Translational Bioinformatics and Multi-Omics Integration for AI-Driven Biomarker Discovery in Precision Oncology

Technological advances in omics evaluation, bioinformatics, and artificial intelligence have made us rethink ways to improve patient outcomes. Collective quantification and characterization of biological data including genomics, epigenomics, metabolomics, and proteomics is now feasible at low cost with rapid turnover. Significant advances in the integration methods of these multiomics data sets by machine learning promise us a holistic view of disease pathogenesis and yield biomarkers for disease diagnosis and prognosis. Using machine learning tools and algorithms, it is possible to integrate multiomics data with clinical information to develop predictive models that identify risk before the condition is clinically apparent, thus facilitating early interventions to improve the health trajectories of the patients. In this review, we intend to update the readers on the recent developments related to the use of artificial intelligence in integrating multiomic and clinical data sets in the field of perinatology, focusing on neonatal intensive care and the opportunities for precision medicine. We intend to briefly discuss the potential negative societal and ethical consequences of using artificial intelligence in healthcare. We are poised for a new era in medicine where computational analysis of biological and clinical data sets will make precision medicine a reality. IMPACT: Biotechnological advances have made multiomic evaluations feasible and integration of multiomics data may provide a holistic view of disease pathophysiology. Artificial Intelligence and machine learning tools are being increasingly used in healthcare for diagnosis, prognostication, and outcome predictions. Leveraging artificial intelligence and machine learning tools for integration of multiomics and clinical data will pave the way for precision medicine in perinatology.

The integration of advanced computational methods into precision medicine represents a transformative advancement in healthcare, enabling highly personalized treatment strategies based on individual genetic, environmental, and lifestyle factors. These methodologies have significantly enhanced disease diagnostics, genomic analysis, and drug discovery. However, rapid expansion in this field has resulted in fragmented understandings of its evolution and persistent knowledge gaps. This study employs a scientometric approach to systematically map the research landscape, identify key contributors, and highlight emerging trends in precision medicine. Methods: A scientometric analysis was conducted using data retrieved from the Scopus database, covering publications from 2019 to 2024. Tools such as VOSviewer and R-bibliometrix package (version 4.3.0) were used to perform co-authorship analysis, co-citation mapping, and keyword evolution tracking. The study examined annual publication growth, citation impact, research productivity by country and institution, and thematic clustering to identify core research areas. Results: The analysis identified 4574 relevant publications, collectively amassing 70,474 citations. A rapid growth trajectory was observed, with a 34.3% increase in publications in 2024 alone. The United States, China, and Germany emerged as the top contributors, with Harvard Medical School, the Mayo Clinic, and Sichuan University leading in institutional productivity. Co-citation and keyword analysis revealed three primary research themes: diagnostics and medical imaging, genomic and multi-omics data integration, and personalized treatment strategies. Recent trends indicate a shift toward enhanced clinical decision support systems and precision drug discovery. Conclusions: Advanced computational methods are revolutionizing precision medicine, spurring increased global research collaboration and rapidly evolving methodologies. This study provides a comprehensive knowledge framework, highlighting key developments and future directions. The insights derived can inform policy decisions, funding allocations, and interdisciplinary collaborations, driving further advancements in healthcare solutions.

Integration of multi-omics data provides a comprehensive perspective on complex biological systems, facilitating advances in disease classification and biomarker discovery. However, the heterogeneity and high dimensionality of omics data present significant analytical challenges. To achieve effective and interpretable multi-omics integration, we propose a novel deep learning framework named MOGOLA(Multi-Omics integration by Gating and Omics-Linked Attention). MOGOLA consists of three core components: (1) A hybrid graph learning module that integrates Graph Convolutional Networks and Graph Attention Networks for intra-omics feature extraction. (2) A gating and confidence mechanism that adaptively weighs feature importance across different omics types. (3) A cross-omics attention-based fusion module that captures inter-omics relationships. Comprehensive evaluations on four benchmark datasets (BRCA, KIPAN, ROSMAP, and LGG) demonstrate that MOGOLA consistently outperforms eleven state-of-the-art approaches. Ablation studies further validate the contribution of each module, while biomarkers identification highlight the framework's clinical potential. These results show that MOGOLA is a robust and interpretable approach for multi-omics data integration and a contribution to advances in computational biology and precision medicine.

Artificial intelligence (AI) is significantly advancing precision medicine, particularly in the fields of immunogenomics, radiomics, and pathomics. In immunogenomics, AI can process vast amounts of genomic and multi-omic data to identify biomarkers associated with immunotherapy responses and disease prognosis, thus providing strong support for personalized treatments. In radiomics, AI can analyze high-dimensional features from computed tomography (CT), magnetic resonance imaging (MRI), and positron emission tomography/computed tomography (PET/CT) images to discover imaging biomarkers associated with tumor heterogeneity, treatment response, and disease progression, thereby enabling non-invasive, real-time assessments for personalized therapy. Pathomics leverages AI for deep analysis of digital pathology images, and can uncover subtle changes in tissue microenvironments, cellular characteristics, and morphological features, and offer unique insights into immunotherapy response prediction and biomarker discovery. These AI-driven technologies not only enhance the speed, accuracy, and robustness of biomarker discovery but also significantly improve the precision, personalization, and effectiveness of clinical treatments, and are driving a shift from empirical to precision medicine. Despite challenges such as data quality, model interpretability, integration of multi-modal data, and privacy protection, the ongoing advancements in AI, coupled with interdisciplinary collaboration, are poised to further enhance AI's roles in biomarker discovery and immunotherapy response prediction. These improvements are expected to lead to more accurate, personalized treatment strategies and ultimately better patient outcomes, marking a significant step forward in the evolution of precision medicine.

Abstract: This research volume offers a comprehensive and academically rigorous examination of the intersection between high-dimensional artificial intelligence (AI) and quantum computing, with a focus on their combined potential to transform scientific discovery, genomic analysis, and economic modeling. The book constructs an integrated conceptual and computational framework that synthesizes tensor-based AI architectures with quantum state formalism, enabling scalable solutions to complex, multi-dimensional problems that are intractable with classical methods alone. The study begins by analyzing the theoretical underpinnings of high-dimensional data spaces, manifold learning, and quantum mechanical principles such as superposition and entanglement. From this foundation, the volume explores hybrid quantum-AI methodologies applied to dynamic systems modeling in the sciences, precision diagnostics in genomics, and macroeconomic forecasting. Methodologically, the work employs a combination of variational quantum algorithms, quantum kernel methods, and tensor factorization, supported by simulations, real-world datasets, and benchmarking. Key results highlight improvements in model efficiency, predictive accuracy, and scalability across all domains explored. In genomics, quantum-AI models enable multi-omic integration and improved variant detection; in economics, they provide enhanced risk modeling and policy simulation. The implications extend to both theory and application, offering pathways for advancing scientific inquiry, personalizing medicine, and shaping resilient economic systems. The book concludes with a critical assessment of ethical, epistemological, and governance considerations, ensuring responsible and equitable development of quantum-AI technologies. Keywords High-dimensional AI, quantum computing, quantum machine learning, tensor factorization, quantum kernel methods, genomic intelligence, economic forecasting, hybrid quantum-classical systems, quantum reinforcement learning, multi-omic data integration, macroeconomic modeling, quantum algorithms, data scalability, scientific simulation, epistemological implications, algorithmic ethics, responsible AI, quantum explainability, federated quantum learning, quantum cloud infrastructure.

Three years ago, in 2021, our first collection on Application of Novel Statistical and Machine-learning Methods to High-dimensional Clinical Cancer and (Multi-) Omics Data has been a highlight for the readership in Frontiers, with over 52K views and 13K downloads. It has contributed greatly to the field by highlighting cutting edge research in the area of statistical genetics and methodology. Building on the success of the first volume, we bring another collection of insightful and thought-provoking research on this research topic by presenting four articles.In this second volume, we continue our previous focus on the development and application of novel statistical and machine-learning methods for high-dimensional clinical and (multi-)omics data in cancerrelated research. With the development of artificial intelligence (AI), especially the deep learning (DL), three out of four articles in Volume II investigated methods in multi-omics data integration using DL, while the fourth article investigated a new method for sequencing data processing.With the rapid evolvement of DL, significant progress has been made in applying DL based method to multi-omics integration. In a review article, Wekesa et al. comprehensively discussed the recent trends in using DL techniques for multi-omics data analysis in disease diagnosis, prognosis, and treatment. They focused particularly on multi-omics datasets that involve non-coding RNAs, such as miRNAs and long non-coding RNAs (lncRNAs), which played essential roles in cancer development and research. Several novel DL methods for integration and interpretation were highlighted, including contrastive learning, DeepLIFT, factorization machine deep learning (FMDNN), and graph neural networks (GNNs). Further, they assessed studies combing DL methods and emerging technologies, such as blockchain and internet of things (IoTs), in computational biology. Cases studies in breast and brain cancer detection demonstrated how integrating cutting-edge technologies and DL methods could advance the cancer research and clinical applications. By reading this review, it becomes clear that the development of innovative methods, algorithms, and analytical frameworks that integrate clinical, multi-omics, and imaging data for cancer research is particularly exciting. Moreover, they discussed potential challenges and future prospects, providing valuable insights into the field's future.In addition to the data types discussed in Volume I, we aim to showcase more studies that analyze imaging data, particularly due to the extensive use of imaging technique in cancer diagnosis, treatment, and research. Zhao et al. developed new models that can integrate radiomics data and whole genome sequencing data. Although their prediction outcome focused on proximal femoral strength related to hip fracture, their models can be straightforwardly adapted for imaging analysis in cancer research. Specifically, they extended the DL method of variational autoencoder from a single-view input into a multi-view input approach. Compared to other high-dimension multi-view information integration algorithms, the proposed model demonstrated superior performance in terms of root mean squared error (RMSE) and the coefficient of determination (R-squared). The significance of the analyzed features/variables was further interpreted through the leave-one-out technique.Another compelling study in this collection explores a linear dimensionality reduction method using DL. Dimension reduction is a critical step in the analysis of high-dimensional genetic and imaging data, as it helps to extract representative features for visualization or downstream analysis, such as prediction or classification. Li et al. introduced neural principal component analysis (nPCA), which enhances the widely-used original principal component analysis (PCA) by retaining the linear information of raw data. This new method was successfully applied to high-dimensional single-cell RNA sequencing datasets of pancreas. The nPCA method holds promise as an alternative dimension reduction technique for cancer investigators.The last article in this collection addressed the issue of sequencing data compression. With the reduction in sequencing cost, multi-omics data are increasingly generated through sequencing technologies. While bioinformatician and biostatistician often worked with processed sequencing file, such as bam and VCF files, the large raw sequencing files still need to be stored for backup, sharing, and legal requirements. Chen et al. presented a two-step framework for sequencing data compression, achieving up to a four-fold compression ratio compared to Gzip, all within an acceptable timeframe. Their tool, repaq, is freely available on GitHub, providing a valuable solution for managing large-scale sequencing data efficiently.In summary, the Volume II collection of original research, review, and technology papers highlights the latest advancements in the integrative analysis of clinical, imaging, and (multi-)omics cancer data, along with statistical and computational methods for high-dimensional data analysis. Combined with Volume I, we hope these collections will contribute to the integrative cancer research and inspire further methodology development in related fields.

Deep learning-based multi-omics data integration methods have the capability to reveal the mechanisms of cancer development, discover cancer biomarkers and identify pathogenic targets. However, current methods ignore the potential correlations between samples in integrating multi-omics data. In addition, providing accurate biological explanations still poses significant challenges due to the complexity of deep learning models. Therefore, there is an urgent need for a deep learning-based multi-omics integration method to explore the potential correlations between samples and provide model interpretability. Herein, we propose a novel interpretable multi-omics data integration method (DeepKEGG) for cancer recurrence prediction and biomarker discovery. In DeepKEGG, a biological hierarchical module is designed for local connections of neuron nodes and model interpretability based on the biological relationship between genes/miRNAs and pathways. In addition, a pathway self-attention module is constructed to explore the correlation between different samples and generate the potential pathway feature representation for enhancing the prediction performance of the model. Lastly, an attribution-based feature importance calculation method is utilized to discover biomarkers related to cancer recurrence and provide a biological interpretation of the model. Experimental results demonstrate that DeepKEGG outperforms other state-of-the-art methods in 5-fold cross validation. Furthermore, case studies also indicate that DeepKEGG serves as an effective tool for biomarker discovery. The code is available at https://github.com/lanbiolab/DeepKEGG.

The integration of multi-omics data—encompassing genomics, transcriptomics, proteomics, and metabolomics—has revolutionized biomedical research, offering unprecedented insights into disease mechanisms and therapeutic interventions. However, the complexity and volume of multi-omics datasets present significant analytical challenges that traditional computational methods struggle to address. Artificial Intelligence (AI), particularly deep learning and neural networks, has emerged as a powerful tool to overcome these limitations by enabling advanced data integration, biomarker discovery, and personalized treatment strategies. This paper explores the role of AI-driven multi-omics data integration in enhancing disease prediction, early diagnosis, and precision medicine. By leveraging AI models such as deep neural networks (DNNs), convolutional neural networks (CNNs), and transformers, researchers can analyze complex biological interactions, identify patterns indicative of disease onset, and stratify patient populations for tailored treatment approaches. Additionally, AI-powered feature selection methods facilitate the identification of disease-specific biomarkers across multiple omics layers, paving the way for more effective targeted therapies. Moreover, AI plays a crucial role in pharmacogenomics by predicting individualized drug responses, optimizing dosage regimens, and minimizing adverse drug reactions. Machine learning algorithms, including reinforcement learning and generative models, enable real-time modeling of drug-gene interactions, leading to safer and more efficacious therapeutic interventions. Despite the transformative potential of AI in multi-omics data analysis, challenges such as data standardization, model interpretability, and ethical considerations must be addressed to ensure reliability and clinical applicability. This paper provides a comprehensive review of AI-driven multi-omics research, highlighting current advancements, challenges, and future directions in precision medicine.

BackgroundThe incidence and mortality of colorectal cancer (CRC) have been rising steadily. Early diagnosis and precise treatment are essential for improving patient survival outcomes. Over the past decade, the integration of artificial intelligence (AI) and medical imaging technologies has positioned radiomics as a critical area of research in the diagnosis, treatment, and prognosis of CRC.MethodsWe conducted a comprehensive review of CRC-related radiomics literature published between 1 January 2013 and 31 December 2023 using the Web of Science Core Collection database. Bibliometric tools such as Bibliometrix, VOSviewer, and CiteSpace were employed to perform an in-depth bibliometric analysis.ResultsOur search yielded 1,226 publications, revealing a consistent annual growth in CRC radiomics research, with a significant rise after 2019. China led in publication volume (406 papers), followed by the United States (263 papers), whereas the United States dominated in citation numbers. Notable institutions included General Electric, Harvard University, University of London, Maastricht University, and the Chinese Academy of Sciences. Prominent researchers in this field are Tian J from the Chinese Academy of Sciences, with the highest publication count, and Ganeshan B from the University of London, with the most citations. Journals leading in publication and citation counts are Frontiers in Oncology and Radiology. Keyword and citation analysis identified deep learning, texture analysis, rectal cancer, image analysis, and management as prevailing research themes. Additionally, recent trends indicate the growing importance of AI and multi-omics integration, with a focus on improving precision medicine applications in CRC. Emerging keywords such as deep learning and AI have shown rapid growth in citation bursts over the past 3 years, reflecting a shift toward more advanced technological applications.ConclusionsRadiomics plays a crucial role in the clinical management of CRC, providing valuable insights for precision medicine. It significantly contributes to predicting molecular biomarkers, assessing tumor aggressiveness, and monitoring treatment efficacy. Future research should prioritize advancing AI algorithms, enhancing multi-omics data integration, and further expanding radiomics applications in CRC precision medicine.

Pharmacogenomics is the study of how genes affect a person's response to drugs. Thus, understanding the effect of drug at the molecular level can be helpful in both drug discovery and personalized medicine. Over the years, transcriptome data upon drug treatment has been collected and several databases compiled before drug treatment cancer cell multi-omics data with drug sensitivity (IC50, AUC) or time-series transcriptomic data after drug treatment. However, analyzing transcriptome data upon drug treatment is challenging since more than 20,000 genes interact in complex ways. In addition, due to the difficulty of both time-series analysis and multi-omics integration, current methods can hardly perform analysis of databases with different data characteristics. One effective way is to interpret transcriptome data in terms of well-characterized biological pathways. Another way is to leverage state-of-the-art methods for multi-omics data integration. In this paper, we developed Drug Response analysis Integrating Multi-omics and time-series data (DRIM), an integrative multi-omics and time-series data analysis framework that identifies perturbed sub-pathways and regulation mechanisms upon drug treatment. The system takes drug name and cell line identification numbers or user's drug control/treat time-series gene expression data as input. Then, analysis of multi-omics data upon drug treatment is performed in two perspectives. For the multi-omics perspective analysis, IC50-related multi-omics potential mediator genes are determined by embedding multi-omics data to gene-centric vector space using a tensor decomposition method and an autoencoder deep learning model. Then, perturbed pathway analysis of potential mediator genes is performed. For the time-series perspective analysis, time-varying perturbed sub-pathways upon drug treatment are constructed. Additionally, a network involving transcription factors (TFs), multi-omics potential mediator genes, and perturbed sub-pathways is constructed, and paths to perturbed pathways from TFs are determined by an influence maximization method. To demonstrate the utility of our system, we provide analysis results of sub-pathway regulatory mechanisms in breast cancer cell lines of different drug sensitivity. DRIM is available at: http://biohealth.snu.ac.kr/software/DRIM/.

Cancer’s staggering molecular heterogeneity demands innovative approaches beyond traditional single-omics methods. The integration of multi-omics data, spanning genomics, transcriptomics, proteomics, metabolomics and radiomics, can improve diagnostic and prognostic accuracy when accompanied by rigorous preprocessing and external validation; for example, recent integrated classifiers report AUCs around 0.81–0.87 for difficult early-detection tasks. This review synthesizes how artificial intelligence (AI), particularly deep learning and machine learning, bridges this gap by enabling scalable, non-linear integration of disparate omics layers into clinically actionable insights. We explore cutting-edge AI methodologies, including graph neural networks for biological network modeling, transformers for cross-modal fusion, and explainable AI (XAI) for transparent clinical decision support. Critical applications are highlighted, such as AI-driven therapy selection (e.g., predicting targeted therapy resistance), proteogenomic early detection, and radiogenomic non-invasive diagnostics. We further address translational challenges: data harmonization, batch correction, missing data imputation, and computational scalability. Emerging trends, federated learning for privacy-preserving collaboration, spatial/single-cell omics for microenvironment decoding, quantum computing, and patient-centric “N-of-1” models, signal a paradigm shift toward dynamic, personalized cancer management. Despite persistent hurdles in model generalizability, ethical equity, and regulatory alignment, AI-powered multi-omics integration promises to transform precision oncology from reactive population-based approaches to proactive, individualized care.

The rapid accumulation of multi-omics cancer data has created the opportunity for biological discovery and biomedical applications. In this study, we propose an approach that integrates multi-omics data to identify dysregulated pathways driving cancer subtypes, which simultaneously considers DNA methylation, DNA copy number, somatic mutation and gene expression profiles. After applying it to Breast Invasive Carcinoma (BRCA) in TCGA, we identify distinct top 30 dysregulated pathways for each breast cancer subtypes. The result suggests that dysregulated pathways of different subtypes display common and specific patterns. Furthermore, 44 differentially expressed genes with corresponding genetic and epigenetic dysregulation are retrieved from the subtype-specific pathways. Literature validation and functional enrichment analysis indicate that these genes are function associated with BRCA. Our method provides a new insight for identifying the driver of cancer subtypes through multi-omics data integration.

Breast cancer is the most common cancers among women worldwide. Early diagnosis and personalized medicine are crucial for the treatment of breast cancer. With the development of computer science and the emergence of whole slide imaging technology, artificial intelligence (AI) is having a surprisingly positive impact on the field of pathology, including breast pathology. The deployment of AI provides powerful tools for research in digital pathology and provides potential solutions in precision medicine in breast cancer. In this review, we systematically reviewed the applications of AI in digital pathology of breast cancer, including the identification of histological features, such as tumor-infiltrating lymphocytes, and the evaluation of classical biomarkers, such as human epidermal growth factor receptor 2, estrogen receptor, progesterone receptor. We also introduce the combined use of AI with multi-omics data in outcome prediction and treatment in breast cancer, and outline the evolution of AI methods applied in digital pathology. Collectively, the robustly evolving AI technologies would profoundly impact the precision pathology and medicine in breast cancer.

The integration of Artificial Intelligence (AI) into precision medicine represents a transformative advancement in modern healthcare, enabling clinicians to tailor medical treatments to individual patients based on genetic, environmental, and lifestyle factors. This review explores the impact of AI technologies in improving patient outcomes through the lens of precision medicine. It evaluates how machine learning, deep learning, and natural language processing (NLP) contribute to enhanced diagnostics, risk prediction, treatment personalization, and real-time monitoring. AI enables the analysis of complex, high-dimensional datasets—including electronic health records (EHRs), genomic sequences, imaging results, and wearable sensor data—to uncover hidden patterns and predictive biomarkers that inform clinical decisions. In oncology, for instance, AI-driven models support early detection and precise targeting of cancer therapies based on tumor-specific genetic profiles. Similarly, in cardiology and neurology, AI tools assist in identifying disease risks and suggesting preventive interventions tailored to individual needs. These personalized insights foster more accurate diagnoses, reduce adverse drug reactions, and improve treatment adherence, all contributing to better health outcomes. The review also highlights challenges, including data silos, interoperability barriers, ethical concerns, algorithmic bias, and the need for regulatory oversight. It emphasizes the importance of explainable AI, patient privacy, and transparency to build trust among stakeholders. Moreover, the adoption of AI in clinical settings requires adequate infrastructure, interdisciplinary collaboration, and clinician training to ensure responsible and effective implementation. Ultimately, the review underscores that AI-enhanced precision medicine holds immense potential to shift healthcare from reactive to proactive models, especially in managing chronic and complex diseases. By focusing on individualized care pathways, AI can bridge gaps in traditional healthcare delivery, address disparities, and optimize resource utilization. Future directions include developing standardized frameworks for AI integration, longitudinal studies to evaluate real-world impact, and strategies to ensure equitable access to AI-driven care across populatisons. This review offers a foundation for researchers, clinicians, and policymakers to harness AI responsibly in the pursuit of improved patient-centered healthcare.

Rapid advances in high-throughput sequencing technology have led to the generation of a large number of multi-omics biological datasets. Integrating data from different omics provides an unprecedented opportunity to gain insight into disease mechanisms from different perspectives. However, integrative analysis and predictive modeling from multi-omics data are facing three major challenges: i) heavy noises; ii) the high dimensions compared to the small samples; iii) data heterogeneity. Current multi-omics data integration approaches have some limitations and are susceptible to heavy noise. In this paper, we present MSPL, a robust supervised multi-omics data integration method that simultaneously identifies significant multi-omics signatures during the integration process and predicts the cancer subtypes. The proposed method not only inherits the generalization performance of self-paced learning but also leverages the properties of multi-omics data containing correlated information to interactively recommend high-confidence samples for model training. We demonstrate the capabilities of MSPL using simulated data and five multi-omics biological datasets, integrating up three omics to identify potential biological signatures, and evaluating the performance compared to state-of-the-art methods in binary and multi-class classification problems. Our proposed model makes multi-omics data integration more systematic and expands its range of applications.