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      "content": "## Overview\n\nThe data has been split into two groups:\n- training set (**train.csv**)\n- test set (**test.csv**)\n\nThe training set should be used to build your machine learning models. For the training set, we provide the label (also known as the “ground truth”) for each gene. Your model will be based on the sequence. You can use feature engineering to create new features.\n\nThe test set should be used to see how well your model performs on unseen data. For the test set, we do not provide the ground truth for each gene. It is your job to predict these labels. For each sequence in the test set, use the model you trained to predict whether or not it is a plant resistance gene. This set will be used for evaluation and team ranking.\n\nWe also include **sample_submission.csv**, a set of random predictions, as an example of what a submission file should look like.\n\n## Columns\n\n* `ID` - unique protein identifier\n* `Sequence` - protein sequence\n* `Label` - protein class; 1 for plant resistance gene, 0 otherwise",
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      "name": "Description",
      "content": "Welcome to the Bioinformatics Hackathon on Plant Resistance Genes! In this competition, you will have the opportunity to apply your machine learning skills in the field of bioinformatics to identify potential plant resistance genes in a given plant species. By participating, you will contribute to the advancement of agricultural research and help address the global challenge of protecting crops against diseases.\n\n## Problem statement\n\nPlant resistance genes play a crucial role in plant defense mechanisms against various pathogens. Identifying these genes is a complex task that requires advanced computational techniques. In this competition, your task is to develop a machine learning model that can accurately classify whether a gene belongs to the plant resistance gene category or not.\n\nYou will be provided with a dataset of plant protein sequences, along with corresponding labels indicating whether each gene is a resistance gene or not. Your goal is to build a model that can effectively learn from this data and accurately identify resistance genes in unseen samples.\n\n## Participation\n\nThis competition is open to university students of all backgrounds. It provides a platform for learning, collaboration and showcasing your skills to the wider community. Additionally, this competition offers a valuable opportunity to network with your peers and potentially collaborate on future research projects.\n\n## Get started\n\nTo get started, make sure to review the competition rules, data description and evaluation criteria provided on the competition page. We also encourage you to explore the available resources, such as tutorials, example code, and relevant research papers, to enhance your understanding of plant resistance genes and machine learning techniques in bioinformatics.\n\nHere is a handy link to Kaggle's [competition documentation](https://www.kaggle.com/docs/competitions), which includes, among other things, instructions on [submitting predictions](https://www.kaggle.com/docs/competitions#making-a-submission).\n\nWe wish you the best of luck in the Bioinformatics Hackathon! Join us in this journey to make a positive impact on agriculture and contribute to the advancement of scientific knowledge.\n\n## Winners\n- Team 1 (best model award)\n- Team 4 (creativity award)\n\n## Acknowledgements\nThis hackathon is organized by [NGO Bioinformatika](https://bioinform-org.github.io/) and is part of the project \"Bioinforming the youth\". The project has been funded by the [Western Balkans Fund](https://westernbalkansfund.org/) and European Union, under the Call for Regional Project Proposals, \"Support to the Promotion of Civil Society regional activities in the Western Balkans.",
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      "name": "Evaluation",
      "content": "The evaluation metric for this competition is [Mean F1-Score](https://en.wikipedia.org/wiki/F-score). The F1 score, commonly used in information retrieval, measures accuracy using the statistics precision \\( \\text{p} \\) and recall \\( \\text{r} \\).\n\nPrecision is the ratio of true positives \\(\\text{tp}\\) to all predicted positives \\(\\text{tp} + \\text{fp}\\). Recall is the ratio of true positives \\(\\text{tp}\\) to all actual positives \\(\\text{tp} + \\text{fn}\\). The F1 score is given by:\n\n\\[ \\text{F1} = 2\\frac{\\text{p} \\cdot \\text{r}}{\\text{p}+\\text{r}}\\ \\ \\mathrm{where}\\ \\ \\text{p} = \\frac{\\text{tp}}{\\text{tp}+\\text{fp}},\\ \\ \\text{r} = \\frac{\\text{tp}}{\\text{tp}+\\text{fn}} \\]\n\nThe F1 metric weights recall/precision equally and a good retrieval algorithm will maximize both precision and recall simultaneously. Thus, moderately good performance on both will be favored over extremely good performance on one and poor performance on the other.",
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      "name": "rules",
      "content": "## One account per participant\nYou cannot sign up to Kaggle from multiple accounts and therefore you cannot submit from multiple accounts.\n\n## No private sharing outside teams\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\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\nThe maximum team size is 6.\n\n## Submission Limits\nYou may submit a maximum of 10 entries per day.\n\nYou may select up to 5 final submissions for judging.\n\n##Competition Timeline\nStart Date: 17/06/2023 10:00 CET\n\nEnd Date: 18/06/2023 18:00 CET",
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