Evidence map›Paper›PMID 41111912›Full record

ReviewiScience2025

Multilevel classification framework for breast cancer cell selection and its integration with advanced disease models.

Catarina Franco Jones, Diogo Dias, Ana C Moreira, Gil Gonçalves, Stefano Cinti, Mustafa B A Djamgoz, Frederico Castelo Ferreira, Paola Sanjuán-Alberte, Rosalia Moreddu

Abstract readReview
PubMed Full text at PMC Publisher
In one paragraph

Review in iScience, 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

9 authors.

Catarina Franco JonesSchool of Electronics and Computer Science, University of Southampton, Southampton SO171BJ, UK.
Diogo DiasDepartment of Bioengineering, Instituto Superior Técnico, Universidade de Lisboa, Lisbon 1049-001, Portugal.
Ana C MoreiraCentre for Mechanical Technology and Automation (TEMA), University of Aveiro, Aveiro 3810-193, Portugal.
Gil GonçalvesCentre for Mechanical Technology and Automation (TEMA), University of Aveiro, Aveiro 3810-193, Portugal.
Stefano CintiUniversity of Naples "Federico II", Naples, Italy.
Mustafa B A DjamgozDepartment of Life Sciences, Imperial College London, South Kensington Campus, London SW7 2AZ, UK.
Frederico Castelo FerreiraDepartment of Bioengineering, Instituto Superior Técnico, Universidade de Lisboa, Lisbon 1049-001, Portugal.
Paola Sanjuán-AlberteDepartment of Bioengineering, Instituto Superior Técnico, Universidade de Lisboa, Lisbon 1049-001, Portugal.
Rosalia MoredduSchool of Electronics and Computer Science, University of Southampton, Southampton SO171BJ, UK.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Breast cancer cell lines are indispensable tools for unraveling disease mechanisms, enabling drug discovery, and developing personalized treatments, yet their heterogeneity and inconsistent classification pose significant challenges in model selection and data reproducibility. This review aims at providing a comprehensive and user-friendly framework for broadly mapping the features of breast cancer types and commercially available human breast cancer cell lines, defining

Indexed as

Biological sciences research methodologiesCancerTechnical aspects of cell biology

Identifiers

PMID41111912
PMCPMC12529357

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.