Computational Intelligent Framework for Biomarker Identification in Multi Omics Data

Abstract

Omics data, encompassing genomics, proteomics, transcriptomics, and metabolomics, is generated through cutting-edge sequencing and mass spectrometry technologies. Biomarker identification, crucial in omics data analysis, relies on Deoxyribonucleic Acid (DNA), Ribonucleic Acid (RNA), and protein indicators to reveal physiological processes and disease symptoms. Leveraging machine learning and deep learning in computational bioinformatics enables the identification of biomarkers across single and multi-omics datasets, offering groundbreaking potential for early disease prediction. Integration of computational technologies with multi-omics data revolutionizes healthcare by facilitating advanced insights, aiding in disease diagnosis, prognosis, and targeted therapy development, thus advancing human health outcomes. This research aims to utilize computational technologies like machine learning, deep learning, and statistical methods for effective biomarker identification using multi-omics data, targeting disease survival prediction, subtype classification, and disease prediction. Beginning with a comprehensive review, the study explores intelligent computational approaches for biomarker identification across single and multi-omics datasets. It identifies a significant demand for a tailored framework specifically designed for biomarker identification using multi-omics data, highlighting shortcomings in existing tools related to data pre-processing, feature selection, biomarker validation, and prediction model creation. To bridge these gaps, the research proposes a novel framework for biomarker identification in multi-omics analysis, aiming to empower researchers with accessible and comprehensive options for conducting biomarker identification effectively. The framework is proposed for biomarker identification in multi-omics data which consists of six phases, i.e., data acquisition, data preprocessing, feature/ biomarker identification, biological interpretation, modeling, and performance evaluation. Through the data