An Advanced Multi-Omics Data Integration Framework Using Machine Learning and Bioinformatics Techniques for Tumor Genomics and Cancer Subtype Identification

Abstract

One of the most significant dilemmas to cancer genomics is tumour heterogeneity, since molecular differences among patients tend to downgrade the efficiency of single-omics analyses in the proper characterization of tumour behaviour and clinical outcome characteristics. Multi-omics data integration gives a global perspective of the intricate regulated interactions of the molecular processes involved in cancer progression and establishment; nonetheless, cross-dimensional assembly of heterogeneous and high-dimensional omics measurements has been a crucial obstacle in computation. In this work, we suggest a highly developed machine learning-based multi-omics information integration framework, which will improve the tumour genomic analysis and will help to make the cancer subtype identification strong. The suggested model uses representation learning, which is based on a deep learning approach, and attempts to combine various omics layers, such as genomic, transcriptomics, and epigenomics data, into a unified latent feature space. The quality of integration is determined in a systematic manner through several quantitative measures such as reconstruction error, values of clustering validity and stability analysis and compared with the standard methods of integration. The integrated representations are then subjected to unsupervised clustering solutions to determine discrete cancer subtypes, which are then performed on supervised classification models to confirm the predictability of the subtypes. Through experimental findings, it is indicative of the fact that the proposed framework has better integration quality and better separations of subtypes as compared with the baseline methods. Moreover, the subtypes identified have high biological and clinical significance as they share a considerable molecular signature and differ largely in the outcomes of patient survival. On the whole, this analysis indicates that a multi-omics integration using machine learning is efficient in the area of tumour genomics and can play a significant role in a more accurate cancer analysis and individualization in the therapy approach.