Prediction of RNA-protein binding interactions in H. salinarum using machine learning techniques

Resumo

Ribonucleoprotein (RNP) interactions engage in critical roles within a broad range of cellular processes, ranging from transcriptional and posttranscriptional regulation of gene expression to host protection in opposition to pathogens. High throughput experiments to recognize RNA-protein interactions produce details about the complexity of interaction networks, but require time and considerable efforts. Therefore, there is need to have for trustworthy computational approaches for predicting ribonucleoprotein interactions. With this research project, we discuss an amount of approaches which have been formulated to predict the ability of proteins and RNA molecules to associate based on advance learning machine methods (Self Organizing Map (SOM), SOM-based Optimization (SOMO), Double Parallel Feedforward Neural Network (DPFNN), Extreme Learning Machine (ELM) and Support Vector Machine (SVM)).