Enhancing statistical power in temporal biomarker discovery through representative shapelet mining | Zendy

Thomas Gumbsch | Zendy; Christian Bock | Zendy; Michael Moor | Zendy; Bastian Rieck | Zendy; Karsten Borgwardt | Zendy

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Enhancing statistical power in temporal biomarker discovery through representative shapelet mining

Author(s) -

Thomas Gumbsch,

Christian Bock,

Michael Moor,

Bastian Rieck,

Karsten Borgwardt

Publication year - 2020

Publication title -

bioinformatics

Language(s) - English

Resource type - Journals

SCImago Journal Rank - 3.599

H-Index - 390

eISSN - 1367-4811

pISSN - 1367-4803

DOI - 10.1093/bioinformatics/btaa815

Subject(s) - computer science , biomarker discovery , data mining , statistical power , artificial intelligence , machine learning , pattern recognition (psychology) , mathematics , statistics , biology , biochemistry , gene , proteomics

Temporal biomarker discovery in longitudinal data is based on detecting reoccurring trajectories, the so-called shapelets. The search for shapelets requires considering all subsequences in the data. While the accompanying issue of multiple testing has been mitigated in previous work, the redundancy and overlap of the detected shapelets results in an a priori unbounded number of highly similar and structurally meaningless shapelets. As a consequence, current temporal biomarker discovery methods are impractical and underpowered.

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