Your browser doesn't support javascript.
loading
Enhancing statistical power in temporal biomarker discovery through representative shapelet mining.
Gumbsch, Thomas; Bock, Christian; Moor, Michael; Rieck, Bastian; Borgwardt, Karsten.
Afiliação
  • Gumbsch T; Department of Biosystems Science and Engineering, ETH Zurich, Basel 4058, Switzerland.
  • Bock C; SIB Swiss Institute of Bioinformatics, Lausanne 1015, Switzerland.
  • Moor M; Department of Biosystems Science and Engineering, ETH Zurich, Basel 4058, Switzerland.
  • Rieck B; SIB Swiss Institute of Bioinformatics, Lausanne 1015, Switzerland.
  • Borgwardt K; Department of Biosystems Science and Engineering, ETH Zurich, Basel 4058, Switzerland.
Bioinformatics ; 36(Suppl_2): i840-i848, 2020 12 30.
Article em En | MEDLINE | ID: mdl-33381811
MOTIVATION: 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. RESULTS: We find that the pre- or post-processing of shapelets does not sufficiently increase the power and practical utility. Consequently, we present a novel method for temporal biomarker discovery: Statistically Significant Submodular Subset Shapelet Mining (S5M) that retrieves short subsequences that are (i) occurring in the data, (ii) are statistically significantly associated with the phenotype and (iii) are of manageable quantity while maximizing structural diversity. Structural diversity is achieved by pruning non-representative shapelets via submodular optimization. This increases the statistical power and utility of S5M compared to state-of-the-art approaches on simulated and real-world datasets. For patients admitted to the intensive care unit (ICU) showing signs of severe organ failure, we find temporal patterns in the sequential organ failure assessment score that are associated with in-ICU mortality. AVAILABILITY AND IMPLEMENTATION: S5M is an option in the python package of S3M: github.com/BorgwardtLab/S3M.
Assuntos

Texto completo: 1 Eixos temáticos: Pesquisa_clinica Base de dados: MEDLINE Assunto principal: Pesquisa Biomédica Limite: Humans Idioma: En Ano de publicação: 2020 Tipo de documento: Article

Texto completo: 1 Eixos temáticos: Pesquisa_clinica Base de dados: MEDLINE Assunto principal: Pesquisa Biomédica Limite: Humans Idioma: En Ano de publicação: 2020 Tipo de documento: Article