Getting Started with Biopython - By Miles O'Neill
\nhttps://www.kaggle.com/code/mylesoneill/getting-started-with-biopython
Gene Ontology Python Tutorial - By Alexander Chervov
\nhttps://www.kaggle.com/code/alexandervc/gene-ontology-python-tutorial
Biopython | Bioinformatics Basics - By Andrey Shtrauss
\nhttps://www.kaggle.com/code/shtrausslearning/biopython-bioinformatics-basics
Bioconductor | Bioinformatics Basics - By Andrey Shtrauss - R code
\nhttps://www.kaggle.com/code/shtrausslearning/bioconductor-bioinformatics-basics
Starter Notebook for END ALS (Kaggle Challenge) - By Paul T. Mooney
\nhttps://www.kaggle.com/code/paultimothymooney/starter-notebook-for-end-als-kaggle-challenge
ALS Challenge Task 1 - By Randy Williams
\nhttps://www.kaggle.com/code/rwilliams7653/als-challenge-task-1/notebook
Genes information - Dataset by Alexander Chervov
\nhttps://www.kaggle.com/datasets/alexandervc/genes-information
Sequence GenBank - Dataset by Andrey Shtrauss
\nhttps://www.kaggle.com/datasets/shtrausslearning/biopython-genbank
End ALS Kaggle Challenge
\nhttps://www.kaggle.com/datasets/alsgroup/end-als
Open Problems - Multimodal Single-Cell Integration
\nhttps://www.kaggle.com/competitions/open-problems-multimodal
Novozymes Enzyme Stability Prediction
\nhttps://www.kaggle.com/competitions/novozymes-enzyme-stability-prediction/overview
Though we don't have communities, the topic below include many Bioinformatics sources:
\nPlease create \"Bioinformatics\" \"community\" By Alexander Chervov
\nhttps://www.kaggle.com/general/203136
Getting Started with Biopython - By Miles O'Neill
\nhttps://www.kaggle.com/code/mylesoneill/getting-started-with-biopython
Gene Ontology Python Tutorial - By Alexander Chervov
\nhttps://www.kaggle.com/code/alexandervc/gene-ontology-python-tutorial
Biopython | Bioinformatics Basics - By Andrey Shtrauss
\nhttps://www.kaggle.com/code/shtrausslearning/biopython-bioinformatics-basics
Bioconductor | Bioinformatics Basics - By Andrey Shtrauss - R code
\nhttps://www.kaggle.com/code/shtrausslearning/bioconductor-bioinformatics-basics
Starter Notebook for END ALS (Kaggle Challenge) - By Paul T. Mooney
\nhttps://www.kaggle.com/code/paultimothymooney/starter-notebook-for-end-als-kaggle-challenge
ALS Challenge Task 1 - By Randy Williams
\nhttps://www.kaggle.com/code/rwilliams7653/als-challenge-task-1/notebook
Genes information - Dataset by Alexander Chervov
\nhttps://www.kaggle.com/datasets/alexandervc/genes-information
Sequence GenBank - Dataset by Andrey Shtrauss
\nhttps://www.kaggle.com/datasets/shtrausslearning/biopython-genbank
End ALS Kaggle Challenge
\nhttps://www.kaggle.com/datasets/alsgroup/end-als
Open Problems - Multimodal Single-Cell Integration
\nhttps://www.kaggle.com/competitions/open-problems-multimodal
Novozymes Enzyme Stability Prediction
\nhttps://www.kaggle.com/competitions/novozymes-enzyme-stability-prediction/overview
Though we don't have communities, the topic below include many Bioinformatics sources:
\nPlease create \"Bioinformatics\" \"community\" By Alexander Chervov
\nhttps://www.kaggle.com/general/203136
@mpwolke Very interesting list of helpful materials about the bioinformatic's theme.
", "rawMarkdown": "mpwolke Very interesting list of helpful materials about the bioinformatic's theme.", "votes": null }, { "id": "2226788", "postDate": "04/19/2023 08:05:18", "content": "Thanks for kind words !
\nIt is a regular challenge in bioinformatics and huge literature exists:
\n\nChatGPT:
\nQuestion: best approaches to solve \"CAFA challenge - Critical Assessment of Functional Annotation \"
\nAnswer:
\nThe CAFA (Critical Assessment of Function Annotation) challenge is a community-wide effort to evaluate and advance the state-of-the-art in protein function prediction. Here are some of the best approaches that have been used to solve the CAFA challenge:
Machine learning-based methods: These methods use various machine learning algorithms such as Support Vector Machines (SVMs), Random Forests, and Deep Neural Networks (DNNs) to predict protein functions. These algorithms learn from previously annotated data and make predictions based on similarities between the target protein and annotated proteins.
\nNetwork-based methods: These methods use protein-protein interaction networks to predict protein functions. They take advantage of the fact that functionally related proteins tend to interact with each other more frequently than with other proteins.
\nHomology-based methods: These methods predict the function of a protein based on its sequence similarity to proteins with known functions. They assume that proteins with similar sequences tend to have similar functions.
\nHybrid methods: These methods combine multiple approaches, such as machine learning and network-based methods, to improve the accuracy of function predictions.
\nEnsemble methods: These methods combine the results from multiple methods to produce a consensus prediction. This approach has been shown to improve the accuracy of function predictions.
\nIt's worth noting that the best approach for a specific problem in the CAFA challenge can depend on the specific features of the data being analyzed. Therefore, it's common to use a combination of multiple methods and to continuously refine them based on the results obtained in the challenge.
", "rawMarkdown": "Thanks for kind words ! \n\nIt is a regular challenge in bioinformatics and huge literature exists: \n\nhttps://scholar.google.fr/scholar?start=0&q=cafa+protein+function+prediction&hl=en&as_sdt=0,5&as_vis=1 \n\nChatGPT: \n\nQuestion: best approaches to solve \"CAFA challenge - Critical Assessment of Functional Annotation \"\n\nAnswer: \nThe CAFA (Critical Assessment of Function Annotation) challenge is a community-wide effort to evaluate and advance the state-of-the-art in protein function prediction. Here are some of the best approaches that have been used to solve the CAFA challenge:\n\nMachine learning-based methods: These methods use various machine learning algorithms such as Support Vector Machines (SVMs), Random Forests, and Deep Neural Networks (DNNs) to predict protein functions. These algorithms learn from previously annotated data and make predictions based on similarities between the target protein and annotated proteins.\n\nNetwork-based methods: These methods use protein-protein interaction networks to predict protein functions. They take advantage of the fact that functionally related proteins tend to interact with each other more frequently than with other proteins.\n\nHomology-based methods: These methods predict the function of a protein based on its sequence similarity to proteins with known functions. They assume that proteins with similar sequences tend to have similar functions.\n\nHybrid methods: These methods combine multiple approaches, such as machine learning and network-based methods, to improve the accuracy of function predictions.\n\nEnsemble methods: These methods combine the results from multiple methods to produce a consensus prediction. This approach has been shown to improve the accuracy of function predictions.\n\nIt's worth noting that the best approach for a specific problem in the CAFA challenge can depend on the specific features of the data being analyzed. Therefore, it's common to use a combination of multiple methods and to continuously refine them based on the results obtained in the challenge.", "votes": null }, { "id": "2226996", "postDate": "04/19/2023 12:56:19", "content": "Hi Alexander,
\nI edited my comment since I read it too fast.
\nAfter analysing critically the last paragraph of ChatGPT (\"depend on the specific features\". \"Use a combination of multiple methods\". \"Continuously refine them based on the results obtained\")
\nEven with my poor knowledge, I thought ChatGPT didn't add anything concrete(on that last paragraph) for approaching the competition. The answer is abstract/vague and it could be applyed for answering any other challenge.
\nYesterday, when I checked ALS Challenge I realized that you are The Alexander(with the generic bird image) that had published so many remarkable Notebooks on that competition. And on Multimodal Single-Cell you had already changed your name and image.
\nGlad to know that since it's easier to recognize your brilliant, generous work.
I'll be waiting to read your contributions on this competition.
", "rawMarkdown": "Hi Alexander,\n\nI edited my comment since I read it too fast. \n\nAfter analysing critically the last paragraph of ChatGPT (\"depend on the specific features\". \"Use a combination of multiple methods\". \"Continuously refine them based on the results obtained\")\n\nEven with my poor knowledge, I thought ChatGPT didn't add anything concrete(on that last paragraph) for approaching the competition. The answer is abstract/vague and it could be applyed for answering any other challenge.\n\nYesterday, when I checked ALS Challenge I realized that you are The Alexander(with the generic bird image) that had published so many remarkable Notebooks on that competition. And on Multimodal Single-Cell you had already changed your name and image. \nGlad to know that since it's easier to recognize your brilliant, generous work.\n\nI'll be waiting to read your contributions on this competition.", "votes": null }, { "id": "2227005", "postDate": "04/19/2023 13:05:01", "content": "Indeed, that material is valuable for such an important subject as Bioinformatics.
", "rawMarkdown": "Indeed, that material is valuable for such an important subject as Bioinformatics.", "votes": null }, { "id": "2227075", "postDate": "04/19/2023 13:50:41", "content": "Thanks for sharing Maria.
\nVery cool to see Brazilian from bioinfo here on Kaggle!
Nice to meet you!
", "rawMarkdown": "Thanks for sharing Maria.\nVery cool to see Brazilian from bioinfo here on Kaggle!\n\nNice to meet you!", "votes": null }, { "id": "2227144", "postDate": "04/19/2023 14:29:06", "content": "Oi Marcel,
\nobrigada pelo apoio. Estou apenas tentando aprender algo sobre programação no Kaggle.
\nBioinformatics eu sei menos ainda. Até para abrir os arquivos eu sofri, lendo dicas no StackOverflow.
Porém, é um assunto extremamente interessante.
", "rawMarkdown": "Oi Marcel, \nobrigada pelo apoio. Estou apenas tentando aprender algo sobre programação no Kaggle.\nBioinformatics eu sei menos ainda. Até para abrir os arquivos eu sofri, lendo dicas no StackOverflow.\n\nPorém, é um assunto extremamente interessante.", "votes": null }, { "id": "2227159", "postDate": "04/19/2023 14:43:31", "content": "Que legal!
\nEu trabalho com pesquisas utilizando bioinfo! É realmente um campo sensacional!
\nEstou a disposição para trocar informações se quiser!
\nAbraço!
", "rawMarkdown": "Que legal!\n\nEu trabalho com pesquisas utilizando bioinfo! É realmente um campo sensacional!\n\nEstou a disposição para trocar informações se quiser!\n\nAbraço!", "votes": null }, { "id": "2228129", "postDate": "04/20/2023 09:37:11", "content": "Thank you for the informative list @mpwolke :)
", "rawMarkdown": "Thank you for the informative list @mpwolke :)", "votes": null }, { "id": "2228264", "postDate": "04/20/2023 11:52:26", "content": "Thank you Waticson. For those that intend to learn Bioinformatics, it's a good start on Kaggle.
", "rawMarkdown": "Thank you Waticson. For those that intend to learn Bioinformatics, it's a good start on Kaggle.", "votes": null }, { "id": "2228938", "postDate": "04/21/2023 00:35:35", "content": "Hello, may I ask if this competition requires a basic understanding of biology? Without knowledge of biology, is it not suitable to participate in this competition?
", "rawMarkdown": "Hello, may I ask if this competition requires a basic understanding of biology? Without knowledge of biology, is it not suitable to participate in this competition?", "votes": null }, { "id": "2325222", "postDate": "07/01/2023 07:27:32", "content": "Thanks for sharing. This is exactly what I was looking for!!
", "rawMarkdown": "Thanks for sharing. This is exactly what I was looking for!!", "votes": null }, { "id": "2325570", "postDate": "07/01/2023 13:02:50", "content": "Thank you for the kind words and support (mugichaa). Nice account name CompDad : )
", "rawMarkdown": "Thank you for the kind words and support (mugichaa). Nice account name CompDad : )", "votes": null } ], "comments": [ { "id": 2226625, "author_name": "serangu", "author_url": "", "post_date": "04/19/2023 05:10:01", "content": "@mpwolke Very interesting list of helpful materials about the bioinformatic's theme.
", "votes": null, "replies": [ { "id": 2227005, "author_name": "mpwolke", "author_url": "", "post_date": "04/19/2023 13:05:01", "content": "Indeed, that material is valuable for such an important subject as Bioinformatics.
", "votes": null, "replies": [] } ] }, { "id": 2226788, "author_name": "alexandervc", "author_url": "", "post_date": "04/19/2023 08:05:18", "content": "Thanks for kind words !
\nIt is a regular challenge in bioinformatics and huge literature exists:
\n\nChatGPT:
\nQuestion: best approaches to solve \"CAFA challenge - Critical Assessment of Functional Annotation \"
\nAnswer:
\nThe CAFA (Critical Assessment of Function Annotation) challenge is a community-wide effort to evaluate and advance the state-of-the-art in protein function prediction. Here are some of the best approaches that have been used to solve the CAFA challenge:
Machine learning-based methods: These methods use various machine learning algorithms such as Support Vector Machines (SVMs), Random Forests, and Deep Neural Networks (DNNs) to predict protein functions. These algorithms learn from previously annotated data and make predictions based on similarities between the target protein and annotated proteins.
\nNetwork-based methods: These methods use protein-protein interaction networks to predict protein functions. They take advantage of the fact that functionally related proteins tend to interact with each other more frequently than with other proteins.
\nHomology-based methods: These methods predict the function of a protein based on its sequence similarity to proteins with known functions. They assume that proteins with similar sequences tend to have similar functions.
\nHybrid methods: These methods combine multiple approaches, such as machine learning and network-based methods, to improve the accuracy of function predictions.
\nEnsemble methods: These methods combine the results from multiple methods to produce a consensus prediction. This approach has been shown to improve the accuracy of function predictions.
\nIt's worth noting that the best approach for a specific problem in the CAFA challenge can depend on the specific features of the data being analyzed. Therefore, it's common to use a combination of multiple methods and to continuously refine them based on the results obtained in the challenge.
", "votes": null, "replies": [ { "id": 2226996, "author_name": "mpwolke", "author_url": "", "post_date": "04/19/2023 12:56:19", "content": "Hi Alexander,
\nI edited my comment since I read it too fast.
\nAfter analysing critically the last paragraph of ChatGPT (\"depend on the specific features\". \"Use a combination of multiple methods\". \"Continuously refine them based on the results obtained\")
\nEven with my poor knowledge, I thought ChatGPT didn't add anything concrete(on that last paragraph) for approaching the competition. The answer is abstract/vague and it could be applyed for answering any other challenge.
\nYesterday, when I checked ALS Challenge I realized that you are The Alexander(with the generic bird image) that had published so many remarkable Notebooks on that competition. And on Multimodal Single-Cell you had already changed your name and image.
\nGlad to know that since it's easier to recognize your brilliant, generous work.
I'll be waiting to read your contributions on this competition.
", "votes": null, "replies": [] } ] }, { "id": 2227075, "author_name": "marceelrf", "author_url": "", "post_date": "04/19/2023 13:50:41", "content": "Thanks for sharing Maria.
\nVery cool to see Brazilian from bioinfo here on Kaggle!
Nice to meet you!
", "votes": null, "replies": [ { "id": 2227144, "author_name": "mpwolke", "author_url": "", "post_date": "04/19/2023 14:29:06", "content": "Oi Marcel,
\nobrigada pelo apoio. Estou apenas tentando aprender algo sobre programação no Kaggle.
\nBioinformatics eu sei menos ainda. Até para abrir os arquivos eu sofri, lendo dicas no StackOverflow.
Porém, é um assunto extremamente interessante.
", "votes": null, "replies": [ { "id": 2227159, "author_name": "marceelrf", "author_url": "", "post_date": "04/19/2023 14:43:31", "content": "Que legal!
\nEu trabalho com pesquisas utilizando bioinfo! É realmente um campo sensacional!
\nEstou a disposição para trocar informações se quiser!
\nAbraço!
", "votes": null, "replies": [] } ] } ] }, { "id": 2228129, "author_name": "yutodennou", "author_url": "", "post_date": "04/20/2023 09:37:11", "content": "Thank you for the informative list @mpwolke :)
", "votes": null, "replies": [ { "id": 2228264, "author_name": "mpwolke", "author_url": "", "post_date": "04/20/2023 11:52:26", "content": "Thank you Waticson. For those that intend to learn Bioinformatics, it's a good start on Kaggle.
", "votes": null, "replies": [] } ] }, { "id": 2228938, "author_name": "yunsuxiaozi", "author_url": "", "post_date": "04/21/2023 00:35:35", "content": "Hello, may I ask if this competition requires a basic understanding of biology? Without knowledge of biology, is it not suitable to participate in this competition?
", "votes": null, "replies": [] }, { "id": 2325222, "author_name": "mugichaa", "author_url": "", "post_date": "07/01/2023 07:27:32", "content": "Thanks for sharing. This is exactly what I was looking for!!
", "votes": null, "replies": [ { "id": 2325570, "author_name": "mpwolke", "author_url": "", "post_date": "07/01/2023 13:02:50", "content": "Thank you for the kind words and support (mugichaa). Nice account name CompDad : )
", "votes": null, "replies": [] } ] } ], "raw_markdown_by_id": { "2226481": "#Bioinformatics on Kaggle\n\n#KAGGLE NOTEBOOKS:\n\nGetting Started with Biopython - By Miles O'Neill\nhttps://www.kaggle.com/code/mylesoneill/getting-started-with-biopython\n\nGene Ontology Python Tutorial - By Alexander Chervov \nhttps://www.kaggle.com/code/alexandervc/gene-ontology-python-tutorial\n\nBiopython | Bioinformatics Basics - By Andrey Shtrauss\nhttps://www.kaggle.com/code/shtrausslearning/biopython-bioinformatics-basics\n\nBioconductor | Bioinformatics Basics - By Andrey Shtrauss - R code\nhttps://www.kaggle.com/code/shtrausslearning/bioconductor-bioinformatics-basics\n\nStarter Notebook for END ALS (Kaggle Challenge) - By Paul T. Mooney\nhttps://www.kaggle.com/code/paultimothymooney/starter-notebook-for-end-als-kaggle-challenge\n\nALS Challenge Task 1 - By Randy Williams\nhttps://www.kaggle.com/code/rwilliams7653/als-challenge-task-1/notebook\n\n#KAGGLE DATASETS:\n\nGenes information - Dataset by Alexander Chervov\nhttps://www.kaggle.com/datasets/alexandervc/genes-information\n\nSequence GenBank - Dataset by Andrey Shtrauss\nhttps://www.kaggle.com/datasets/shtrausslearning/biopython-genbank\n\n#KAGGLE COMPETITIONS:\n\nEnd ALS Kaggle Challenge\nhttps://www.kaggle.com/datasets/alsgroup/end-als\n\nOpen Problems - Multimodal Single-Cell Integration\nhttps://www.kaggle.com/competitions/open-problems-multimodal\n\nNovozymes Enzyme Stability Prediction\nhttps://www.kaggle.com/competitions/novozymes-enzyme-stability-prediction/overview\n\n#Discussion Topic:\n\nThough we don't have communities, the topic below include many Bioinformatics sources:\n\nPlease create \"Bioinformatics\" \"community\" By Alexander Chervov\nhttps://www.kaggle.com/general/203136\n\n#Have fun with those fasta and obo files.", "2226625": "mpwolke Very interesting list of helpful materials about the bioinformatic's theme.", "2226788": "Thanks for kind words ! \n\nIt is a regular challenge in bioinformatics and huge literature exists: \n\nhttps://scholar.google.fr/scholar?start=0&q=cafa+protein+function+prediction&hl=en&as_sdt=0,5&as_vis=1 \n\nChatGPT: \n\nQuestion: best approaches to solve \"CAFA challenge - Critical Assessment of Functional Annotation \"\n\nAnswer: \nThe CAFA (Critical Assessment of Function Annotation) challenge is a community-wide effort to evaluate and advance the state-of-the-art in protein function prediction. Here are some of the best approaches that have been used to solve the CAFA challenge:\n\nMachine learning-based methods: These methods use various machine learning algorithms such as Support Vector Machines (SVMs), Random Forests, and Deep Neural Networks (DNNs) to predict protein functions. These algorithms learn from previously annotated data and make predictions based on similarities between the target protein and annotated proteins.\n\nNetwork-based methods: These methods use protein-protein interaction networks to predict protein functions. They take advantage of the fact that functionally related proteins tend to interact with each other more frequently than with other proteins.\n\nHomology-based methods: These methods predict the function of a protein based on its sequence similarity to proteins with known functions. They assume that proteins with similar sequences tend to have similar functions.\n\nHybrid methods: These methods combine multiple approaches, such as machine learning and network-based methods, to improve the accuracy of function predictions.\n\nEnsemble methods: These methods combine the results from multiple methods to produce a consensus prediction. This approach has been shown to improve the accuracy of function predictions.\n\nIt's worth noting that the best approach for a specific problem in the CAFA challenge can depend on the specific features of the data being analyzed. Therefore, it's common to use a combination of multiple methods and to continuously refine them based on the results obtained in the challenge.", "2226996": "Hi Alexander,\n\nI edited my comment since I read it too fast. \n\nAfter analysing critically the last paragraph of ChatGPT (\"depend on the specific features\". \"Use a combination of multiple methods\". \"Continuously refine them based on the results obtained\")\n\nEven with my poor knowledge, I thought ChatGPT didn't add anything concrete(on that last paragraph) for approaching the competition. The answer is abstract/vague and it could be applyed for answering any other challenge.\n\nYesterday, when I checked ALS Challenge I realized that you are The Alexander(with the generic bird image) that had published so many remarkable Notebooks on that competition. And on Multimodal Single-Cell you had already changed your name and image. \nGlad to know that since it's easier to recognize your brilliant, generous work.\n\nI'll be waiting to read your contributions on this competition.", "2227005": "Indeed, that material is valuable for such an important subject as Bioinformatics.", "2227075": "Thanks for sharing Maria.\nVery cool to see Brazilian from bioinfo here on Kaggle!\n\nNice to meet you!", "2227144": "Oi Marcel, \nobrigada pelo apoio. Estou apenas tentando aprender algo sobre programação no Kaggle.\nBioinformatics eu sei menos ainda. Até para abrir os arquivos eu sofri, lendo dicas no StackOverflow.\n\nPorém, é um assunto extremamente interessante.", "2227159": "Que legal!\n\nEu trabalho com pesquisas utilizando bioinfo! É realmente um campo sensacional!\n\nEstou a disposição para trocar informações se quiser!\n\nAbraço!", "2228129": "Thank you for the informative list @mpwolke :)", "2228264": "Thank you Waticson. For those that intend to learn Bioinformatics, it's a good start on Kaggle.", "2228938": "Hello, may I ask if this competition requires a basic understanding of biology? Without knowledge of biology, is it not suitable to participate in this competition?", "2325222": "Thanks for sharing. This is exactly what I was looking for!!", "2325570": "Thank you for the kind words and support (mugichaa). Nice account name CompDad : )" }, "source": "meta" }