Acceleratingin silicosaturation mutagenesis using compressed sensing | Zendy

Jacob Schreiber | Zendy; Surag Nair | Zendy; Akshay Balsubramani | Zendy; Anshul Kundaje | Zendy

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Acceleratingin silicosaturation mutagenesis using compressed sensing

Author(s) -

Jacob Schreiber,

Surag Nair,

Akshay Balsubramani,

Anshul Kundaje

Publication year - 2022

Publication title -

bioinformatics

Language(s) - Uncategorized

Resource type - Journals

SCImago Journal Rank - 3.599

H-Index - 390

eISSN - 1367-4811

pISSN - 1367-4803

DOI - 10.1093/bioinformatics/btac385

Subject(s) - computer science , sequence (biology) , convolution (computer science) , in silico , algorithm , position (finance) , saturated mutagenesis , dimension (graph theory) , artificial intelligence , mathematics , biology , genetics , artificial neural network , gene , finance , pure mathematics , mutant , economics

In silico saturation mutagenesis (ISM) is a popular approach in computational genomics for calculating feature attributions on biological sequences that proceeds by systematically perturbing each position in a sequence and recording the difference in model output. However, this method can be slow because systematically perturbing each position requires performing a number of forward passes proportional to the length of the sequence being examined.

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