Aug 2026· Omics· pp.
15578100261472236
· 0 citations· 91 references
Medicine
TL;DR
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.
Abstract
Lung cancer remains a leading cause of cancer-related mortality worldwide due to its extensive molecular heterogeneity, late-stage diagnosis, and therapeutic resistance. Advances in high-throughput omics technologies have enabled comprehensive characterization of tumors across multiple biological layers, including genomics, epigenomics, transcriptomics, proteomics, and metabolomics. However, single-omics analyses provide only fragmented insights into tumor biology, highlighting the need for integrative multiomics approaches. Artificial intelligence (AI), particularly machine learning and deep learning, has emerged as a powerful tool for integrating heterogeneous datasets and uncovering biologically and clinically relevant patterns. This review summarizes recent advances in AI-driven multiomics integration for lung cancer, highlighting its applications in molecular subtyping, biomarker discovery, prognosis prediction, therapeutic response modeling, and precision oncology. We also discuss current challenges, including data heterogeneity, model interpretability, reproducibility, and clinical translation, together with emerging strategies for integrating multimodal data such as radiomics and digital pathology. Finally, we introduce precision phenomics as a unifying framework that links molecular, spatial, functional, and clinical characteristics of tumors to support personalized cancer management. Collectively, AI-driven multiomics integration has the potential to transform lung cancer research and improve patient outcomes.
Breast cancer (BC) is a highly complex and heterogeneous malignancy and the most prevalent cancer among women worldwide. The diagnosis, prognosis, and the treatment of BC pose significant challenges that are responsible for their limited therapeutic efficacy. Omics-based technologies have gained substantial attention in BC diagnosis through molecular profiling and diverse clinical analytics. The integration of metabolomics, proteomics, transcriptomics, and genomics provides a multidimensional approach to personalized BC diagnosis and treatment through high-throughput molecular profiling. Moreover, the emergence of artificial intelligence (AI) has also supported more accurate and early diagnosis of BC through multimodal integration of diverse datasets. The integration of advanced deep learning (DL) and machine learning (ML) has been extensively exploited for tumor grading, histopathological classification, molecular profiling, diagnostic imaging, and prognostic prediction. This review aims to summarize recent developments in AI-driven multi-omics approaches for the discovery of BC biomarkers. We have also highlighted the integration of omics-based data like metabolomics, proteomics, transcriptomics, and genomics with key AI techniques, including ML and DL, that play a crucial role in the inclusion of multi-omics in cancer and biomarker discovery. We have further discussed AI-based BC screening and diagnostic approaches, as well as the contribution of AI models for patient stratification, biomarker discovery, and prediction of therapeutic response. Additionally, key limitations and challenges, including data heterogeneity, high computational complexity, and model interpretability, have also been highlighted in the present review. Conclusively, we have also outlined future perspectives on the integration of AI and multi-omics to revolutionize precision clinical medicine and improve clinical outcomes in BC theranostics.
V. Kumari, Harshita Tiwari, Swati Singh et al.· Medicinal research reviews (...· 1 citation
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
Cancer epigenomics has become central to understanding tumor initiation, progression, heterogeneity, and therapeutic response. High-throughput profiling technologies including bisulfite sequencing, chromatin immunoprecipitation sequencing (ChIP-seq), assay for transposase-accessible chromatin using sequencing (ATAC-seq), and RNA sequencing (RNA-seq) generate complex, multi-dimensional datasets that require robust computational frameworks for meaningful interpretation. This review outlines key bioinformatics workflows in cancer epigenomics, including data preprocessing, quality control, sequence alignment, signal detection, and differential analysis. While epigenomic data provide a mechanistic regulatory foundation, their full interpretive value emerges through integration with genomic, transcriptomic, and clinical data within computational oncology frameworks. Accordingly, we emphasize integrative modeling approaches that combine multi-omics data to uncover regulatory mechanisms, identify biomarkers, and define disease-associated molecular subtypes. Machine learning methods are increasingly applied for classification, prognosis prediction, and therapeutic response modeling; however, challenges remain in model interpretability, reproducibility, and external validation. We further highlight critical analytical limitations, including data heterogeneity, tumor complexity, lack of standardized workflows, and the persistent gap between association and biological mechanism. Emerging advances in single-cell epigenomics, spatial profiling, and explainable AI offer new opportunities to refine biological insight and clinical translation. Importantly, we propose a structured multi-layer interpretation framework that links computational outputs across data-level processing, epigenomics-informed integrative regulatory modeling, and multi-omics-informed clinical interpretation. This framework differs from existing pipelines by explicitly constraining how information is transformed across analytical layers, enabling traceable and mechanistically interpretable clinical inference.
M. Srivastava, Pratik Kumar, Ankita Chouhan et al.· Academia Molecular Biology a...· 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
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.
Nur Vanu, Nur Mohammad, Fahad Ahmed et al.· Computational and Systems On...· 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