Determination of protein-protein interaction through Artificial Neural Network and Support Vector Machine: A Comparative study

Protein-protein interactions (PPI) plays considerable role in most of the cellular processes and study of PPI enhances understanding of molecular mechanism of the cells. After emergence of proteomics, huge amount of protein sequences were generated but there interaction patterns are still unrevealed. Traditionally various techniques were used to predict PPI but are deficient in terms of accuracy. To overcome the limitations of experimental approaches numerous computational approaches were developed to find PPI. However previous computational approaches were based on descriptors, various external factors and protein sequences. In this article, a sequence based prediction model is proposed by using various machine learning approaches. A comparative study was done to understand efficiency of various machine learning approaches. Large amount of yeast PPI data have been analyzed. Same data has been incorporated for different classification approach like Artificial Neural Network (ANN) and Support Vector Machine (SVM), and compared their results. Existing methods with additional features were implemented to enhance the accuracy of the result. Thus it was concluded that efficiency of this model was more admirable than those existing sequence-based methods; therefore it can be effective for future proteomics research work

Prediction of protein-protein interactions between viruses and human by an SVM model

<p>Abstract</p> <p>Background</p> <p>Several computational methods have been developed to predict protein-protein interactions from amino acid sequences, but most of those methods are intended for the interactions within a species rather than for interactions across different species. Methods for predicting interactions between homogeneous proteins are not appropriate for finding those between heterogeneous proteins since they do not distinguish the interactions between proteins of the same species from those of different species.</p> <p>Results</p> <p>We developed a new method for representing a protein sequence of variable length in a frequency vector of fixed length, which encodes the relative frequency of three consecutive amino acids of a sequence. We built a support vector machine (SVM) model to predict human proteins that interact with virus proteins. In two types of viruses, human papillomaviruses (HPV) and hepatitis C virus (HCV), our SVM model achieved an average accuracy above 80%, which is higher than that of another SVM model with a different representation scheme. Using the SVM model and Gene Ontology (GO) annotations of proteins, we predicted new interactions between virus proteins and human proteins.</p> <p>Conclusions</p> <p>Encoding the relative frequency of amino acid triplets of a protein sequence is a simple yet powerful representation method for predicting protein-protein interactions across different species. The representation method has several advantages: (1) it enables a prediction model to achieve a better performance than other representations, (2) it generates feature vectors of fixed length regardless of the sequence length, and (3) the same representation is applicable to different types of proteins.</p

Multi Layer Analysis

This thesis presents a new methodology to analyze one-dimensional signals trough a new approach called Multi Layer Analysis, for short MLA. It also provides some new insights on the relationship between one-dimensional signals processed by MLA and tree kernels, test of randomness and signal processing techniques. The MLA approach has a wide range of application to the fields of pattern discovery and matching, computational biology and many other areas of computer science and signal processing. This thesis includes also some applications of this approach to real problems in biology and seismology

Fusion of classifiers for predicting Protein-Protein interactions

Prediction of protein-protein interaction is a difficult and an important problem in biology. In this paper, we describe a very general method for predicting protein-protein interactions. The interaction mining approach is demonstrated by building a learning system based on experimentally validated protein-protein interactions in the human gastric bacterium Helicobacter pylori. We show that combining linear discriminant classifier and cloud points we obtain an error rate lower than previously published in the literature