January 27, 2021 Conference paper Open Access

Alexander Chervov; Andrei Zinovyev

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    "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>", 
    "language": "eng", 
    "title": "Clinical trajectories estimated from bulk tumoral molecular proles using elastic principal trees", 
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    "keywords": [
      "clinical trajectories,", 
      "breast cancer", 
      "transcriptome", 
      "principal tree", 
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    "publication_date": "2021-01-27", 
    "creators": [
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        "affiliation": "Institut Curie", 
        "name": "Alexander Chervov"
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        "affiliation": "Institut Curie", 
        "name": "Andrei Zinovyev"
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      "acronym": "IJCNN2021", 
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