Enhancing Efficiency of Protein Functional Prediction Through Association Network Using Greedy Weighting Method

Title:Enhancing Efficiency of Protein Functional Prediction Through Association Network Using Greedy Weighting Method

Volume: 12 Issue: 3

Author(s): Atabak Kheirkhah*, Salwani Mohd Daud and Kamilia Kamardin

Affiliation:

• Advanced Informatics School, Universiti Teknologi Malaysia, Jalan Sultan Yahya Petra (Jalan Semarak), 54100 Kuala Lumpur,Malaysia

Keywords: Composite network, greedy, gene weighting, protein functional prediction, gene similarity, network redundancy.

Abstract: Background: In spite of the significant data surrounding complex gene networks including gene function, the occurrence of huge redundancy affects the efficiency.

Objective: This work proposes a mining method to reduce the number of redundant nodes in a composite weighted network.

Method: The idea is to eliminate the redundancies of nodes via a hybrid approach, i.e. the integration of multiple functional association networks using a Greedy Algorithm. This is achieved by mining the gene function from weighted gene co-expression networks based on neighbor similarity, as per the available datasets. Subsequently, Linear Regression and Greedy Algorithm are applied simultaneously for exclusion of the redundant nodes. Then, assigning the indexing rates for the remaining nodes in the dataset further assists the process.

Results and Conclusion: In comparison with other well-known algorithms, this method is 93% more efficient, as per three selected benchmarks.

Cite this article as:

Kheirkhah Atabak *, Daud Mohd Salwani and Kamardin Kamilia , Enhancing Efficiency of Protein Functional Prediction Through Association Network Using Greedy Weighting Method, Current Bioinformatics 2017; 12 (3) . https://dx.doi.org/10.2174/1574893611666161118142028

DOI https://dx.doi.org/10.2174/1574893611666161118142028	Print ISSN 1574-8936
Publisher Name Bentham Science Publisher	Online ISSN 2212-392X