Evidence map›Paper›PMID 41209342›Full record

ArticleComputational and structural biotechnology journal2025

MiDNE a tool for Multi-omics genes and drugs interactions discovery.

Aurora Brandi, Barbara Majello, Ines Simeone, Massimiliano Romano, Michele Ceccarelli, Giovanni Scala

Abstract read
In one paragraph

Article in Computational and structural biotechnology journal, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.

0numbers the graph read from it
0cells of the map it votes in
2citing 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

2 citing papers in PubMed.

  1. Multi-omics integration identifiesTranslational andrology and urology · 2026
    Article
  2. Artemis: Harnessing Knowledge Graphs for Next-Generation Drug Target Prioritization.Computational and structural biotechnology journal · 2026
    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

6 authors.

Aurora BrandiDepartment of Biology, University of Naples "Federico II", Via Vicinale Cupa Cintia 26, Naples, 80126, Italy.
Barbara MajelloDepartment of Biology, University of Naples "Federico II", Via Vicinale Cupa Cintia 26, Naples, 80126, Italy.
Ines SimeoneDepartment of Electrical Engineering and Information Technologies, University of Naples "Federico II", Via Claudio 21, Naples, 80121, Italy.
Massimiliano RomanoDepartment of Biology, University of Naples "Federico II", Via Vicinale Cupa Cintia 26, Naples, 80126, Italy.
Michele CeccarelliDepartment of Electrical Engineering and Information Technologies, University of Naples "Federico II", Via Claudio 21, Naples, 80121, Italy.
Giovanni ScalaDepartment of Biology, University of Naples "Federico II", Via Vicinale Cupa Cintia 26, Naples, 80126, Italy.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

The availability of models representing molecular interactions in complex pathologies is essential for understanding their molecular setup and identifying therapeutic vulnerabilities. In this context, the advent of high-throughput technologies has enabled the rapid and cost-effective profiling of multiple omics layers, driving a paradigm shift from generalized models to disease-specific, context-aware modeling approaches. While the analysis of individual omics layers can provide information about specific aspects of cellular biology for a given disease, it often fails to capture complex interactions among molecules and drugs operating across different regulatory levels. Here, we introduce MiDNE (Multi-omics genes and Drugs Network Embedding), a novel computational framework that integrates experimental multi-omics data with pharmacological knowledge to uncover disease specific multi-omics gene and drug interactions. MiDNE integrates omics-specific networks, derived from experimental data, with known drug interactors in a multiplex heterogeneous network. It applies a network embedding procedure based on the random walk with restart algorithm to project genes and drugs into a shared multi-omics latent space, enabling gene-drug clustering and neighborhood search. We demonstrate the potential of MiDNE on Breast Invasive Carcinoma and Glioblastoma multiforme, by integrating gene expression, methylation, proteomic, and copy number variation profiles with curated drug-target interactions. By providing multilayer and disease-specific views of gene and drug interactions, MiDNE facilitates the discovery of actionable gene-drug relationships and the development of precision pharmacological strategies. MiDNE is available as both an open-source R package and a Shiny web application.

Indexed as

Cancer researchDrug discoveryMulti-omics data integrationR packageSystems biology

Identifiers

PMID41209342
PMCPMC12593681

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.