Knowledge-based computational search for genes associated with the metabolic syndrome | Zendy

Tsutomu Matsunaga | Zendy; Masaaki Muramatsu | Zendy

AI Assistant Blog Pricing

Home ZAIA Blog

Open Access

Knowledge-based computational search for genes associated with the metabolic syndrome

Author(s) -

Tsutomu Matsunaga,

Masaaki Muramatsu

Publication year - 2005

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/bti484

Subject(s) - gene , computer science , computational biology , subspace topology , matching (statistics) , metabolic syndrome , set (abstract data type) , bioinformatics , artificial intelligence , biology , genetics , diabetes mellitus , medicine , pathology , programming language , endocrinology

A methodology to search for genes associated with multifactorial diseases by integrating the large amount of accumulated knowledge is seriously needed. A comprehensive understanding derived from a holistic view of gene relationship structures can be gained from our proposed analysis called the cross-subspace analysis (CSA). In this analysis, gene objects are generated by machine learning using their term occurrence patterns in MEDLINE abstracts and the degree of relationship between gene objects is quantified by matching these patterns.

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