Evidence map›Paper›PMID 35860408›Full record

ArticleComputational and structural biotechnology journal2022

i-Modern: Integrated multi-omics network model identifies potential therapeutic targets in glioma by deep learning with interpretability.

Xingxin Pan, Brandon Burgman, Erxi Wu, Jason H Huang, Nidhi Sahni, S Stephen Yi

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Computational and structural biotechnology journal, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 19 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
19citing papers in PubMed, 1 pooled it
–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

19 citing papers in PubMed, 1 synthesis or guideline pooled it.

  1. Pooled it
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  5. Multi-Omics Integration for Advancing Glioma Precision Medicine.Annals of clinical and translational neurology · 2026
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  7. Artificial Intelligence-Driven Multi-Omics Approaches in Glioblastoma.International journal of molecular sciences · 2025
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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

6 authors.

Xingxin PanDepartment of Oncology, Livestrong Cancer Institutes, Dell Medical School, The University of Texas at Austin, Austin, TX 78712, USA.
Brandon BurgmanDepartment of Oncology, Livestrong Cancer Institutes, Dell Medical School, The University of Texas at Austin, Austin, TX 78712, USA.
Erxi WuDepartment of Oncology, Livestrong Cancer Institutes, Dell Medical School, The University of Texas at Austin, Austin, TX 78712, USA.
Jason H HuangNeuroscience Institute and Department of Neurosurgery, Baylor Scott & White Health, Temple, TX 76502, USA.
Nidhi SahniDepartment of Epigenetics and Molecular Carcinogenesis, The University of Texas MD Anderson Cancer Center, Houston, TX 77230, USA.
S Stephen YiDepartment of Oncology, Livestrong Cancer Institutes, Dell Medical School, The University of Texas at Austin, Austin, TX 78712, USA.

Funding

Network-based Framework to Decode Novel Gain-of-Function Mutations and their Mechanistic Roles in General Human DiseasesR35GM133658 · NIGMS · UNIVERSITY OF TEXAS AT AUSTIN · PI S. Stephen Yi · 2019 to 2026
$2.6M
NIGMS NIH HHS R35 GM133658
6 · The paper itself

Abstract

Effective and precise classification of glioma patients for their disease risks is critical to improving early diagnosis and patient survival. In the recent past, a significant amount of multi-omics data derived from cancer patients has emerged. However, a robust framework for integrating multi-omics data types to efficiently and precisely subgroup glioma patients and predict survival prognosis is still lacking. In addition, effective therapeutic targets for treating glioma patients with poor prognoses are in dire need. To begin to resolve this difficulty, we developed i-Modern, an integrated Multi-omics deep learning network method, and optimized a sophisticated computational model in gliomas that can accurately stratify patients based on their prognosis. We built a survival-associated predictive framework integrating transcription profile, miRNA expression, somatic mutations, copy number variation (CNV), DNA methylation, and protein expression. This framework achieved promising performance in distinguishing high-risk glioma patients from those with good prognoses. Furthermore, we constructed multiple fully connected neural networks that are trained on prioritized multi-omics signatures or even only potential single-omics signatures, based on our customized scoring system. Together, the landmark multi-omics signatures we identified may serve as potential therapeutic targets in gliomas.

Indexed as

Data integrationDeep learning modelGliomaMulti-omicsPatient stratification

Identifiers

PMID35860408
PMCPMC9284388

What OpenQuestion holds

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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.