Evidence map›Paper›PMID 41742924›Full record

ArticleMissouri medicine

AI for Scientific Discovery in Omics Data-Driven Precision Medicine.

Fuhai Li, Heming Zhang, Di Huang, Hao Li, Wenyu Li, Tianqi Xu, Yixin Chen, Michael Province, Philip R O Payne

Abstract read
PubMed Full text at PMC
In one paragraph

Article in Missouri medicine. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing 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

1 citing paper in PubMed.

  1. Article
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

9 authors.

Fuhai LiAssociate Director of the Center for Translational Bioinformatics and an Associate Professor in the Institute for Informatics, Data Science and Biostatistics, the Department of Pediatrics, Washington University School of Medicine, and the Department of Computer Science and Engineering at Washington University, St. Louis, Missouri, USA.
Heming ZhangPhD student in the Roy and Diana Vagelos Division of Biology & Biomedical Sciences program at Washington University, St. Louis, Missouri, USA.
Di HuangPhD student in the Department of Computer Science and Engineering at Washington University, St. Louis, Missouri, USA.
Hao LiBioinformatics research analyst in the Institute for Informatics, Data Science and Biostatistics at Washington University, St. Louis, Missouri, USA.
Wenyu LiPhD student in the Department of Computer Science and Engineering at Washington University, St. Louis, Missouri, USA.
Tianqi XuBioinformatics research analyst in the Institute for Informatics, Data Science and Biostatistics at Washington University, St. Louis, Missouri, USA.
Yixin ChenProfessor in the Department of Computer Science and Engineering at Washington University, St. Louis, Missouri, USA.
Michael ProvinceProfessor in the Department of Genetics, Washington University School of Medicine at Washington University, St. Louis, Missouri, USA.
Philip R O PayneDirector, Institute for Informatics, Data Science, and Biostatistics, the Janet and Bernard Becker Professor, Vice Chancellor for Biomedical Informatics and Data Science, WashU Medicine, and Chief Health AI Officer at BJC Health System and Washington University Medicine, St. Louis, Missouri, USA.

Funding

Systems-Level Approach to Neuronopathic Lysosomal Storage DisordersRM1NS132962 · NINDS · WASHINGTON UNIVERSITY · PI JONATHAN D COOPER, PATRICIA I DICKSON · 2023 to 2026
$6.3M
AI models of multi-omic data integration for ming longevity core signaling pathwaysR33AG078799 · NIA · WASHINGTON UNIVERSITY · PI LI, FUHAI, PROVINCE, MICHAEL A. · 2025 to 2025
$1.5M
Modeling and targeting tumor-immune signaling interactions in tumor microenvironmentR01LM013902 · NLM · WASHINGTON UNIVERSITY · PI Fuhai Li · 2023 to 2026
$1.4M
Combine Genomics and Symptoms Data Driven Models to Discover Synergistic Combinatory Therapies for Alzheimer's DiseaseR56AG065352 · NIA · WASHINGTON UNIVERSITY · PI LI, FUHAI · 2020 to 2021
$986k
AI models of multi-omic data integration for ming longevity core signaling pathwaysR21AG078799 · NIA · WASHINGTON UNIVERSITY · PI LI, FUHAI, PROVINCE, MICHAEL A. · 2023 to 2023
$461k
NIA NIH HHS R21 AG078799NIA NIH HHS R33 AG078799NIA NIH HHS R56 AG065352NINDS NIH HHS RM1 NS132962NLM NIH HHS R01 LM013902
6 · The paper itself

Abstract

In recent years, the rapid advancement of high-throughput technologies has led to the generation of vast and complex multi-omics datasets that are valuable for characterizing and understanding complex cell signaling network systems. On the other hand, large language models (LLMs), domain-specific foundation models (FMs) and AI agents, have achieved significant breakthroughs and have been revolutionizing scientific research. The convergence of these two trends is catalyzing a new era for biomedical research to augment and speed up scientific discovery and the development of precision medicine. In this study, we examine the large-scale omics datasets, emerging applications, and challenges at the intersection of massive omic datasets and related AI models and agents, highlighting how their integration is reshaping the landscape of biomedical research and precision medicine.

Indexed as

Agentic AIAI AgentsBiomedical ResearchFoundation ModelsLarge Language ModelsMulti-OmicsPrecision MedicineSignaling NetworkSystems Biology

Identifiers

PMID41742924
PMCPMC12931591

What OpenQuestion holds

Textmetadata
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.