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Benchmarking network algorithms for contextualizing genes of interest.


ABSTRACT: Computational approaches have shown promise in contextualizing genes of interest with known molecular interactions. In this work, we evaluate seventeen previously published algorithms based on characteristics of their output and their performance in three tasks: cross validation, prediction of drug targets, and behavior with random input. Our work highlights strengths and weaknesses of each algorithm and results in a recommendation of algorithms best suited for performing different tasks.

SUBMITTER: Hill A 

PROVIDER: S-EPMC6944391 | biostudies-literature | 2019 Dec

REPOSITORIES: biostudies-literature

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Benchmarking network algorithms for contextualizing genes of interest.

Hill Abby A   Gleim Scott S   Kiefer Florian F   Sigoillot Frederic F   Loureiro Joseph J   Jenkins Jeremy J   Morris Melody K MK  

PLoS computational biology 20191220 12


Computational approaches have shown promise in contextualizing genes of interest with known molecular interactions. In this work, we evaluate seventeen previously published algorithms based on characteristics of their output and their performance in three tasks: cross validation, prediction of drug targets, and behavior with random input. Our work highlights strengths and weaknesses of each algorithm and results in a recommendation of algorithms best suited for performing different tasks. ...[more]

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