January 27, 2021 Conference paper Open Access

Alexander Chervov; Andrei Zinovyev

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{
  "publisher": "Zenodo", 
  "DOI": "10.5281/zenodo.5782816", 
  "language": "eng", 
  "title": "Clinical trajectories estimated from bulk tumoral molecular proles using elastic principal trees", 
  "issued": {
    "date-parts": [
      [
        2021, 
        1, 
        27
      ]
    ]
  }, 
  "abstract": "<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>", 
  "author": [
    {
      "family": "Alexander Chervov"
    }, 
    {
      "family": "Andrei Zinovyev"
    }
  ], 
  "id": "5782816", 
  "type": "paper-conference", 
  "event": "International Joint Conference on Neural Networks-2021 (IJCNN2021)"
}