Detalhe da pesquisa
1.
A feasibility study of an unsupervised, pre-operative exercise program for adults with lung cancer.
Eur J Cancer Care (Engl)
; 29(4): e13254, 2020 Jul.
Artigo
em Inglês
| MEDLINE | ID: mdl-32469129
2.
Evolutionarily derived networks to inform disease pathways.
Genet Epidemiol
; 41(8): 866-875, 2017 12.
Artigo
em Inglês
| MEDLINE | ID: mdl-28944497
3.
Studying the Genetics of Complex Disease With Ancestry-Specific Human Phenotype Networks: The Case of Type 2 Diabetes in East Asian Populations.
Genet Epidemiol
; 40(4): 293-303, 2016 May.
Artigo
em Inglês
| MEDLINE | ID: mdl-27061195
4.
Packaging Biocomputing Software to Maximize Distribution and Reuse.
Pac Symp Biocomput
; 27: 412-416, 2022.
Artigo
em Inglês
| MEDLINE | ID: mdl-34890169
5.
Potential effectiveness of a surgeon-delivered exercise prescription and an activity tracker on pre-operative exercise adherence and aerobic capacity of lung cancer patients.
Surg Oncol
; 37: 101525, 2021 Jun.
Artigo
em Inglês
| MEDLINE | ID: mdl-33813267
6.
Packaging Biocomputing Software to Maximize Distribution and Reuse.
Pac Symp Biocomput
; 25: 739-742, 2020.
Artigo
em Inglês
| MEDLINE | ID: mdl-31797644
7.
Session Introduction - Pattern Recognition in Biomedical Data: Challenges in putting big data to work.
Pac Symp Biocomput
; 24: 1-7, 2019.
Artigo
em Inglês
| MEDLINE | ID: mdl-30864305
8.
Session Introduction: Challenges of Pattern Recognition in Biomedical Data.
Pac Symp Biocomput
; 23: 104-110, 2018.
Artigo
em Inglês
| MEDLINE | ID: mdl-29218873
9.
Infection dynamics on spatial small-world network models.
Phys Rev E
; 96(5-1): 052316, 2017 Nov.
Artigo
em Inglês
| MEDLINE | ID: mdl-29347688
10.
PATTERNS IN BIOMEDICAL DATA-HOW DO WE FIND THEM?
Pac Symp Biocomput
; 22: 177-183, 2017.
Artigo
em Inglês
| MEDLINE | ID: mdl-27896973
11.
AN INTEGRATED NETWORK APPROACH TO IDENTIFYING BIOLOGICAL PATHWAYS AND ENVIRONMENTAL EXPOSURE INTERACTIONS IN COMPLEX DISEASES.
Pac Symp Biocomput
; 21: 9-20, 2016.
Artigo
em Inglês
| MEDLINE | ID: mdl-26776169
12.
Genome-wide epistasis and pleiotropy characterized by the bipartite human phenotype network.
Methods Mol Biol
; 1253: 269-83, 2015.
Artigo
em Inglês
| MEDLINE | ID: mdl-25403537
13.
Editorial: Machine Learning in Genome-Wide Association Studies.
Front Genet
; 11: 593958, 2020.
Artigo
em Inglês
| MEDLINE | ID: mdl-33193740
14.
A bipartite network approach to inferring interactions between environmental exposures and human diseases.
Pac Symp Biocomput
; : 171-82, 2015.
Artigo
em Inglês
| MEDLINE | ID: mdl-25592579
15.
Genome-wide genetic interaction analysis of glaucoma using expert knowledge derived from human phenotype networks.
Pac Symp Biocomput
; : 207-18, 2015.
Artigo
em Inglês
| MEDLINE | ID: mdl-25592582
16.
Using the bipartite human phenotype network to reveal pleiotropy and epistasis beyond the gene.
Pac Symp Biocomput
; : 188-99, 2014.
Artigo
em Inglês
| MEDLINE | ID: mdl-24297546
17.
The multiscale backbone of the human phenotype network based on biological pathways.
BioData Min
; 7(1): 1, 2014 Jan 25.
Artigo
em Inglês
| MEDLINE | ID: mdl-24460644
18.
Additive functions in boolean models of gene regulatory network modules.
PLoS One
; 6(11): e25110, 2011.
Artigo
em Inglês
| MEDLINE | ID: mdl-22132067