Evidence map›Paper›PMID 42635126›Full record

ArticleNucleic acids research2026

CompBio and MIRaS-a multi-omic analysis platform built on a memory-based intelligence engine.

Ruteja A Barve, Chad E Storer, Wayne D Hoxsie, Curtis Marcum, Joshua F McMichael, Jared M Lalmansingh, Brittany K Smith, Mark R Johnson, Daniel J Kuster, Richard D Head

Abstract read
In one paragraph

Article in Nucleic acids research, 2026. 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

10 authors.

Ruteja A BarveCenter for Translational Bioinformatics, Washington University School of Medicine, St. Louis, MO 63110, United States.ORCID 0000-0002-1171-9761
Chad E StorerCenter for Translational Bioinformatics, Washington University School of Medicine, St. Louis, MO 63110, United States.
Wayne D HoxsieCenter for Translational Bioinformatics, Washington University School of Medicine, St. Louis, MO 63110, United States.
Curtis MarcumMcDonnell Genome Institute, Washington University School of Medicine, St. Louis, MO 63110, United States.
Joshua F McMichaelMcDonnell Genome Institute, Washington University School of Medicine, St. Louis, MO 63110, United States.ORCID 0000-0002-0197-2245
Jared M LalmansinghCenter for Translational Bioinformatics, Washington University School of Medicine, St. Louis, MO 63110, United States.ORCID 0000-0003-1505-4805
Brittany K SmithCenter for Translational Bioinformatics, Washington University School of Medicine, St. Louis, MO 63110, United States.
Mark R JohnsonMcDonnell Genome Institute, Washington University School of Medicine, St. Louis, MO 63110, United States.
Daniel J KusterCambrio LLC, Boston, MA 01776, United States.
Richard D HeadCenter for Translational Bioinformatics, Washington University School of Medicine, St. Louis, MO 63110, United States.ORCID 0000-0002-6865-1542

Funding

internal departmental funds
6 · The paper itself

Abstract

As molecular and cellular technologies have advanced, the need to analyze and interpret the resulting vast and multi-modal data into actionable intelligence has become a rate-limiting factor in scientific advancement. While traditional knowledgebase-pathway tools and LLM-based AI tools provide a degree of support in data interpretation, both exhibit known limitations. Presented here is the CompBio multi-omic analysis platform built upon Memory-based Intelligent Reasoning System (MIRaS), an associative reasoning engine grounded in episodic and semantic memory, capable of inferring biological relationships directly from stored knowledge rather than from model training. The MIRaS system is not dependent on human-curated pathway knowledgebases, provides statistical rigor, does not suffer from hallucination, and produces fully traceable results, representing a number of significant advancements. Furthermore, substantial effort has been placed in the computer-human knowledge transfer components of the system to enable efficient interaction, interpretation, and learning. The system has undergone years of extensive testing, validation, and evolution in omics-based analysis with many associated publications in translational and human subjects research. These publications have covered numerous areas such as nutrition, immunology, infectious disease, cardiovascular disease, and degenerative diseases with many experimentally verified findings. The CompBio-MIRaS system described and assessed here is freely available to all academic and non-profit institutions.

Indexed as

Artificial IntelligenceMultiomicsSoftwareHumansKnowledge Bases

Identifiers

PMID42635126
PMCPMC13501141

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.