January 27, 2021 Conference paper Open Access

Alexander Chervov; Andrei Zinovyev

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{
  "inLanguage": {
    "alternateName": "eng", 
    "@type": "Language", 
    "name": "English"
  }, 
  "description": "<p>Clinical trajectory is a clinically relevant sequence of ordered patient phenotypes representing consecutive states of a developing disease and leading to some final state. Extracting trajectories from large scale&nbsp;medical data is of great interest for dynamical phenotyping of various diseases but remains a challenge for&nbsp;machine learning methods, especially in the case of synchronic (with short follow up) observations. Here&nbsp;we describe an approach for trajectory-based analysis of cancer data using elastic principal trees and test&nbsp;it on a large collection of molecular tumoral profiles for breast cancer. We show that the disease progress&nbsp;quantified with pseudotime (the geodesic distance from the root) along a particular trajectory can serve&nbsp;as a significant prognostic factor, not redundant with gene expression-based predictors. We conclude that&nbsp;application of the elastic principal trees to transcriptomic data can be of interest for clinical applications.</p>", 
  "license": "https://creativecommons.org/licenses/by/4.0/legalcode", 
  "creator": [
    {
      "affiliation": "Institut Curie", 
      "@type": "Person", 
      "name": "Alexander Chervov"
    }, 
    {
      "affiliation": "Institut Curie", 
      "@type": "Person", 
      "name": "Andrei Zinovyev"
    }
  ], 
  "headline": "Clinical trajectories estimated from bulk tumoral molecular proles using elastic principal trees", 
  "image": "https://zenodo.org/static/img/logos/zenodo-gradient-round.svg", 
  "datePublished": "2021-01-27", 
  "url": "https://zenodo.org/record/5782816", 
  "@type": "ScholarlyArticle", 
  "keywords": [
    "clinical trajectories,", 
    "breast cancer", 
    "transcriptome", 
    "principal tree", 
    "survival analysis"
  ], 
  "@context": "https://schema.org/", 
  "identifier": "https://doi.org/10.5281/zenodo.5782816", 
  "@id": "https://doi.org/10.5281/zenodo.5782816", 
  "workFeatured": {
    "url": "https://www.ijcnn.org/", 
    "alternateName": "IJCNN2021", 
    "@type": "Event", 
    "name": "International Joint Conference on Neural Networks-2021"
  }, 
  "name": "Clinical trajectories estimated from bulk tumoral molecular proles using elastic principal trees"
}