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

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.