Evidence mapPaperPMID 41676557Full record

ArticlebioRxiv : the preprint server for biology2026

MultiGEOmics: Graph-Based Integration of Multi-Omics via Biological Information Flows.

Bizhan Alipour Pijani, Jubair Ibn Malik Rifat, Serdar Bozdag

Abstract readPreprint
In one paragraph

Article in bioRxiv : the preprint server for biology, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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

0 citing papers in PubMed.

No citing paper in PubMed yet.

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

3 authors.

Bizhan Alipour PijaniDepartment of Computer Science & Engineering, University of North Texas, Denton, TX 76203, USA.ORCID 0009-0004-6824-4519
Jubair Ibn Malik RifatDepartment of Computer Science & Engineering, University of North Texas, Denton, TX 76203, USA.ORCID 0009-0007-4838-5711
Serdar BozdagDepartment of Computer Science & Engineering, University of North Texas, Denton, TX 76203, USA.ORCID 0000-0002-4813-4310

Funding

Advanced machine learning models to integrate multi-modal biomedical datasets for gene regulation and precision medicineR35GM133657 · UNIVERSITY OF NORTH TEXAS · 2025 to 2025
$540k
NIGMS NIH HHS R35 GM133657
6 · The paper itself

Abstract

Multi-omics datasets capture complementary aspects of biological systems and are central to modern machine learning applications in biology and medicine. Existing graph-based integration methods typically construct separate graphs for each omics type and focus primarily on intra-omic relationships. As a result, they often overlook cross-omics regulatory signals-bidirectional interactions across omics layers-that are critical for modeling complex cellular processes. A second major challenge is missing or incomplete omics data; many current approaches degrade substantially in performance or exclude patients lacking one or more omics modalities. To address these limitations, we introduce

Identifiers

PMID41676557
PMCPMC12889541

What Socratic 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 Socratic graph.