Results 171 to 180 of about 7,142,455 (272)

Genome comparison using Gene Ontology (GO) with statistical testing [PDF]

, 2006
Zhaotao Cai, Xizeng Mao, Songgang Li, Liping Wei   +3 more
openalex   +1 more source

Genetics of ovulatory dysfunction and infertility: a scoping review and gene ontology analysis. [PDF]

Front Endocrinol (Lausanne)
DiPietro EE, Sarasua SM, Hopkins CS, Ganakammal SR, Boccuto L, Hurwitz J.   +5 more
europepmc   +1 more source

Microbial Gene Ontology informed deep neural network for microbe functionality discovery in human diseases. [PDF]

PLoS One, 2023
Liu Y, Zhang YZ, Imoto S.
europepmc   +1 more source

Validation and functional annotation of expression-based clusters based on gene ontology [PDF]

, 2006
Ralf Steuer, Peter Humburg, Joachim Selbig   +2 more
openalex   +1 more source

Gene Ontology Causal Activity Modeling (GO-CAM) moves beyond GO annotations to structured descriptions of biological functions and systems

Nature Genetics, 2019
P. Thomas, D. Hill, H. Mi, David Osumi-Sutherland, K. V. Van Auken, S. Carbon, J. Balhoff, Laurent-Philippe Albou, Benjamin M. Good, P. Gaudet, S. Lewis, C. Mungall   +11 more
semanticscholar   +1 more source

Genome-scale prediction of gene ontology from mass fingerprints reveals new metabolic gene functions. [PDF]

Life Sci Alliance
Vavricka CJ, Mochizuki M, Yuzawa S, Murata M, Yoshida T, Watanabe N, Nakatsui M, Ishii J, Hara KY, Alper HS, Hasunuma T, Kondo A, Araki M.   +12 more
europepmc   +1 more source

DGH-GO: dissecting the genetic heterogeneity of complex diseases using gene ontology. [PDF]

BMC Bioinformatics, 2023
Asif M, Martiniano HFMC, Lamurias A, Kausar S, Couto FM.   +4 more
europepmc   +1 more source

Gene Ontology Driven Feature Selection from Microarray Gene Expression Data [PDF]

, 2006
Jianlong Qi, Jian Tang
openalex   +1 more source

Structural Changes in Gene Ontology Reveal Modular and Complex Representations of Biological Function. [PDF]

Mol Biol Evol
Valverde S, Vidiella B, Martínez-Redondo GI, Duran-Nebreda S, Fernández R, Bombarely A, Rojas AM, Bentley RA.   +7 more
europepmc   +1 more source

SSLpheno: a self-supervised learning approach for gene-phenotype association prediction using protein-protein interactions and gene ontology data. [PDF]

Bioinformatics, 2023
Bi X, Liang W, Zhao Q, Wang J.
europepmc   +1 more source