Fast numerical optimization for genome sequencing data in population biobanks | Zendy

Ruilin Li | Zendy; Christopher C. Chang | Zendy; Yosuke Tanigawa | Zendy; Balasubramanian Narasimhan | Zendy; Trevor Hastie | Zendy; Robert Tibshirani | Zendy; Manuel A. Rivas | Zendy

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Fast numerical optimization for genome sequencing data in population biobanks

Author(s) -

Ruilin Li,

Christopher C. Chang,

Yosuke Tanigawa,

Balasubramanian Narasimhan,

Trevor Hastie,

Robert Tibshirani,

Manuel A. Rivas

Publication year - 2021

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/btab452

Subject(s) - computer science , solver , lasso (programming language) , sparse approximation , genetic algorithm , sparse matrix , algorithm , representation (politics) , population , biobank , block (permutation group theory) , machine learning , mathematics , bioinformatics , law , gaussian , programming language , biology , physics , demography , geometry , quantum mechanics , sociology , politics , world wide web , political science

Large-scale and high-dimensional genome sequencing data poses computational challenges. General-purpose optimization tools are usually not optimal in terms of computational and memory performance for genetic data.

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