scholarly journals Analysis of protein sequence and interaction data for candidate disease gene prediction

2006 ◽  
Vol 34 (19) ◽  
pp. e130-e130 ◽  
Author(s):  
Richard A. George ◽  
Jason Y. Liu ◽  
Lina L. Feng ◽  
Robert J. Bryson-Richardson ◽  
Diane Fatkin ◽  
...  
Keyword(s):  
Protein Sequence ◽  
Disease Gene ◽  
Gene Prediction ◽  
Interaction Data ◽  
Candidate Disease Gene ◽  
Disease Gene Prediction

scholarly journals Gentrepid V2.0: a web server for candidate disease gene prediction

2013 ◽  
Vol 14 (1) ◽  
pp. 249 ◽  
Author(s):  
Sara Ballouz ◽  
Jason Y Liu ◽  
Richard A George ◽  
Naresh Bains ◽  
Arthur Liu ◽  
...  
Keyword(s):  
Disease Gene ◽  
Gene Prediction ◽  
Web Server ◽  
Candidate Disease Gene ◽  
Disease Gene Prediction

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

scholarly journals Ensemble disease gene prediction by clinical sample-based networks

2020 ◽  
Vol 21 (S2) ◽  
Author(s):  
Ping Luo ◽  
Li-Ping Tian ◽  
Bolin Chen ◽  
Qianghua Xiao ◽  
Fang-Xiang Wu
Keyword(s):  
Disease Gene ◽  
Clinical Sample ◽  
Gene Prediction ◽  
Disease Gene Prediction

scholarly journals Recent advances in network-based methods for disease gene prediction

2020 ◽  
Author(s):  
Sezin Kircali Ata ◽  
Min Wu ◽  
Yuan Fang ◽  
Le Ou-Yang ◽  
Chee Keong Kwoh ◽  
...  
Keyword(s):  
Empirical Analysis ◽  
Genome Wide Association Study ◽  
Disease Gene ◽  
Gene Prediction ◽  
Representation Learning ◽  
Graph Representation ◽  
Molecular Networks ◽  
Learning Methods ◽  
Gene Association ◽  
Disease Gene Prediction

Abstract Disease–gene association through genome-wide association study (GWAS) is an arduous task for researchers. Investigating single nucleotide polymorphisms that correlate with specific diseases needs statistical analysis of associations. Considering the huge number of possible mutations, in addition to its high cost, another important drawback of GWAS analysis is the large number of false positives. Thus, researchers search for more evidence to cross-check their results through different sources. To provide the researchers with alternative and complementary low-cost disease–gene association evidence, computational approaches come into play. Since molecular networks are able to capture complex interplay among molecules in diseases, they become one of the most extensively used data for disease–gene association prediction. In this survey, we aim to provide a comprehensive and up-to-date review of network-based methods for disease gene prediction. We also conduct an empirical analysis on 14 state-of-the-art methods. To summarize, we first elucidate the task definition for disease gene prediction. Secondly, we categorize existing network-based efforts into network diffusion methods, traditional machine learning methods with handcrafted graph features and graph representation learning methods. Thirdly, an empirical analysis is conducted to evaluate the performance of the selected methods across seven diseases. We also provide distinguishing findings about the discussed methods based on our empirical analysis. Finally, we highlight potential research directions for future studies on disease gene prediction.

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