An Intelligent Bioinformatics Framework Integrating Deep Neural Networks and Big Data Analytics for Comprehensive DNA–RNA Interaction and Tumor Genomic Analysis

Abstract

High-throughput sequencing technologies have grown exponentially, transforming genomics and transcriptomics, generating large amounts of multi-omics data that present great challenges in understanding the regulation interactions of complex DNA-RNA and tumour genomic heterogeneity. Traditional bioinformatics and machine-learning methods typically have a narrow range of scalability, the inability to combine heterogeneous data, and to represent the nonlinear molecular interactions between cells that regulate progression and development of cancer. On this work, we suggest an intelligent bioinformatics scheme that effectively combines deep neural networks (DNNs) with big data analytics to provide support for the extensive forecast of DNA and RNA relations and high-level genomic examination of tumours. The suggested architecture makes use of a unified component of whole-genome sequencing, RNA sequencing, epigenetic signals, and tumour mutation profiles a part of a scalable computational pipeline that relies on distributed big data platforms. Such convolutional and attention-based networks as deep learning modules are utilised to extract high-dimensional features, analyse the interaction, prioritise the biomarkers, and classify the distinct cancer subtypes with precision. The framework also includes systems level modelling to explain regulatory networks and pathways upheavals caused by mutation related to oncogenesis. The results of experimental assessments of benchmark cancer genomic datasets prove that the suggested methodology has the potential to substantially increase the accuracy of interaction prediction, improve the quality of tumour stratification, and enable efficient discovery of clinically meaningful molecular biomarkers over conventional statistical and machine-learning inferential bases. This framework is an effective and scalable solution to accuracy in oncology, molecular diagnostics, and integrative cancer genomics investigations through the joint implementation of predictive intelligence and scalable genomic data processing. The suggested approach offers a sufficiently bright future of uses in the area of personalised medicine, identification of therapeutic targets, and next-generational computational genomics.