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      Are machine learning based methods suited to address complex biological problems? Lessons from CAGI-5 challenges.

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          Abstract

          In-silico approaches are routinely adopted to predict the effects of genetic variants and their relation to diseases. The Critical Assessment of Genome Interpretation (CAGI) has established a common framework for the assessment of available predictors of variant effects on specific problems and our group has been an active participant of CAGI since its first edition.

          In this paper, we summarize our experience and lessons learned from the last edition of the experiment (CAGI-5). In particular, we analyse prediction performances of our tools on five CAGI-5 selected challenges grouped into three different categories: prediction of variant effects on protein stability, prediction of variant pathogenicity and prediction of complex functional effects. For each challenge, we analyse in detail the performance of our tools, highlighting their potentialities and drawbacks. The aim is to better define the application boundaries of each tool.

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          Author and article information

          Journal
          9215429
          2408
          Hum Mutat
          Hum. Mutat.
          Human mutation
          1059-7794
          1098-1004
          15 May 2020
          18 June 2019
          September 2019
          01 September 2020
          : 40
          : 9
          : 1455-1462
          Affiliations
          []Biocomputing Group, Department of Pharmacy and Biotechnology, University of Bologna, Italy.
          [§ ]Institute of Biomembranes, Bioenergetics and Molecular Biotechnologies (IBIOM), Italian National Research Council (CNR), Bari, Italy.
          Author notes
          [] Corresponding author
          Article
          PMC7281835 PMC7281835 7281835 nihpa1568237
          10.1002/humu.23784
          7281835
          31066146
          a64568c8-5663-4938-87cf-3a9e230b94d3
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