Jul 2026· Computational and Systems Oncology· 0 citations· 73 references
TL;DR
Big data in cancer genomics offer substantial potential to advance precision oncology by enabling more accurate and personalized treatment strategies, however, overcoming technical, ethical, and infrastructural barriers is essential to ensure effective translation into clinical practice and equitable healthcare outcomes.
Abstract
This review aims to explore the computational foundations of big data in cancer genomics and examine emerging pathways that support precision oncology and personalized cancer care. A narrative review approach was adopted to synthesize evidence from PubMed, Scopus, Web of Science, and IEEE Xplore. The literature search was conducted between January 10 and February 25, 2026, and 68 relevant studies were included in the final synthesis. Relevant studies were selected on the basis of their focus on computational methods, data integration strategies, and artificial intelligence (AI) applications in cancer genomics. Extracted data were organized into thematic categories and analyzed using an iterative synthesis framework. The findings indicate that high‐throughput sequencing and multi‐omics technologies have significantly expanded the volume and complexity of cancer‐related data. Advanced infrastructures, including cloud platforms, improve storage and access but raise privacy and interoperability concerns. Machine learning and AI support tumor classification, biomarker discovery, and treatment prediction. Integrative multi‐omics enhances biological insight and predictive accuracy. However, challenges such as data heterogeneity, limited model generalizability, and gaps in clinical integration remain. Big data in cancer genomics offer substantial potential to advance precision oncology by enabling more accurate and personalized treatment strategies. However, overcoming technical, ethical, and infrastructural barriers is essential to ensure effective translation into clinical practice and equitable healthcare outcomes.
The discussion on precision oncology integrates multiomics technologies and artificial intelligence, specifically addressing biomarker discovery and personalized therapeutic strategies. In this way, clinical translation and multiomics biomarkers are reconstructed challenges such as heterogeneity, validate, algorithmic bias, regulatory complexities, and ethical issues. This review critically evaluates how advanced technologies operating in an integrated manner facilitate precision oncology by supporting fields such as genomics, transcriptomics, proteomics, metabolomics, and radiomics. To conduct the study, we performed literature search using standard databases such as PubMed, Web of Science, and Scopus, focusing on papers published between 2020 and 2025. We address the all aspects of biomarker identification and clinical applications; we employed a five-stage framework comprising multiparametric data generation, integration, biomarker discovery, rigorous validation, and regulatory implementation. To further examine this review, we have employed emerging computational approaches, including machine learning and deep learning and graph neural networks alongside regulatory frameworks and ethical, legal, and social considerations. Discussing translation barriers, we consider factors such as limited reproducibility, validation, and critical discussion particularly studies. Ultimately, a future model based on standardized, validated, and transparent learning strategies accelerates and fosters the development of clinically reliable standards. Our review provides an integrative roadmap for modern, multiomics-driven biomarker approaches in precision oncology practice.
Ujwal Havelikar, Atharva A. Shinde, Hrushikesh Mhaismale et al.· Omics· 0 citations
Artificial intelligence (AI) has emerged as a transformative analytical paradigm for modelling cancer progression using medical imaging and complementary clinical data. This systematic review collates and analyses AI-powered systems and techniques for cancer progression risk analytics and prediction. It provides an empirical examination of algorithmic approaches, imaging-derived feature domains, and clinical metrics that underpin progression prediction. Following the PRISMA 2020 reporting guidance, empirical English-language studies published between 2020 and the final search cutoff date of 15 May 2026 were identified from PubMed, EBSCOhost, Google Scholar and Web of Science. Forty (40) eligible studies spanning diverse cancer types were included, appraised using the MMAT and synthesised narratively. The evidence shows that both classical machine learning models and deep learning architectures are widely used to extract predictive information from radiological data. Radiomic descriptors of intratumoural heterogeneity, tumour morphology, functional imaging biomarkers and multiscale transform-based features consistently demonstrate strong associations with disease progression. Imaging-derived features were linked to clinically meaningful progression endpoints, including progression-free survival, disease-free survival, recurrence and metastasis, while clinical and molecular covariates supported risk stratification. This study further develops the AI-driven cancer progression risk analytics framework (AI CanPRAF), which integrates AI taxonomies, imaging phenotypes, clinical context, progression analytics and decision support into one clinically oriented model. The results demonstrate the growing role of multimodal AI systems in the development of clinically grounded cancer progression analytics and prediction solutions.
Wellington Kanyongo, B. Chimbo· Information· 0 citations
Findings indicate that artificial intelligence can support the integration and analysis of multi-omics data, support the identification of genetic variants and disease associations, and improve predictive modeling for precision medicine.
Towsif Alam, Koushik Saha, M. K. K. Rony et al.· Journal of Multidisciplinary...· 0 citations
This review synthesizes key developments in ML for oncology, covering foundational algorithms alongside emerging approaches, and describes future directions, including federated learning, graph neural networks, longitudinal modeling, and integration of real-world and wearable data to support precision oncology.
Kanishk Yadav, Taneesha Gupta· Journal of the Egyptian Nati...· 0 citations
Recent literature on the application of artificial intelligence (AI) and data science within bioinformatics-driven cancer drug discovery is synthesized, examining how these tools are reshaping target identification, molecular design, biomarker discovery, and treatment personalization.
Yejide Eniola Dabiri· Magna Scientia Advanced Rese...· 0 citations
Precision phenomics is introduced as a unifying framework that links molecular, spatial, functional, and clinical characteristics of tumors to support personalized cancer management and has the potential to transform lung cancer research and improve patient outcomes.