Intelligent Framework for Omics Data Analysis using Machine Learning

Abstract

Omics data encompasses extensive genetic information as genomics, proteomics, transcriptomics, and metabolomics, generated through advanced sequencing and mass spectrometry technologies. In computational bioinformatics, machine learning techniques are harnessed for analysis of omics data. Recent advancements in omics data analysis presents a breakthrough in healthcare which enables researchers to predict the disease before its onset. The combination of computational technologies and omics data in healthcare has revolutionized the way large datasets are retrieved and analyzed. This integration enables researchers to extract valuable insights and make significant advancements in prediction for the development of targeted therapies which ultimately leads to improvements in human health. The substantial omics data generated necessitates the requirement of advanced computational methods for effective survival prediction and disease prediction. The aim of this research is to employ computational technologies such as machine learning, and metaheuristic methods for effective disease prediction and survival prediction of patients using omics data. At the beginning, a comprehensive review has been undertaken to explore computationally intelligent approaches for omics data analysis. It involved investigating, comparing, and categorizing diverse technologies and tools utilized in disease prediction, survival prediction, biomarker discovery, and disease recurrence using omics data. Through this critical analysis, it became evident that there is a significant demand for the development of effective framework specifically designed for survival prediction and disease prediction using omics data. Additionally, it was noted that existing tools in the field often lack the necessary provisions for users to make informed choices concerning data pre-processing, feature selection, and prediction models for omics data. This limitation underscores the crucial need for an accessible solution that empowers researchers with a wide range