AI-bioinformatics synergy for precision medicine in brain diseases: integrating radiomics and multi-omics data.
- 1Department of Biomedical Sciences, Marquette University, 1250 W Wisconsin Ave, Milwaukee, WI 53233, United States.
- 2Department of Psychology, University of Wisconsin Milwaukee, 3203 N Downer Ave, Milwaukee, WI 53211, United States.
- 3Department of Psychology, University of Cyprus, 1 Panepistimiou Avenue, Aglantzia, Nicosia 2109, Cyprus.
Abstract
The convergence of artificial intelligence (AI) and bioinformatics is reshaping the landscape of precision medicine in brain diseases, particularly in neurological diseases and brain tumors. Despite advances in both fields, the combined application of radiomics and multi-omics fusion remains underutilized, even though integrative frameworks are essential for addressing the complexity and heterogeneity of imaging and molecular data. This review examines how integrative AI-bioinformatics frameworks enable the fusion of radiomics with genomics, transcriptomics, proteomics, and other omics modalities to more comprehensively model disease complexity. Leveraging deep learning, graph neural networks, and advanced representation learning, these multimodal systems facilitate biomarker discovery, patient stratification, and personalized treatment strategies. We critically evaluate key challenges including data heterogeneity, limited interpretability, and barriers to clinical validation, and highlight emerging solutions aimed at enhancing reproducibility and translational readiness. Case studies in gliomas, Alzheimer's disease, and multiple sclerosis illustrate the clinical utility of unified imaging-omics architectures. Ultimately, this review positions radiomics and multi-omics integration as a foundational approach for the next generation of precision medicine in brain diseases by bridging the gap between computational innovation and clinical application.
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BibTeX
@article{altas2026aibioinformatics,
title = {{AI-bioinformatics synergy for precision medicine in brain diseases: integrating radiomics and multi-omics data.}},
author = {Bekir Altas and Polymnia Georgiou and Anna Onisiforou},
journal = {Briefings in bioinformatics},
year = {2026},
month = sep,
doi = {10.1093/bib/bbag568},
url = {https://europepmc.org/article/MED/42893563},
note = {CC BY-NC},
}
RIS
TY - JOUR
TI - AI-bioinformatics synergy for precision medicine in brain diseases: integrating radiomics and multi-omics data.
AU - Bekir Altas
AU - Polymnia Georgiou
AU - Anna Onisiforou
PY - 2026
DA - 2026/09/01
JO - Briefings in bioinformatics
DO - 10.1093/bib/bbag568
UR - https://europepmc.org/article/MED/42893563
AB - The convergence of artificial intelligence (AI) and bioinformatics is reshaping the landscape of precision medicine in brain diseases, particularly in neurological diseases and brain tumors. Despite advances in both fields, the combined application of radiomics and multi-omics fusion remains underutilized, even though integrative frameworks are essential for addressing the complexity and heterogeneity of imaging and molecular data. This review examines how integrative AI-bioinformatics frameworks enable the fusion of radiomics with genomics, transcriptomics, proteomics, and other omics modalities to more comprehensively model disease complexity. Leveraging deep learning, graph neural networks, and advanced representation learning, these multimodal systems facilitate biomarker discovery, patient stratification, and personalized treatment strategies. We critically evaluate key challenges including data heterogeneity, limited interpretability, and barriers to clinical validation, and highlight emerging solutions aimed at enhancing reproducibility and translational readiness. Case studies in gliomas, Alzheimer's disease, and multiple sclerosis illustrate the clinical utility of unified imaging-omics architectures. Ultimately, this review positions radiomics and multi-omics integration as a foundational approach for the next generation of precision medicine in brain diseases by bridging the gap between computational innovation and clinical application.
SN - 1467-5463
SN - 1477-4054
ER -