Evidence map›Paper›PMID 42302787›Full record

ArticleCell reports methods2026

IAN, an intelligent system for omics data analysis and discovery.

Vijayaraj Nagarajan, Reiko Horai, Guangpu Shi, Cheng-Rong Yu, Jaanam Gopalakrishnan, Manoj Kumar Yadav, Michael H Liew, Calla Gentilucci, Rachel R Caspi

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Cell reports methods, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.

0numbers the graph read from it
0cells of the map it votes in
3citing 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

3 citing papers in PubMed.

  1. Article
  2. Review
  3. Article
4 · The record

Corrections and comments

5 · Who and what money

Authors and funding

9 authors.

Vijayaraj NagarajanLaboratory of Immunology, National Eye Institute, NIH, Bethesda, MD 20892, USA. Electronic address: nagarajanv@nih.gov.
Reiko HoraiLaboratory of Immunology, National Eye Institute, NIH, Bethesda, MD 20892, USA.
Guangpu ShiLaboratory of Immunology, National Eye Institute, NIH, Bethesda, MD 20892, USA.
Cheng-Rong YuMolecular Immunology Section, National Eye Institute, NIH, Bethesda, MD 20892, USA.
Jaanam GopalakrishnanNeuro-Immune Regulome Unit, National Eye Institute, NIH, Bethesda, MD 20892, USA.
Manoj Kumar YadavMolecular Immunology Section, National Eye Institute, NIH, Bethesda, MD 20892, USA.
Michael H LiewNeuro-Immune Regulome Unit, National Eye Institute, NIH, Bethesda, MD 20892, USA.
Calla GentilucciLaboratory of Immunology, National Eye Institute, NIH, Bethesda, MD 20892, USA.
Rachel R CaspiLaboratory of Immunology, National Eye Institute, NIH, Bethesda, MD 20892, USA.

Funding

T-CELL LINES AND CLONES IN UVEITAZ01EY000184 · NEI · NATIONAL EYE INSTITUTE · PI CASPI, RACHEL R. · 1985 to 2008
$6.6M
Understanding the microbial requirements for colonization and immunogenicity of commensal bacteria at the ocular surfaceR01EY032482 · NEI · UNIVERSITY OF PITTSBURGH AT PITTSBURGH · PI ST LEGER, ANTHONY J · 2021 to 2025
$2.0M
Intramural NIH HHS Z01 EY000184NEI NIH HHS R01 EY032482
6 · The paper itself

Abstract

IAN is an R package addressing the challenge of integrating, analyzing, and interpreting gene expression data using an artificial intelligence (AI) system. It leverages popular pathway (KEGG, WikiPathways, Reactome, and GO) and regulatory datasets (ChEA), along with STRING for protein-protein interactions, to perform standard enrichment analysis. A multi-agent architecture uses individual enrichment results to generate summaries using large language models (LLMs). These summaries are contextually integrated and interpreted by the LLM, guided by engineered prompts and grounding instructions, to provide useful explanations, system overviews, key regulators, and data-driven insights. We demonstrate IAN's function on published human transcriptomic data, evaluate IAN's robustness to underlying LLM choice and report human expert evaluations of the clarity and relevance of IAN's outputs. The IAN package is available at https://github.com/NIH-NEI/IAN.

Indexed as

Artificial IntelligenceComputational BiologyData AnalysisSoftwareHumansLarge Language Modelsbiological discoveryCP: computational biologyCP: systems biologygene regulationlarge language modelsLLM groundingLLMsmulti-agent AInetwork analysisomics data analysispathway enrichmentreproducibility

Identifiers

PMID42302787
PMCPMC13494537

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.