Evidence map›Paper›PMID 38890548›Full record

ReviewMolecular systems biology2024

Molecular causality in the advent of foundation models.

Sebastian Lobentanzer, Pablo Rodriguez-Mier, Stefan Bauer, Julio Saez-Rodriguez

Abstract readReview
In one paragraph

Review in Molecular systems biology, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers.

0numbers the graph read from it
0cells of the map it votes in
8citing 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

8 citing papers in PubMed.

  1. Review
  2. Review
  3. Article
  4. Review
  5. Article
  6. Article
  7. Predicting fitness inFrontiers in tuberculosis · 2025
    Article
  8. 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

4 authors.

Sebastian LobentanzerHeidelberg University, Faculty of Medicine and Heidelberg University Hospital, Institute for Computational Biomedicine, Heidelberg, Germany. sebastian.lobentanzer@gmail.com.ORCID http://orcid.org/0000-0003-3399-6695
Pablo Rodriguez-MierHeidelberg University, Faculty of Medicine and Heidelberg University Hospital, Institute for Computational Biomedicine, Heidelberg, Germany.ORCID http://orcid.org/0000-0002-4938-4418
Stefan BauerHelmholtz AI and TU Munich, Munich, Germany.ORCID http://orcid.org/0000-0003-1712-060X
Julio Saez-RodriguezHeidelberg University, Faculty of Medicine and Heidelberg University Hospital, Institute for Computational Biomedicine, Heidelberg, Germany. pub.saez@uni-heidelberg.de.ORCID http://orcid.org/0000-0002-8552-8976

Funding

EC | Horizon 2020 Framework Programme (H2020) 951773EC | Horizon 2020 Framework Programme (H2020) 965193
6 · The paper itself

Abstract

Correlation is not causation: this simple and uncontroversial statement has far-reaching implications. Defining and applying causality in biomedical research has posed significant challenges to the scientific community. In this perspective, we attempt to connect the partly disparate fields of systems biology, causal reasoning, and machine learning to inform future approaches in the field of systems biology and molecular medicine.

Indexed as

CausalityMachine LearningSystems BiologyBiomedical ResearchHumansModels, BiologicalCausalityFoundation ModelsInductive BiasLatent SpacesSystems Biology

Identifiers

PMID38890548
PMCPMC11297329

What OpenQuestion 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 OpenQuestion graph.