Predicting the Functions of Unknown Protein by Analyzing Known Protein Interaction: A surveyRohini Mugur, Smitha P. S and Pallavi M. S.
Department of Computer Science Amrita School of Arts and Sciences Amrita Vishwa Vidyapeetham Mysuru, Karnataka, India.
Corresponding Author E-mail: firstname.lastname@example.org
Abstract: The Protein complexes from PPIs are responsible for the important biological processes about the cell and learning the functionality under these biological process need uncovering and learning complexes and related interacting proteins. One way for studying and dealing with this PPI involves Markov Clustering (MCL) algorithm and has successfully produced result, due to its efficiency and accuracy. The Markov clustering produced result contains clusters which are noisy, these wont represent any complexes that are known or will contains additional noisy proteins which will impact on the correctness of correctly predicted complexes. And correctly predicted correctness of these clusters works well with matched and complexes that are known are quite less. Increasing in the clusters will eventually improve the correctness required to understand and organize of these complexes. The consistency of experimental proof varies largely techniques for assessing quality that have been prepared and used to find the most suitable subset of the interacting proteins. The physical interactions between the proteins are complimented by the, amplitude of data regarding the various types of functional associations among proteins, which includes interactions between the gene, shared evolutionary history and about co-expression. This technique involves the facts and figures from interactions between the proteins, microarray gene-expression profiles, protein complexes, and practical observations for proteins that are known. Clusters communicate not only to protein complex but they also interact with other set proteins by this, graph theoretic clustering method will drop the dynamic interaction by producing false positive rates.Keywords: Cluster MCL; Gene; Interaction; MCODE; Network; Protein; RRW Algorithms Back to TOC