Evidence map›Paper›PMID 40736744›Full record

ReviewBriefings in bioinformatics2025

Reconciling multiple connectivity-based systems biology methods for drug repurposing.

Catalina Gonzalez Gomez, Manuel Rosa-Calatrava, Julien Fouret

Abstract readReview
In one paragraph

Review in Briefings in bioinformatics, 2025. 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

3 authors.

Catalina Gonzalez GomezCIRI, Centre International de Recherche en Infectiologie, Team VirPath, Inserm U1111, Université Claude Bernard Lyon 1, CNRS UMR5308, ENS de Lyon, 8 rue Guillaume Paradin Faculté de Médecine RTH Laennec, Lyon 69008, France.ORCID 0009-0004-5232-6544
Manuel Rosa-CalatravaCIRI, Centre International de Recherche en Infectiologie, Team VirPath, Inserm U1111, Université Claude Bernard Lyon 1, CNRS UMR5308, ENS de Lyon, 8 rue Guillaume Paradin Faculté de Médecine RTH Laennec, Lyon 69008, France.
Julien FouretCIRI, Centre International de Recherche en Infectiologie, Team VirPath, Inserm U1111, Université Claude Bernard Lyon 1, CNRS UMR5308, ENS de Lyon, 8 rue Guillaume Paradin Faculté de Médecine RTH Laennec, Lyon 69008, France.ORCID 0000-0002-2427-5207

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

In the last two decades, numerous in silico methods have been developed for drug repurposing, to accelerate and reduce the risks about early drug development. Particularly, following Connectivity Map, dozens of distinct data-driven methods have been implemented to find candidates from the comparison of differential transcriptomic signatures. Interestingly, there have been multiple proposals to integrate available knowledge using systems biology databases and adapted algorithms from the network biology research field. Despite their similarities, these methods have been formulated inconsistently over the years, even if some of them are fundamentally similar. The aim of this review is to reconcile these integrative methods, focusing on elucidating their common structures while underlining the specificities of their strategies. To achieve this, we classified those methods into two main categories, provided schematic workflow representations, and presented a homogenized formulation for each.

Indexed as

Drug RepositioningSystems BiologyAlgorithmsComputational BiologyHumansconnectivity scoredata integrationdifferential expression signaturedrug repurposingsystem biology

Identifiers

PMID40736744
PMCPMC12309248

What OpenQuestion holds

Textmetadata
LicenceCC BY
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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.