Evidence map›Paper›PMID 41249457›Full record

ReviewNPJ digital medicine2025

Advancing the frontier of rare disease modeling: a critical appraisal of in silico technologies.

Francesca Pistollato, Fabia Furtmann, Lindsay J Marshall, Surat Parvatam, Jan Turner, Flora Tshinanu Musuamba, Giulia Russo, Francesco Pappalardo

Abstract readReview
PubMed Full text at PMC Publisher
In one paragraph

Review in NPJ digital medicine, 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. Review
  2. 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

8 authors.

Francesca PistollatoHumane World For Animals, Brussels, Belgium.
Fabia FurtmannHumane World For Animals, Brussels, Belgium.
Lindsay J MarshallHumane World For Animals, Washington, D.C, NW, USA.
Surat ParvatamHumane World For Animals, Hyderabad, India.
Jan TurnerHumane World For Animals, Brussels, Belgium.
Flora Tshinanu MusuambaUniversity of Namur, NAmur Research Institute for LIfe Sciences (NARILIS), Clinical Pharmacology and Toxicology Research Unit, Namur, Belgium.
Giulia RussoDepartment of Drug and Health Sciences, The COMBINE Group, University of Catania, Catania, Italy.
Francesco PappalardoDepartment of Drug and Health Sciences, The COMBINE Group, University of Catania, Catania, Italy. francesco.pappalardo@unict.it.

Funding

European Commission 101137141
6 · The paper itself

Abstract

Rare diseases affect over 300 million people worldwide and pose unique research challenges. In silico approaches, such as mechanistic models, machine learning, and simulations, offer scalable tools for disease characterisation, drug discovery, and virtual trials. This review categorises these methods by context of use, critically appraises their strengths and limitations, and identifies barriers to translation, highlighting key opportunities and ongoing challenges in advancing computational strategies for rare disease research.

Identifiers

PMID41249457
PMCPMC12623476

What Socratic holds

Textmetadata
LicenceCC BY
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.