scholarly journals Network-based methods for human disease gene prediction

2011 ◽  
Vol 10 (5) ◽  
pp. 280-293 ◽  
Author(s):  
X. Wang ◽  
N. Gulbahce ◽  
H. Yu
Keyword(s):  
Human Disease ◽  
Disease Gene ◽  
Gene Prediction ◽  
Disease Gene Prediction ◽  
Human Disease Gene

A partially supervised classification approach to dominant and recessive human disease gene prediction

2007 ◽  
Vol 85 (3) ◽  
pp. 229-237 ◽  
Author(s):  
Borja Calvo ◽  
Núria López-Bigas ◽  
Simon J. Furney ◽  
Pedro Larrañaga ◽  
Jose A. Lozano
Keyword(s):  
Human Disease ◽  
Supervised Classification ◽  
Disease Gene ◽  
Gene Prediction ◽  
Classification Approach ◽  
Disease Gene Prediction ◽  
Human Disease Gene

scholarly journals Ofd1, a Human Disease Gene, Regulates the Length and Distal Structure of Centrioles

2010 ◽  
Vol 18 (3) ◽  
pp. 410-424 ◽  
Author(s):  
Veena Singla ◽  
Miriam Romaguera-Ros ◽  
Jose Manuel Garcia-Verdugo ◽  
Jeremy F. Reiter
Keyword(s):  
Human Disease ◽  
Disease Gene ◽  
Human Disease Gene

scholarly journals MOSES: A New Approach to Integrate Interactome Topology and Functional Features for Disease Gene Prediction

Genes ◽  
2021 ◽  
Vol 12 (11) ◽  
pp. 1713
Author(s):  
Manuela Petti ◽  
Lorenzo Farina ◽  
Federico Francone ◽  
Stefano Lucidi ◽  
Amalia Macali ◽  
...  
Keyword(s):  
Network Topology ◽  
Disease Gene ◽  
Gene Prediction ◽  
Knowledge Bases ◽  
Biological Knowledge ◽  
Disease Genes ◽  
Human Interactome ◽  
Disease Gene Prediction ◽  
Disease Associations ◽  
Functional Features

Disease gene prediction is to date one of the main computational challenges of precision medicine. It is still uncertain if disease genes have unique functional properties that distinguish them from other non-disease genes or, from a network perspective, if they are located randomly in the interactome or show specific patterns in the network topology. In this study, we propose a new method for disease gene prediction based on the use of biological knowledge-bases (gene-disease associations, genes functional annotations, etc.) and interactome network topology. The proposed algorithm called MOSES is based on the definition of two somewhat opposing sets of genes both disease-specific from different perspectives: warm seeds (i.e., disease genes obtained from databases) and cold seeds (genes far from the disease genes on the interactome and not involved in their biological functions). The application of MOSES to a set of 40 diseases showed that the suggested putative disease genes are significantly enriched in their reference disease. Reassuringly, known and predicted disease genes together, tend to form a connected network module on the human interactome, mitigating the scattered distribution of disease genes which is probably due to both the paucity of disease-gene associations and the incompleteness of the interactome.

scholarly journals Significance-based multi-scale method for network community detection and its application in disease-gene prediction

PLoS ONE ◽  
2020 ◽  
Vol 15 (3) ◽  
pp. e0227244
Author(s):  
Ke Hu ◽  
Ju Xiang ◽  
Yun-Xia Yu ◽  
Liang Tang ◽  
Qin Xiang ◽  
...  
Keyword(s):  
Community Detection ◽  
Disease Gene ◽  
Gene Prediction ◽  
Disease Gene Prediction ◽  
Multi Scale ◽  
Scale Method ◽  
Network Community

scholarly journals Disease gene prediction for molecularly uncharacterized diseases

2019 ◽  
Vol 15 (7) ◽  
pp. e1007078 ◽  
Author(s):  
Juan J. Cáceres ◽  
Alberto Paccanaro
Keyword(s):  
Disease Gene ◽  
Gene Prediction ◽  
Disease Gene Prediction
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