engGNN: A Dual-Graph Neural Network for Omics-Based Disease Classification and Feature Selection.

Tiantian Yang, Yuxuan Wang, Zhenwei Zhou, Ching-Ti Liu

Abstract readPreprint

In one paragraph

Article in ArXiv, 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

Updated by
engGNN: a dual-graph neural network for omics-based disease classification and feature selection.2026

5 · Who and what money

Authors and funding

4 authors.

Tiantian YangDepartment of Mathematics and Statistical Science, University of Idaho, Moscow, Idaho, USA.

Yuxuan WangDepartment of Biostatistics, Boston University Boston, Massachusetts, USA.

Zhenwei ZhouDepartment of Biostatistics, Boston University Boston, Massachusetts, USA.

Ching-Ti LiuDepartment of Biostatistics, Boston University, Boston, Massachusetts, USA.

Funding

Sequence-structure-function relationships in human visual photopigmentsP20GM104420 · NIGMS · UNIVERSITY OF IDAHO · PI MILLER, CRAIG R · 2015 to 2024

$22.9M

Integrative Approaches to Identifying Function and Clinical Significance of Adiposity Susceptibility GenesR01DK122503 · NIDDK · UNIV OF NORTH CAROLINA CHAPEL HILL · PI Anne Justice, Ching-Ti Liu · 2020 to 2026

$4.5M

NIDDK NIH HHS R01 DK122503NIGMS NIH HHS P20 GM104420

6 · The paper itself

Abstract

Omics data, such as transcriptomics, proteomics, and metabolomics, provide critical insights into disease mechanisms and clinical outcomes. However, their high dimensionality, small sample sizes, and intricate biological networks pose major challenges for reliable prediction and meaningful interpretation. Graph Neural Networks (GNNs) offer a promising way to integrate prior knowledge by encoding feature relationships as graphs. Yet, existing methods typically rely solely on either an externally curated feature graph or a data-driven generated one, which limits their ability to capture complementary information. To address this, we propose the

Indexed as

Biological networksDisease classificationFeature selectionGraph neural networksOmics data

Identifiers

PMID41647208

PMCPMC12869420

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LicenceCC BY

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Registered trials

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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.