Taxonomic weighting improves the accuracy of a gap-filling algorithm for metabolic models | Zendy

Wai Kit Ong | Zendy; Peter Midford | Zendy; Peter D. Karp | Zendy

AI Assistant Blog Pricing

Home ZAIA Blog

Open Access

Taxonomic weighting improves the accuracy of a gap-filling algorithm for metabolic models

Author(s) -

Wai Kit Ong,

Peter Midford,

Peter D. Karp

Publication year - 2019

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/btz813

Subject(s) - weighting , usable , algorithm , computer science , phylum , taxonomic rank , software , data mining , biology , gene , genetics , ecology , physics , world wide web , taxon , programming language , acoustics

The increasing availability of annotated genome sequences enables construction of genome-scale metabolic networks, which are useful tools for studying organisms of interest. However, due to incomplete genome annotations, draft metabolic models contain gaps that must be filled in a time-consuming process before they are usable. Optimization-based algorithms that fill these gaps have been developed, however, gap-filling algorithms show significant error rates and often introduce incorrect reactions.

The content you want is available to Zendy users.

Already have an account? Click here to sign in.

Having issues? You can contact us here

Accelerating Research