Evidence map›Paper›PMID 40881104›Full record

ReviewMayo Clinic proceedings. Digital health2025

Artificial Intelligence and Multi-Omics in Pharmacogenomics: A New Era of Precision Medicine.

Mike Zack, Danil N Stupichev, Alex J Moore, Ioan D Slobodchikov, David G Sokolov, Igor F Trifonov, Allan Gobbs

Abstract readReview
PubMed Full text at PMC Publisher
In one paragraph

Review in Mayo Clinic proceedings. Digital health, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 23 papers.

0numbers the graph read from it
0cells of the map it votes in
23citing papers in PubMed
–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

23 citing papers in PubMed.

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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

7 authors.

Mike ZackPGxAI Inc., Palo Alto, CA.
Danil N StupichevPGxAI Inc., Palo Alto, CA.
Alex J MoorePGxAI Inc., Palo Alto, CA.
Ioan D SlobodchikovPGxAI Inc., Palo Alto, CA.
David G SokolovPGxAI Inc., Palo Alto, CA.
Igor F TrifonovPGxAI Inc., Palo Alto, CA.
Allan GobbsPGxAI Inc., Palo Alto, CA.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Pharmacogenomics is entering a transformative phase as high-throughput "omics" techniques become increasingly integrated with state-of-the-art artificial intelligence (AI) methods. Although early successes in single-gene pharmacogenetics reported clear clinical benefits, many drug response phenotypes are governed by intricate networks of genomic variants, epigenetic modifications, and metabolic pathways. Multi-omics approaches address this complexity by capturing genomic, transcriptomic, proteomic, and metabolomic data layers, offering a comprehensive view of patient-specific biology. Advanced AI models, including deep neural networks, graph neural networks, and representation learning techniques, further enhance this landscape by detecting hidden patterns, filling gaps in incomplete data sets, and enabling in silico simulations of treatment responses. Such capabilities not only improve predictive accuracy but also deepen mechanistic insights, revealing how gene-gene and gene-environment interactions shape therapeutic outcomes. At the same time, real-world data from diverse patient populations is broadening the evidence base, underscoring the importance of inclusive datasets and population-specific algorithms to reduce health disparities. Despite challenges related to data harmonization, interpretability, and regulatory oversight, the synergy between multi-omics integration and AI-driven analytics holds relevant promise for revolutionizing clinical decision-making. In this review, we highlighted key technological advances, discussed current limitations, and outlined future directions for translating multi-omics plus AI innovations into routine personalized medicine.

Identifiers

PMID40881104
PMCPMC12381589

What OpenQuestion holds

Textmetadata
LicenceCC BY-NC-ND
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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.