{
  "id": 612702,
  "title": "Fast Sequence Feature Extraction in R for CAFA6 (Adapted from CAFA5 Code)",
  "url": "/competitions/cafa-6-protein-function-prediction/discussion/612702",
  "author_name": "Ali Jalaali",
  "post_date": "2025-10-21T13:11:44.032000",
  "votes": 3,
  "comment_count": 0,
  "views": 0,
  "content": "<p>Hi fellow competitors,</p>\n<p>With the CAFA 6 competition underway, I wanted to share a foundational piece of work from the last competition, CAFA 5, that many might find useful, especially those working with R.</p>\n<p><strong>Two years ago, I published a popular R notebook focused purely on sequence feature extraction. It was designed to quickly transform the raw protein sequences into a tabular feature set ready for downstream machine learning models.</strong></p>\n<p><strong>What the Notebook Provides:</strong>\nThis notebook, CAFA5 Sequence Feature Extraction by R Packages, demonstrates how to generate highly useful features using native R packages:</p>\n<ol>\n<li><strong>Fast Sequence Processing with Biostrings:</strong> It efficiently loads and processes the protein sequences using the powerful Biostrings package from the Bioconductor project.</li>\n<li><strong>Physicochemical Features:</strong> It extracts common and effective features based on the physicochemical properties of the amino acids (e.g., charge, hydrophobicity, molecular weight). This gives your model a solid, explainable starting point.</li>\n<li><strong>Ready-to-Use Output:</strong> The final output is a clean CSV file containing the features, ready to be used as input for any classification model (be it in R, Python, or another tool).</li>\n</ol>\n<p>I encourage you to fork the notebook to see the methods in action and adapt the code for the CAFA6 dataset structure.</p>\n<h1><strong>🔗 <a href=\"https://www.kaggle.com/code/alijalali4ai/cafa5-sequence-feature-extraction-by-r-packages\" target=\"_blank\">Link to the Notebook</a></strong></h1>\n<p>Good luck to everyone in CAFA6! I look forward to seeing the innovative solutions this year.</p>",
  "messages": [
    {
      "id": 3304882,
      "postDate": "2025-10-21T13:11:44.033Z",
      "content": "<p>Hi fellow competitors,</p>\n<p>With the CAFA 6 competition underway, I wanted to share a foundational piece of work from the last competition, CAFA 5, that many might find useful, especially those working with R.</p>\n<p><strong>Two years ago, I published a popular R notebook focused purely on sequence feature extraction. It was designed to quickly transform the raw protein sequences into a tabular feature set ready for downstream machine learning models.</strong></p>\n<p><strong>What the Notebook Provides:</strong>\nThis notebook, CAFA5 Sequence Feature Extraction by R Packages, demonstrates how to generate highly useful features using native R packages:</p>\n<ol>\n<li><strong>Fast Sequence Processing with Biostrings:</strong> It efficiently loads and processes the protein sequences using the powerful Biostrings package from the Bioconductor project.</li>\n<li><strong>Physicochemical Features:</strong> It extracts common and effective features based on the physicochemical properties of the amino acids (e.g., charge, hydrophobicity, molecular weight). This gives your model a solid, explainable starting point.</li>\n<li><strong>Ready-to-Use Output:</strong> The final output is a clean CSV file containing the features, ready to be used as input for any classification model (be it in R, Python, or another tool).</li>\n</ol>\n<p>I encourage you to fork the notebook to see the methods in action and adapt the code for the CAFA6 dataset structure.</p>\n<h1><strong>🔗 <a href=\"https://www.kaggle.com/code/alijalali4ai/cafa5-sequence-feature-extraction-by-r-packages\" target=\"_blank\">Link to the Notebook</a></strong></h1>\n<p>Good luck to everyone in CAFA6! I look forward to seeing the innovative solutions this year.</p>",
      "rawMarkdown": "Hi fellow competitors,\n\nWith the CAFA 6 competition underway, I wanted to share a foundational piece of work from the last competition, CAFA 5, that many might find useful, especially those working with R.\n\n**Two years ago, I published a popular R notebook focused purely on sequence feature extraction. It was designed to quickly transform the raw protein sequences into a tabular feature set ready for downstream machine learning models.**\n\n**What the Notebook Provides:**\nThis notebook, CAFA5 Sequence Feature Extraction by R Packages, demonstrates how to generate highly useful features using native R packages:\n\n1. **Fast Sequence Processing with Biostrings:** It efficiently loads and processes the protein sequences using the powerful Biostrings package from the Bioconductor project.\n2. **Physicochemical Features:** It extracts common and effective features based on the physicochemical properties of the amino acids (e.g., charge, hydrophobicity, molecular weight). This gives your model a solid, explainable starting point.\n3. **Ready-to-Use Output:** The final output is a clean CSV file containing the features, ready to be used as input for any classification model (be it in R, Python, or another tool).\n\nI encourage you to fork the notebook to see the methods in action and adapt the code for the CAFA6 dataset structure.\n\n# **🔗 [Link to the Notebook](https://www.kaggle.com/code/alijalali4ai/cafa5-sequence-feature-extraction-by-r-packages)**\n\nGood luck to everyone in CAFA6! I look forward to seeing the innovative solutions this year.",
      "votes": 3
    }
  ],
  "comments": [],
  "raw_markdown_by_id": {
    "3304882": "Hi fellow competitors,\n\nWith the CAFA 6 competition underway, I wanted to share a foundational piece of work from the last competition, CAFA 5, that many might find useful, especially those working with R.\n\n**Two years ago, I published a popular R notebook focused purely on sequence feature extraction. It was designed to quickly transform the raw protein sequences into a tabular feature set ready for downstream machine learning models.**\n\n**What the Notebook Provides:**\nThis notebook, CAFA5 Sequence Feature Extraction by R Packages, demonstrates how to generate highly useful features using native R packages:\n\n1. **Fast Sequence Processing with Biostrings:** It efficiently loads and processes the protein sequences using the powerful Biostrings package from the Bioconductor project.\n2. **Physicochemical Features:** It extracts common and effective features based on the physicochemical properties of the amino acids (e.g., charge, hydrophobicity, molecular weight). This gives your model a solid, explainable starting point.\n3. **Ready-to-Use Output:** The final output is a clean CSV file containing the features, ready to be used as input for any classification model (be it in R, Python, or another tool).\n\nI encourage you to fork the notebook to see the methods in action and adapt the code for the CAFA6 dataset structure.\n\n# **🔗 [Link to the Notebook](https://www.kaggle.com/code/alijalali4ai/cafa5-sequence-feature-extraction-by-r-packages)**\n\nGood luck to everyone in CAFA6! I look forward to seeing the innovative solutions this year."
  }
}