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

• Grant number: 12/23329-5
• Support Opportunities: Scholarships in Brazil - Post-Doctoral
• Start date: July 01, 2013
• End date: May 31, 2015
• Field of knowledge: Biological Sciences - Biology
• Principal Investigator: Ricardo Zorzetto Nicoliello Vêncio
• Grantee: Atlas Khan
• Host Institution: Faculdade de Filosofia, Ciências e Letras de Ribeirão Preto (FFCLRP). Universidade de São Paulo (USP). Ribeirão Preto , SP, Brazil

Abstract

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)).

Scientific publications

(References retrieved automatically from Web of Science and SciELO through information on FAPESP grants and their corresponding numbers as mentioned in the publications by the authors)

KHAN, ATLAS; XUE, LI ZHENG; WEI, WU; QU, YANPENG; HUSSAIN, AMIR; VENCIO, RICARDO Z. N.. Convergence Analysis of a New Self Organizing Map Based Optimization (SOMO) Algorithm. COGNITIVE COMPUTATION, v. 7, n. 4, p. 477-486, AUG 2015. (12/23329-5)

KHAN, ATLAS; QU, YAN-PENG; LI, ZHENG-XUE. Convergence Analysis of a New MaxMin-SOMO Algorithm. INTERNATIONAL JOURNAL OF AUTOMATION AND COMPUTING, v. 16, n. 4, p. 534-542, AUG 2019. (12/23329-5)