID is not listed in your submitted file, the organizers will assume that all predictions are 0. The file should not contain a header, columns must be tab or space separated. An example submission file may look as follows:\n\n```\nP9WHI7 GO:0009274 0.931 \nP9WHI7 GO:0071944 0.540\nP9WHI7 GO:0005575 0.324\nP04637 GO:1990837 0.23\nP04637 GO:0031625 0.989\nP04637 GO:0043565 .64\nP04637 GO:0001091 0.49\netc.\n```\n\nThe participants can manually investigate the UniProtKB entries for [P9WHI7](https://www.uniprot.org/uniprotkb/P9WHI7/entry) and [P04637](https://www.uniprot.org/uniprotkb/P04637/entry) to familiarize themselves with biological databases.", "isPublished": false, "hasIsPublished": false, "postTitle": "", "hasPostTitle": false, "mimeType": "", "hasMimeType": false }, { "name": "Timeline", "content": "#### This is a future data prediction competition with an active training phase and a second period where selected submissions will be evaluated against future ground truth data.\n\n### Training Timeline\n* **April 18, 2023** - Start Date.\n\n* **August 14, 2023** - Entry Deadline. You must accept the competition rules before this date in order to compete.\n\n* **August 14, 2023** - Team Merger Deadline. This is the last day participants may join or merge teams.\n\n* **August 21, 2023** - Final Submission Deadline.\n\nAll deadlines are at 11:59 PM UTC on the corresponding day unless otherwise noted. The competition organizers reserve the right to update the contest timeline if they deem it necessary.\n\n### Prediction Timeline:\nStarting after the final submission deadline there will be periodic updates to the leaderboard to reflect future data updates that will be evaluated against selected submissions. We anticipate 2-3 interim updates before the final evaluation.\n\n- December 21, 2023 - Competition End Date", "isPublished": false, "hasIsPublished": false, "postTitle": "", "hasPostTitle": false, "mimeType": "", "hasMimeType": false }, { "name": "data-description", "content": "## Background\n\nThe [Gene Ontology](http://geneontology.org/docs/ontology-documentation/) (GO) is a concept hierarchy that describes the biological function of genes and gene products at different levels of abstraction (Ashburner et al., 2000). It is a good model to describe the multi-faceted nature of protein function.\n\nGO is a *directed acyclic graph*. The nodes in this graph are functional descriptors (terms or classes) connected by relational ties between them (is_a, part_of, etc.). For example, terms 'protein binding activity' and 'binding activity' are related by an is_a relationship; however, the edge in the graph is often reversed to point from binding towards protein binding. This graph contains three subgraphs (subontologies): Molecular Function (MF), Biological Process (BP), and Cellular Component (CC), defined by their root nodes. Biologically, each subgraph represent a different aspect of the protein's function: what it does on a molecular level (MF), which biological processes it participates in (BP) and where in the cell it is located (CC). See the [Gene Ontology Overview](http://geneontology.org/docs/ontology-documentation/) for more details.\n\nThe protein's function is therefore represented by a subset of one or more of the subontologies. \nThese annotations are supported by *evidence codes*, which can be broadly divided into experimental (e.g., as documented in a paper published by a research team of biologists) and non-experimental. Non-experimental terms are usually inferred by computational means. We recommend you read more about the different types of [GO evidence codes](http://geneontology.org/docs/guide-go-evidence-codes/).\n\nWe will use experimentally determined term-protein assignments as class labels for each protein. That is, if a protein is labeled with a term, it means that this protein has this function validated by experimental evidence. By processing these annotated terms, we can generate a dataset of proteins and their ground truth labels for each term. The absence of a term annotation does not necessarily mean a protein does not have this function, only that this annotation does not exist (yet) in the GO. A protein may be annotated by one or more terms from the same subontology, and by terms from more than one subontology. \n\nAshburner M, *et al*. Gene ontology: tool for the unification of biology. The Gene Ontology Consortium. *Nat Genet* (2000) 25(1):25-29.\n\n## Training Set\nFor the *training set*, we include all proteins with annotated terms that have been validated by experimental or high-throughput evidence, traceable author statement (evidence code TAS), or inferred by curator (IC). More information about evidence codes can be found [here](http://geneontology.org/docs/guide-go-evidence-codes/). We use annotations from the UniProtKB release of 2022-11-17. The participants are not required to use these data and are also welcome to use any other data available to them.\n\n## Test Superset\nThe *test superset* is a set of protein sequences on which the participants are asked to predict GO terms. \n\n## Test Set\nThe *test set* is unknown at the beginning of the competition. It will contain protein sequences (and their functions) from the test superset that gained experimental annotations between the submission deadline and the time of evaluation. \n\n## File Descriptions\n*Gene Ontology*: The ontology data is in the file `go-basic.obo`. This structure is the 2023-01-01 release of the GO graph. This file is in OBO format, for which there exist many parsing libraries. For example, the [obonet](https://pypi.org/project/obonet/) package is available for Python. The nodes in this graph are indexed by the term name, for example the roots of the three onotlogies are:\n\n``` \nsubontology_roots = {'BPO':'GO:0008150',\n 'CCO':'GO:0005575',\n 'MFO':'GO:0003674'}\n```\n\n*Training sequences*: `train_sequences.fasta` contains the protein sequences for the training dataset. \nThis files are in [FASTA format](https://en.wikipedia.org/wiki/FASTA_format), a standard format for describing protein sequences. The proteins were all retrieved from the [UniProt data set](https://www.uniprot.org/) curated at the European Bioinformatics Institute.\nThe header contains the protein's UniProt accession ID and additional information about the protein. Most protein sequences were extracted from the Swiss-Prot database, but a subset of proteins that are not represented in Swiss-Prot were extracted from the TrEMBL database. In both cases, the sequences come from the 2022_05 release from 14-Dec-2022. More information can be found [here](https://www.uniprot.org/help/uniprotkb_sections). \n\nThe `train_sequences.fasta` file will indicate from which database the sequence originate. For example, `sp|P9WHI7|RECN_MYCT` in the FASTA header indicates the protein with UniProt ID `P9WHI7` and gene name `RECN_MYCT` was taken from Swiss-Prot (`sp`). Any sequences taken from TrEMBL will have `tr` in the header instead of `sp`. Swiss-Prot and TrEMBL are both parts of [UniProtKB](https://www.uniprot.org).\n\nThis file contains only sequences for proteins with annotations in the dataset (labeled proteins). To obtain the full set of protein sequences for unlabeled proteins, the Swiss-Prot and TrEMBL databases can be found [here](https://www.uniprot.org/help/downloads).\n\n*Labels*: `train_terms.tsv` contains the list of annotated terms (ground truth) for the proteins in `train_sequences.fasta`. The first column indicates the protein's UniProt accession ID, the second is the GO term ID, and the third indicates in which ontology the term appears.\n\n*Taxonomy*: `train_taxonomy.tsv` contains the list of proteins and the species to which they belong, represented by a \"taxonomic identifier\" (taxon ID) number. The first column is the protein UniProt accession ID and the second is the taxon ID. More information about taxonomies can he found [here](https://www.uniprot.org/help/taxonomic_identifier).\n\n*Information accretion*: `IA.txt` contains the information accretion (weights) for each GO term. These weights are used to compute weighted precision and recall, as described in the Evaluation section. The values of this file were computed using the following code [repo](https://github.com/claradepaolis/InformationAccretion).\n\n*Test sequences*: `testsuperset.fasta` contains protein sequences on which the participants are asked to submit predictions. The header for each sequence in `testsuperset.fasta` contains the protein's UniProt accession ID and the Taxon ID of the species this protein belongs to. Only a small subset of those sequences will accumulate functional annotations and will constitute the test set. The file `testsuperset-taxon-list.tsv` is a set of taxon IDs for the proteins in the test superset.\n\n## Files\n\n* **train_sequences.fasta** - amino acid sequences for proteins in training set\n* **train_terms.tsv** - the training set of proteins and corresponding annotated GO terms\n* **train_taxonomy.tsv** - taxon ID for proteins in training set\n* **go-basic.obo** - ontology graph structure\n* **testsuperset.fasta** - amino acid sequences for proteins on which the predictions should be made\n* **testsuperset-taxon-list.tsv** - taxon ID for proteins in test superset (Note: you may need to use `encoding=\"ISO-8859-1\"` to read this file in `pandas`)\n* **IA.txt** - Information Accretion for each term. This is used to weight precision and recall (see Evaluation)\n* **sample_submission.csv** - a sample submission file in the correct format", "isPublished": false, "hasIsPublished": false, "postTitle": "", "hasPostTitle": false, "mimeType": "", "hasMimeType": false }, { "name": "Prizes", "content": "* 1st Place - $ 15,000\n* 2nd Place - $ 10,000\n* 3rd Place - $ 8,000\n* 4th Place - $ 7,000\n* 5th Place - $ 5,000\n* 6th Place - $ 5,000", "isPublished": false, "hasIsPublished": false, "postTitle": "", "hasPostTitle": false, "mimeType": "", "hasMimeType": false }, { "name": "Resources", "content": "Although the organizers provide a training set of sequences with their Gene Ontology (GO) annotations, the participants can use many other resources to train their methods. We consider the following resources to be of good quality:\n\n1. UniProtKB, the main resource on protein function annotation. Contains sequences and many links to additional databases. When parsing functional annotations for proteins, participants should keep in mind that UniProtKB also stores predicted annotations; thus, evidence codes on which annotations are experimental and which ones were predicted should be considered. Note that the organizers extracted the entire training set from UniProt-GOA compendium of UniProtKB. [website](https://www.uniprot.org)\n2. Gene Ontology (GO), a database of functional ontologies. It also contains some protein-GO term mappings, that are generally also contained in the UniProtKB, specifically its UniProt-GOA compendium. [website](http://geneontology.org)\n3. Pfam, a database of sequence families [website](https://pfam.sanger.ac.uk)\n4. Protein Data Bank (PDB), a database of experimentally determined protein structures. Note that many proteins in PDB are only fragments, others are mutated, and some are engineered to be able to be studied using techniques such as X-ray crystallography or NMR spectroscopy. [website](https://www.wwpdb.org)\n5. I2D, a database of protein-protein interactions [website](http://ophid.utoronto.ca/ophidv2.204/)\n6. GEO, a database of gene expression data [website](https://www.ncbi.nlm.nih.gov/geo/)\n7. PRIDE, a database of proteins identified via mass-spectrometry experiments [website](https://www.ebi.ac.uk/pride/)\n\nMost of these resources also have publications, sometimes many, associated with the resource. They can be easily obtained from the websites above.\n", "isPublished": false, "hasIsPublished": false, "postTitle": "", "hasPostTitle": false, "mimeType": "", "hasMimeType": false }, { "name": "CAFA", "content": "The Critical Assessment of Functional Annotation (CAFA) is a challenge designed to provide a large-scale assessment of computational methods dedicated to predicting protein function, using a prospective evaluation mechanism. The CAFA organizers provide a large number of protein sequences (target set). The predictors then predict the function of these proteins by associating them with Gene Ontology (GO) terms or other functional annotations (varying challenge to challenge). Following the prediction deadline, the organizers wait for several months. During that time, some proteins whose functions were not supported by experimental evidence before the prediction deadline will gain such experimental support (e.g., a paper reporting on what the protein does is published during the wait time). Those proteins constitute the benchmark (test) set, against which the methods are tested.\n\nCAFA is a community-wide effort whose goal is to help understand the state of affairs in computational protein function prediction and drive the field forward. The challenge started in 2010 and is held every three years. See more at our [website](https://www.biofunctionprediction.org).\n\nIntroductions to CAFA and protein function prediction are available in the following papers:\n1. Radivojac P. A (not so) quick introduction to protein function prediction (2013). This text is intended primarily for computer scientists with little background in biology. [pdf](https://www.ccs.neu.edu/home/radivojac/papers/radivojac_cafa_2013.pdf)\n2. Friedberg I, Radivojac P. Community-wide evaluation of computational function prediction. *Methods in Molecular Biology* (2017) 1446:133-146. [pdf](https://www.ccs.neu.edu/home/radivojac/papers/friedberg_mmb_2017.pdf)\n\n\nMore thorough reading describing CAFA1-3 challenges and summarizing all results:\n1. Radivojac P, *et al*. A large-scale evaluation of computational protein function prediction. *Nat Methods* (2013) 10(3):221-227. [pdf](https://www.ccs.neu.edu/home/radivojac/papers/radivojac_natmethods_2013.pdf)\n2. Jiang Y, *et al*. An expanded evaluation of protein function prediction methods shows an improvement in accuracy. *Genome Biol*. (2016) 17(1):184. [pdf](https://www.ccs.neu.edu/home/radivojac/papers/jiang_genomebiol_2016.pdf)\n3. Zhou N, *et al*. The CAFA challenge reports improved protein function prediction and new functional annotations for hundreds of genes through experimental screens. *Genome Biol*. (2019) 20(1):244. [pdf](https://www.ccs.neu.edu/home/radivojac/papers/zhou_genomebiol_2019.pdf)\n\nThe knowledge about protein function is notoriously incomplete. Even when a paper reports on the function of a protein, and it is considered accurate, it still may not be comprehensive. That is, some other functions of this macromolecule may not have been investigated and even if they are, there is no guarantee that the researchers covered all possible environmental conditions (e.g., temperature), all possible binding partners, all possible pathogen invasions, etc. These have been reported to be open world annotations; that is, the absence of evidence for a function cannot be considered the evidence for absence of that function. It is therefore to be understood that the assessment of prediction accuracy is only an approximation of the true accuracy. \n\nThe effects of incomplete annotations on performance evaluation vary metric to metric and have been previously discussed and studied by \n\n1. Huttenhower C, *et al*. The impact of incomplete knowledge on evaluation: an experimental benchmark for protein function prediction. *Bioinformatics* (2009) 25(18):2404–2410. [pdf](https://www.ncbi.nlm.nih.gov/pmc/articles/PMC2735660/pdf/btp397.pdf)\n2. Dessimoz C, *et al*. CAFA and the open world of protein function predictions. *Trends Genet*. (2013) 29(11):609–610. [pdf](https://www.cell.com/action/showPdf?pii=S0168-9525%2813%2900166-2)\n3. Jiang Y, *et al*. The impact of incomplete knowledge on the evaluation of protein function prediction: a structured-output learning perspective. *Bioinformatics* (2014) 30(17):i609-i616. [pdf](https://www.ccs.neu.edu/home/radivojac/papers/jiang_bioinformatics_2014.pdf)\n\n", "isPublished": false, "hasIsPublished": false, "postTitle": "", "hasPostTitle": false, "mimeType": "", "hasMimeType": false }, { "name": "Code Requirements", "content": "![Kerneler](https://storage.googleapis.com/kaggle-media/competitions/general/Kerneler-white-desc2_transparent.png)\n### This is a Code Competition\n\nSubmissions to this competition must be made through Notebooks. In order for the \"Submit to Competition\" button to be active after a commit, the following conditions must be met:\n\n- CPU Notebook <= 9 hours run-time\n- GPU Notebook <= 2 hours run-time\n- No internet access enabled\n- External data, freely & publicly available, is allowed, including pre-trained models\n- Submission file must be named \"submission.tsv\"\n\nPlease see the [Code Competition FAQ](https://www.kaggle.com/docs/competitions#kernels-only-FAQ) for more information on how to submit.", "isPublished": false, "hasIsPublished": false, "postTitle": "", "hasPostTitle": false, "mimeType": "", "hasMimeType": false } ] }