NDLI: Determining Potential Yeast Longevity Genes via PPI Networks and Microarray Data Clustering Analysis

Content Provider	IEEE Xplore Digital Library
Author	Chen, B. Doolabh, R. Fusheng Tang
Copyright Year	2013
Description	Author affiliation: Dept. of Biol., Univ. of Arkansas at Little Rock, Little Rock, AR, USA (Fusheng Tang) \|\| Dept. of Comput. Sci., Univ. of Central Arkansas, Conway, AR, USA (Chen, B.; Doolabh, R.)
Abstract	Identification of genes involved in lifespan extension is a pre-requisite for studying aging and age-dependent diseases. So far, very few genes have been identified that relate to longevity. The process of analyzing each single gene one at a time can be a very long and expensive process. It is known that approximately 10% of 6000 yeast genes are lifespan related genes, however, less than 100 genes are identified as longevity genes. The interconnection of multiple genes and the time-dependent protein-protein interactions make researchers use systems biology as a first tool to predict genes potentially involved in aging. In this study, we combined analyses of protein-protein interaction data and micro array data to predict longevity genes. A dataset of all 6000 yeast genes was utilized and a protein-protein interaction ratio was used to narrow the dataset. Next, a hierarchical clustering algorithm was created to group the resulting data. From these clusters, conclusion of 6 highly possible longevity genes was drawn based on the amount of longevity genes in each cluster. Based on our latest information, one of our predicted genes is identified as a longevity gene. Wet lab experiments are applied to our predicted genes for supporting the findings.
Sponsorship	IEEE Syst., Man, Cybern. Soc.
Starting Page	370
Ending Page	373
File Size	497551
Page Count	4
File Format	PDF
ISBN	9780769551449
DOI	10.1109/ICMLA.2013.75
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2013-12-04
Publisher Place	USA
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Proteins Algorithm design and analysis Clustering algorithms Aging PPI Partitioning algorithms yeast longevity genes Clustering Bioinformatics
Content Type	Text
Resource Type	Article

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