Prediction of piRNAs Disease Associations Using Artificial Neural Networks

• Grant number: 24/09531-3
• Support Opportunities: Scholarships in Brazil - Scientific Initiation
• Start date: September 01, 2024
• Status: Discontinued
• Field of knowledge: Physical Sciences and Mathematics - Computer Science
• Principal Investigator: Ricardo Cerri
• Grantee: Anna Carolina Brito Santos Farias
• Host Institution: Centro de Ciências Exatas e de Tecnologia (CCET). Universidade Federal de São Carlos (UFSCAR). São Carlos , SP, Brazil
• Associated scholarship(s): 25/03665-0 - AutoML for Multi-label Classification of piRNAs' Diseases, BE.EP.IC

Abstract

PIWI-interacting RNAs (piRNAs) are a class of small non-coding RNAs that are abundantly present in the human body. They play a fundamental role in gene expression regulation, embryonic development, and cell differentiation. Furthermore, a large number of recent studies show the involvement of piRNAs in various diseases such as tumors, male infertility, Alzheimer's, and cardiovascular diseases. However, despite the broad functionality of piRNAs in the human body and the applications of computing in biology, the study of disease prediction associated with piRNAs using high-performance architectures such as artificial neural networks remains scarce. Thus, the importance arises for a model using neural networks to predict piRNA-related diseases, thereby helping in their prevention and diagnosis. Additionally, it is known that a piRNA can be associated with two or more diseases simultaneously, characterizing the classification problem as a multi-label problem. In this context, this project aims to create a multi-label machine learning model for predicting diseases associated with piRNAs using artificial neural networks.