Determining conserved metabolic biomarkers from a million database queries | Zendy

Michael E. Kurczy | Zendy; Julijana Ivanišević | Zendy; Caroline H. Johnson | Zendy; Winnie Uritboonthai | Zendy; Linh Hoang | Zendy; Mingliang Fang | Zendy; Matthew R. Hicks | Zendy; Anthony Aldebot | Zendy; Duane Rinehart | Zendy; Lisa Mellander | Zendy; Ralf Tautenhahn | Zendy; Gary J. Patti | Zendy; Mary E. Spilker | Zendy; H. Paul Benton | Zendy; Gary Siuzdak | Zendy

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Determining conserved metabolic biomarkers from a million database queries

Author(s) -

Michael E. Kurczy,

Julijana Ivanišević,

Caroline H. Johnson,

Winnie Uritboonthai,

Linh Hoang,

Mingliang Fang,

Matthew R. Hicks,

Anthony Aldebot,

Duane Rinehart,

Lisa Mellander,

Ralf Tautenhahn,

Gary J. Patti,

Mary E. Spilker,

H. Paul Benton,

Gary Siuzdak

Publication year - 2015

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/btv475

Subject(s) - computer science , database , computational biology , data mining , biology

Metabolite databases provide a unique window into metabolome research allowing the most commonly searched biomarkers to be catalogued. Omic scale metabolite profiling, or metabolomics, is finding increased utility in biomarker discovery largely driven by improvements in analytical technologies and the concurrent developments in bioinformatics. However, the successful translation of biomarkers into clinical or biologically relevant indicators is limited.

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