Big data in U.S. neuro-oncology: trends and translational priorities

Big Data Analytics in Urologic Oncology: A Comprehensive Review of Large-scale Database Research and Clinical Applications

Big Data in Cancer Genomics: Computational Foundations and Emerging Pathways for Precision Oncology

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.

Machine learning and AI for cancer research and care: a review of applications, limitations, and future directions

Machine learning (ML) is transforming cancer research and care by enabling analysis of complex, high-dimensional datasets spanning genomics, transcriptomics, proteomics, imaging, and clinical records. By improving risk stratification, accelerating detection and diagnosis, and supporting treatment selection, ML has the potential to enhance survival outcomes while increasing efficiency across oncology workflows. This review synthesizes key developments in ML for oncology, covering foundational algorithms alongside emerging approaches. We highlight major application areas including early cancer detection, tumor classification, molecular subtyping, biomarker discovery, prognosis estimation, multi-omics integration, computational pathology, pharmacogenomics, and clinical decision support. We also summarize commonly used datasets, discuss the importance of interpretability for clinical trust, and outline barriers to translation such as data heterogeneity, bias, and regulatory constraints. Finally, we describe future directions, including federated learning, graph neural networks, longitudinal modeling, and integration of real-world and wearable data to support precision oncology. This review is intended for cancer researchers, clinicians, and data scientists seeking a practical overview of ML methods, opportunities, and translational considerations in oncology.

The landscape of artificial intelligence in neurodegenerative diseases: a systematic review.

BACKGROUND The rising global burden of neurodegenerative diseases underscores an urgent need for advanced research in diagnosis, prognosis, and treatment. Artificial Intelligence (AI) methods, particularly when applied to multimodal data, offer a powerful tool to address these challenges. However, a comprehensive overview and critique of the current landscape of AI methods is lacking. METHODS 4,685 records of peer-reviewed, primary research articles were screened and 1,956 articles reviewed in full text, yielding 1,186 included studies. For each included study, clinical objectives, disease focus, data modalities, modelling approach, evaluation strategy, and reporting practices were extracted. RESULTS Fewer than 5% of studies integrated pharmacological treatments into their predictive models, limiting the extent to which models can directly inform clinical decision-making. Neuroimaging was the predominant input modality, while integration of other clinically relevant data types was relatively rare. Reproducibility rates remain critically low at 35%, and external validation practices fail to use geographically and demographically diverse datasets. CONCLUSIONS Overall, AI research in neurodegenerative diseases suffers from significant limitations in reproducibility, data inclusivity, and clinical translatability. We provide a set of recommendations that can be adopted to address these issues and improve reliability and downstream clinical utility.

The GENIE Data Model: A Solid Tumor, Person-Centric Framework for Scalable, Harmonized Precision Oncology Data Collection.

Adequately powered analyses in precision oncology often require combining cohorts across institutions. Yet integration is constrained by the least granular source and may become infeasible when data elements are too heterogeneous to harmonize and map to a common data model. This challenge is acute in multi-institutional precision oncology research, where real-world evidence requires harmonized clinico-omic data integration. Existing models often lack sufficient treatment patterns, outcomes, and genomic data, limiting interoperability and scalability. To address these gaps, AACR Project GENIE™ (Genomics Evidence Neoplasia Information Exchange) developed the GENIE Data Model (GDM), a comprehensive, open-source, oncology data model for scalable, consistent, and interoperable data collection across solid tumors designed to effectively capture the patient's journey with cancer. Through iterative consensus-building, four working groups comprising 13 subject matter experts defined data elements across multiple clinical domains: patient characteristics, imaging, diagnosis, surgery, histopathology, biomarkers, systemic therapy, radiation, clinical trial history, disease response and outcomes, and social determinants of health. Elements were defined using standardized terminologies and permissible values to support mapping to HL7 FHIR, OMOP, and other existing oncology standards. The model architecture distinguishes manually abstracted elements from computationally collected elements, enabling parallel workflows. The GDM provides an extensible framework that addresses critical gaps and enables scalable, harmonized data collection essential for precision oncology and real-world evidence generation.

AI-Driven Multiomics Biomarkers for Precision Oncology: Navigating the Translational Gap and Regulatory Hurdles.

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.