Aug 2026· International Journal of Advanced Research in Science, Communication and Technology· pp. 97· 0 citations
TL;DR
By comparing genomic and multi-omics strategies, this survey highlights ongoing hurdles related to privacy, bias, interpretability, and scalability.
Abstract
artificial intelligence (AI) is transforming the way we stumble on ailments early, but relying on a single form of facts—which includes genomics on my own—offers most effective a restrained picture of the whole organic complexity. Multi-omics integration—alongside aspect genomics, transcriptomics, proteomics, metabolomics, microbiome information, and scientific signs and symptoms and signs and symptoms—offers a more whole view of illness improvement. modern-day-day research show that graph neural networks (GNNs), federated getting to know (FL), and explainable AI (XAI) outperform genomic-most effective models through identifying novel biomarkers and enhancing diagnostic accuracy. Examples embody Tab net fusion for Alzheimer’s, multimodal deep reading for rheumatoid arthritis, and semi-supervised analyzing for hepatocellular carcinoma. By comparing genomic and multi-omics strategies, this survey highlights ongoing hurdles related to privacy, bias, interpretability, and scalability. destiny tips which consist of transformer-based fusion and basis fashions promise equitable, transparent, and clinically relevant AI-driven multi-omics structures for precision medicine.
The combination of AI and multi-omics has ushered in a new, revolutionary era in disease diagnosis and precision medicine. This review aims to summarize the current status of the artificial intelligence (AI)-based multi-omics diagnostic workflows employed in three disease paradigms: infectious, neurodegenerative, and metabolic diseases, and critically evaluate their translational potential from the discovery of biomarkers to digital twin healthcare systems. Machine Learning (ML) and Deep Learning (DL) algorithms, as well as Explainable Artificial Intelligence (XAI) algorithms, are explored for their application in linking genomics, transcriptomics, proteomics, metabolomics, and microbiomics datasets to enable high-dimensional molecular phenotyping. Harmonisation strategies for multi-omics data, AI driven feature selection, molecular pathway elucidation using graph neural networks (GNNs) and transformer architectures, and the development of digital twin models for personalised, dynamic health simulation are among the key themes. We delve deeper into the regulatory, ethical, and equity issues arising from the deployment of AI-omics systems across heterogeneous clinical settings. The review ends with a strategy for integrating validated AI-omics pipelines into the next-generation precision therapeutics and global health infrastructure.
Emmanuel Nkansah, M. Oladosu, M. Abah et al.· International Journal of Pre...· 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.
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
Alzheimer's disease (AD) is a progressively worsening type of brain disorder that damages the nerve cells. It is marked by the buildup of amyloid-β plaques outside the cells, tau neurofibrillary tangles inside the cells, and overall molecular-level dysfunction. The therapies currently available mainly cater to alleviating the symptoms, whereas the newly approved disease-modifying antibodies, such as lecanemab and donanemab, bring out only limited clinical improvements. Being complicated and involving many factors, AD requires sophisticated computer-based methods to combine different biological data and find suitable therapy targets. In this review, we discuss how artificial intelligence (AI)-powered multi-omics data integration can be a catalyst in discovering drug targets, identifying biomarkers, and stratifying patients for AD. By utilizing machine learning techniques like random forests, graph neural networks, and deep learning, AI-led multi-omics methods have helped uncover new therapeutic targets. Models that were built using federated learning across various institutions outperformed single-center models with a higher area under the curve score (0.84, 0.94 versus 0.76, 0.85). AI-guided patient stratification lessened the clinical trial's sample size needs by 40, 55% while still retaining 80, 90% statistical power. Multi-omics analyses further pointed out that it is the downstream molecular pathways, and not amyloid pathology alone, that are significantly involved in disease progression, thereby questioning the effectiveness of single-target anti-amyloid therapies and endorsing combination treatment strategies. AI and multi-omics data combination can be a game-changer in facilitating new target discovery, making clinical trial design more efficient, and ushering in precision medicine in AD.
T. Periyasamy, Nishu Sekar, Hariprasath Lakshmanan· Journal of Alzheimer's Disea...· 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
Conventional pharmacogenomic markers, including polymorphisms
in CYP or DPYD genes, often fail to accurately predict drug metabolism within solid tumors.
This discrepancy occurs because drug metabolism is an adaptive phenotype influenced
by the tumor microenvironment and host factors rather than a static inherited trait.
Therefore, a transition from variant-focused models to a dynamic systems pharmacology
framework using artificial intelligence (AI) is proposed in this review.
This review evaluates AI architectures, including graph convolutional networks
(GCNs), transformers, and reinforcement learning, for their ability to synthesize highdimensional
data. These models process multi-omic and spatially resolved metabolomic
data to track the shifting nature of tumor biology. We emphasize explainable AI (XAI) for
causal consistency and federated learning for privacy-preserving, multi-institutional collaboration.
AI models can effectively reverse-engineer metabolic states, forecast therapy resistance,
and map evolutionary pathways in tumors. By integrating diverse data streams,
these systems reveal the regulatory networks governing individual patient responses.
The findings emphasize that tumor drug metabolism is governed by dynamic
biological interactions rather than static genetic variants alone. AI-based multi-omics integration
therefore provides a promising strategy to capture the complex regulatory networks
linking tumor metabolism, genetic alterations, and therapeutic response. Such approaches
may improve the predictive accuracy of precision oncology models and support
more individualized treatment strategies.
AI models can effectively reverse-engineer metabolic states, forecast therapy
resistance, and map evolutionary pathways in tumors. By integrating diverse data streams,
these systems reveal the regulatory networks governing individual patient responses.
Anjali Ashok Thumbarambil, Senthil Madasamy· Current Pharmacogenomics and...· 0 citations
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