Evidence map›Paper›PMID 41266662›Full record

ReviewClinical and experimental medicine2025

AI-driven multi-omics integration in precision oncology: bridging the data deluge to clinical decisions.

Chou-Yi Hsu, Shavan Askar, Samer Saleem Alshkarchy, Priya Priyadarshini Nayak, Kassem A L Attabi, Mohammad Ahmar Khan, J Albert Mayan, M K Sharma, Sarvar Islomov, Hamed Soleimani Samarkhazan

Abstract readReview
PubMed Full text at PMC Publisher
In one paragraph

Review in Clinical and experimental medicine, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 72 papers, 2 of them syntheses that pooled it.

0numbers the graph read from it
0cells of the map it votes in
72citing papers in PubMed, 2 pooled it
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

72 citing papers in PubMed, 2 syntheses or guidelines pooled it.

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12 more citing papers are in PubMed but not listed here.

4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

10 authors.

Chou-Yi HsuDepartment of Pharmacy, Chia Nan University of Pharmacy and Science, Tainan, 71710, Taiwan.ORCID http://orcid.org/0000-0001-7105-1161
Shavan AskarErbil Polytechnic University , Technical college of computer and informatic engineering , Information System Engineering Department, Erbil, Iraq.
Samer Saleem AlshkarchyDepartment of Pathology, Al-Qasim Green University, Hilla, Iraq.
Priya Priyadarshini NayakDepartment of Medical Oncology, IMS and SUM Hospital, Siksha 'O' Anusandhan (Deemed to Be University), Bhubaneswar, Odisha, 751003, India.ORCID http://orcid.org/0009-0001-8421-3630
Kassem A L AttabiDepartment of Computers Techniques Engineering, College of Technical Engineering, The Islamic University, Najaf, Iraq.
Mohammad Ahmar KhanDepartment of MIS, College of Commerce and Business Administration, University - Dhofar University, Salalah, Oman.ORCID http://orcid.org/0000-0002-5247-5257
J Albert MayanDepartment of Computer Science and Engineering, Sathyabama Institute of Science and Technology, Tamil Nadu, Chennai, India.ORCID http://orcid.org/0000-0002-9403-1806
M K SharmaDepartment Mathematics, University Chaudhary Charan Singh University, Uttar Pradesh, Meerut, 250004, India.ORCID http://orcid.org/0000-0003-3071-5931
Sarvar IslomovScientific Researcher in Department of "Oncology and Hematology", National Children's Medical Center, 294 Parkent Street, Tashkent, Uzbekistan.ORCID http://orcid.org/0009-0000-8009-3291
Hamed Soleimani SamarkhazanStudent Research Committee, Department of Hematology and Blood Banking, School of Allied Medical Sciences, Shahid Beheshti University of Medical Sciences, Tehran, Iran. hamed.soleimani.s@gmail.com.ORCID http://orcid.org/0000-0003-1045-7613

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

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.

Indexed as

Artificial IntelligenceClinical Decision-MakingGenomicsMedical OncologyNeoplasmsPrecision MedicineDeep LearningHumansMachine LearningMetabolomicsMultiomicsProteomicsAI-driven multi-omicsClinical decision supportData integration challengesPersonalized cancer therapyPrecision oncology

Identifiers

PMID41266662
PMCPMC12634751

What OpenQuestion holds

Textmetadata
LicenceCC BY-NC-ND
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.