scholarly journals Prediction of osteoporosis candidate genes by computational disease-gene identification strategy

2008 ◽  
Vol 53 (7) ◽  
pp. 644-655 ◽  
Author(s):  
Qing-Yang Huang ◽  
Gloria H. Y. Li ◽  
William M. W. Cheung ◽  
You-Qiang Song ◽  
Annie W. C. Kung
Keyword(s):  
Candidate Genes ◽  
Disease Gene ◽  
Gene Identification ◽  
Identification Strategy

scholarly journals Exploring Candidate Genes for Epilepsy by Computational Disease-Gene Identification Strategy

2010 ◽  
Vol 13 (2) ◽  
pp. 35-40 ◽  
Author(s):  
Y Sha ◽  
Q Liu ◽  
Y Wang ◽  
C Dong ◽  
L Song
Keyword(s):  
Candidate Genes ◽  
Complex Disease ◽  
Disease Gene ◽  
Experimental Tests ◽  
Susceptibility Genes ◽  
Gene Identification ◽  
Receptor Signaling ◽  
Bioinformatic Tools ◽  
Network Analyses ◽  
Identification Strategy

Exploring Candidate Genes for Epilepsy by Computational Disease-Gene Identification StrategyEpilepsy is a complex disease with a strong genetic component. So far, studies have focused on experimental validation or genome-wide linkage scans for epilepsy susceptibility genes in multiple populations. We have used four bioinformatic tools (SNPs3D, PROSPECTR and SUSPECTS, GenWanderer, PosMed) to analyze 16 susceptibility loci selected from a literature search. Pathways and regulatory network analyses were performed using the Ingenuity Pathways Analysis (IPA) software. We identified a subset of 48 candidate epilepsy susceptibility genes. Five significant canonical pathways, in four typical networks, were identified: GABA receptor signaling, interleukin-6 (IL-6) signaling, G-protein coupled receptor signaling, type 2 diabetes mellitus signaling and airway inflammation in asthma. We concluded that online analytical tools provide a powerful way to reveal candidate genes which can greatly reduce experimental time. Our study contributes to further experimental tests for epilepsy susceptibility genes.

Dementia key gene identification with multi-layered SNP-gene-disease network

2020 ◽  
Vol 36 (Supplement_2) ◽  
pp. i831-i839
Author(s):  
Dong-gi Lee ◽  
Myungjun Kim ◽  
Sang Joon Son ◽  
Chang Hyung Hong ◽  
Hyunjung Shin
Keyword(s):  
Candidate Genes ◽  
Learning Algorithm ◽  
Search Space ◽  
Supplementary Information ◽  
Gene Identification ◽  
Nucleotide Polymorphisms ◽  
Disease Network ◽  
Single Nucleotide ◽  
Key Genes ◽  
Significant Attention

Abstract Motivation Recently, various approaches for diagnosing and treating dementia have received significant attention, especially in identifying key genes that are crucial for dementia. If the mutations of such key genes could be tracked, it would be possible to predict the time of onset of dementia and significantly aid in developing drugs to treat dementia. However, gene finding involves tremendous cost, time and effort. To alleviate these problems, research on utilizing computational biology to decrease the search space of candidate genes is actively conducted. In this study, we propose a framework in which diseases, genes and single-nucleotide polymorphisms are represented by a layered network, and key genes are predicted by a machine learning algorithm. The algorithm utilizes a network-based semi-supervised learning model that can be applied to layered data structures. Results The proposed method was applied to a dataset extracted from public databases related to diseases and genes with data collected from 186 patients. A portion of key genes obtained using the proposed method was verified in silico through PubMed literature, and the remaining genes were left as possible candidate genes. Availability and implementation The code for the framework will be available at http://www.alphaminers.net/. Supplementary information Supplementary data are available at Bioinformatics online.

Human Gene Mapping and Disease Gene Identification

2007 ◽  
pp. 207-230
Author(s):  
Robert L. Nussbaum ◽  
Roderick R. McInnes ◽  
Huntington F. Willard ◽  
Ada Hamosh
Keyword(s):  
Gene Mapping ◽  
Disease Gene ◽  
Human Gene ◽  
Gene Identification ◽  
Human Gene Mapping

Computational tools for prioritizing candidate genes: boosting disease gene discovery

2012 ◽  
Vol 13 (8) ◽  
pp. 523-536 ◽  
Author(s):  
Yves Moreau ◽  
Léon-Charles Tranchevent
Keyword(s):  
Candidate Genes ◽  
Disease Gene ◽  
Gene Discovery ◽  
Computational Tools ◽  
Disease Gene Discovery
Sign in / Sign up

Export Citation Format

Share Document