Efficient Computational Prediction and Scoring of Human Protein-Protein Interactions Using a Novel Gene Expression Programming Methodology

Abstract

Abstract. Proteins and their interactions have been proven to play a central role in many cellular processes. Thus, many experimental methods have been developed for their prediction. These experimental methods are uneconomic and time consuming in the case of low throughput methods or inaccurate in the case of high throughput methods. To overcome these limitations, many computational methods have been developed to predict and score ProteinProtein Interactions (PPIs) using a variety of functional, sequential and structural data for each protein pair. Existing computational methods can still be enhanced in terms of classification performance and interpretability. In the present paper we present a novel Gene Expression Programming (GEP) algorithm, named as jGEPModelling 2.0, and apply it to the problem of PPI prediction and scoring. jGEPModelling2.0 is a variation of the classic GEP algorithm to make it suitable for the problem of PPI prediction and enhance its classification performance. To test its efficiency, we applied it to a public available dataset and compared it to two other state-of-the-art PPI prediction models. Experimental results proved that jGEPModelling2.0 outperformed existing methodologies in terms of classification performance and interpretability. (This paper is submitted for the CIAB2012 workshop).

A New Framework for Bridging the Gap from Protein-Protein Interactions to Biological Process Interactions

“…The methodologies used for comparative reasons with the proposed EVOKALMA model, include the Naive Bayesian Classifier which is the algorithm utilized by most PPI databases that include computationally predicted PPIs [15,16]. Moreover, two methodologies which have already provided encouraging results in predicting PPIs were used (Random Forests [17] and jGEPModel2.0 [18]) alongside with the hybrid combinations of Genetic Algorithms [19], Particle Swarm Optimization [20] and Differential Evolution [21] with Support Vector Machines (SVM) which have several applications in many fields.…”