{
  "fetched_at": "2026-10-22T06:50:05Z",
  "source_url": "https://www.kaggle.com/competitions/bio322b/data",
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    {
      "name": "data-description",
      "content": "<p>Please download the training and test data <a href=\"https://www.kaggle.com/c/epfl-bio-322a/data\">here</a>.</p>\n<h2>File descriptions</h2>\n<ul>\n<li><strong>sampleSubmission.csv</strong> - a sample submission file in the correct format</li>\n</ul>",
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    {
      "name": "Description",
      "content": "<blockquote>In vision and hearing, the wavelength of light and frequency of sound are highly predictive of color and tone. In contrast, it is not currently possible to predict the smell of a molecule from its chemical structure. This stimulus-percept problem has been difficult to solve in olfaction because odors do not vary continuously in stimulus space, and the size and dimensionality of olfactory perceptual space is unknown. Some molecules with very similar chemical structures can be discriminated by humans, and molecules with very different structures sometimes produce nearly identical percepts. Computational efforts developed models to relate chemical structure to odor percept, but many relied on psychophysical data from a single 30-year-old study that used odorants with limited structural and perceptual diversity. (<a href=\"https://science.sciencemag.org/content/355/6327/820.full\">Predicting human olfactory perception from chemical features of odor molecules</a>)\n</blockquote>\n\n<p>In this challenge, you are given access to a database of responses from a subject who had sniffed a large number of molecules and been asked to rate the pleasantness of the smell. You are also given a comprehensive list of the physical and chemical features of the molecules smelled. Your task is to predict if a new molecule smells more sweet or more sour..</p>\n\n<p>The data set and the task is derived from the publication <a href=\"https://science.sciencemag.org/content/355/6327/820.full\">Predicting human olfactory perception from chemical features of odor molecules</a>.</p>",
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    {
      "name": "Evaluation",
      "content": "<p> Your submission is evaluated with the area under the receiver operating characteristic curve (AUC).</p>\n\n<h2>Submission Format</h2>\n<p>Submission files should contain two columns: \"Id\" and \"SWEETORSOUR\" that contain your predictions of the probability that molecule with the corresponding Id in the <a href=\"https://www.kaggle.com/c/epfl-bio-322a/data\">test_data.csv</a> file is perceived as more sweet than sour.</p>\n<p>The file should contain a header and have the following format:</p>\n<pre>\"Id\",\"SWEETORSOUR\"<br>1,0.1<br>2,0.99<br>3,0.31<br>...<br></pre>\n<p>You may want to use the R function `write.csv` to generate this file.</p>",
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    },
    {
      "name": "rules",
      "content": "See instructions on [moodle](https://moodle.epfl.ch/mod/resource/view.php?id=1117079).",
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      "hasIsPublished": false,
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