Neural Latent Filtering for Gene Discovery in Breast Cancer Subtypes.

Danilo Menegatti, Giulia Fiscon, Alessandro Giuseppi, Paola Paci, Antonio Pietrabissa

Abstract read

In one paragraph

Article in Biotechnology reports (Amsterdam, Netherlands), 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it

0cells of the map it votes in

0citing 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

0 citing papers in PubMed.

No citing paper in PubMed yet.

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

5 authors.

Danilo MenegattiDepartment of Computer, Control and Management Engineering "Antonio Ruberti", Sapienza University of Rome, Via Ariosto 25, Rome, 00185, Italy.

Giulia FisconDepartment for the Promotion of Human Science and Quality of Life, San Raffaele Open University, Via di Val Cannuta 247, Rome, 00166, Italy.

Alessandro GiuseppiDepartment of Computer, Control and Management Engineering "Antonio Ruberti", Sapienza University of Rome, Via Ariosto 25, Rome, 00185, Italy.

Paola PaciDepartment of Computer, Control and Management Engineering "Antonio Ruberti", Sapienza University of Rome, Via Ariosto 25, Rome, 00185, Italy.

Antonio PietrabissaDepartment of Computer, Control and Management Engineering "Antonio Ruberti", Sapienza University of Rome, Via Ariosto 25, Rome, 00185, Italy.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Gene selection from expression data represents a challenging task, primarily due to the high data dimensionality and the vast number of genes that would be identified, many of which may be unrelated to cancer-relevant biological processes. To tackle this issue, filtering methods constitute an effective solution to identify the most informative genes, which can serve as potential biomarkers to tailor cancer therapies. This work proposes a novel neural-based filtering approach which aims to identify genes by means of their latent representation extracted from RNA Sequencing expression data. This approach has been applied to study breast invasive carcinoma dataset, aiming to identify the most relevant genes of two breast cancer subtypes, Luminal-A and Basal-like, to better investigate their molecular landscape.

Indexed as

Artificial intelligenceBreast cancerNeural networksNodes

Identifiers

PMID41560779

DOI10.1016/j.btre.2025.e00942

PMCPMC12814072

What Socratic holds

Textmetadata

LicenceCC BY-NC-ND

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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 Socratic graph.