The Genomics of Sexual Ornaments, Gene Identification and Pleiotropy

2015 ◽  
pp. 19-34 ◽  
Author(s):  
Martin Johnsson
Keyword(s):  
Gene Identification ◽  
Sexual Ornaments

Prokaryotes gene identification based on nonlinear SVM

2009 ◽  
Vol 29 (10) ◽  
pp. 2748-2750
Author(s):  
Ji-hong ZHANG ◽  
Xiao-xia LI ◽  
Bo SUN
Keyword(s):  
Gene Identification

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.

scholarly journals Structure and expression of the human angiotensinogen gene. Identification of a unique and highly active promoter.

1990 ◽  
Vol 265 (13) ◽  
pp. 7576-7582 ◽  
Author(s):  
A Fukamizu ◽  
S Takahashi ◽  
M S Seo ◽  
M Tada ◽  
K Tanimoto ◽  
...  
Keyword(s):  
Gene Identification ◽  
Angiotensinogen Gene ◽  
Highly Active ◽  
Active Promoter
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