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Number 4

Cluster Algorithm using Distributed Processing for Human Protein Function Prediction

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  • Manpreet Singh and Gurvinder Singh and Karanjeet Singh Kahlon 2012. Cluster Algorithm using Distributed Processing for Human Protein Function Prediction. International Journal of Applied Information Systems. 4, 4 (October 2012), 24-28. DOI=http://dx.doi.org/10.5120/ijais450696 
  • @article{10.5120/ijais2017451568,
author = {Manpreet Singh and Gurvinder Singh and Karanjeet Singh Kahlon},
title = {Cluster Algorithm using Distributed Processing for Human Protein Function Prediction},
journal = {International Journal of Applied Information Systems},
issue_date = {October 2012},
volume = {4},
number = {},
month = {October},
year = {2012},
issn = {},
pages = {24-28},
numpages = {},
url = {/archives/volume4/number4/293-0696},
doi = { 10.5120/ijais12-450696},
publisher = { xA9 2010 by IJAIS Journal},
address = {}
}

  • %1 450696
%A Manpreet Singh
%A Gurvinder Singh
%A Karanjeet Singh Kahlon
%T Cluster Algorithm using Distributed Processing for Human Protein Function Prediction
%J International Journal of Applied Information Systems
%@ 
%V 4
%N 
%P 24-28
%D 2012
%I  xA9 2010 by IJAIS Journal

Abstract

For the pharmaceutical industry, the discovery of a new drug presents an enormous scientific challenge, and consists essentially in the identification of the target responsible for the disease. Once the therapeutic target is identified, scientists then find one or more leads that interact with the therapeutic target. Usually leads are searched by employing a long and costly process of trial and error. But if the protein class of the target would have been known it will become very easy to find the complementary lead for the responsible molecule. The 50 protein sequences related to 10 different molecular classes are obtained from Human Protein Reference Database (HPRD). Then the Sequence Derived Features (SDFs) for each of the available sequence are obtained using the different online tools. For the whole SDF database, the variation in the values obtained is analyzed and priorities are assigned accordingly. In the present work, priority based cluster algorithm is used for human protein function prediction. Then the distributed processing using four MATLAB workers is applied for different iterations in the algorithm. Two different methods for distributing the code are applied and the cpu times are computed for these methods.

References

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Keywords

Human Protein Function Prediction, Cluster Algorithm, Sequence Derived Features

Index Terms

Computer Science
Information Sciences