Evidence map›Paper›PMID 42211343›Full record

ArticleBioinformatics advances2026

CardioVar: a machine learning framework for pathogenicity prediction of cardiomyopathy genetic variants.

Nabras Al-Mahrami, Aaisha Albalushi, Fahad Al Hattali, Mashael Al Balushi, Bushra Al Shamsi, Musallam Al-Oraimi, Tuqa Al Lawati, Mohamed Al-Rawahi, Nadia Alhashmi, Ahmed Al-Amri

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Article in Bioinformatics advances, 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

10 authors.

Nabras Al-MahramiMedical Laboratory Sciences Program, Oman College of Health Sciences, Muscat, PO 112 , Oman.ORCID https://orcid.org/0009-0003-0328-2883
Aaisha AlbalushiNational Genetic Center, Royal Hospital, Muscat, PO 111, Oman.
Fahad Al HattaliNational Genetic Center, Royal Hospital, Muscat, PO 111, Oman.
Mashael Al BalushiNational Genetic Center, Royal Hospital, Muscat, PO 111, Oman.
Bushra Al ShamsiNational Genetic Center, Royal Hospital, Muscat, PO 111, Oman.
Musallam Al-OraimiNational Genetic Center, Royal Hospital, Muscat, PO 111, Oman.
Tuqa Al LawatiNational Heart Center, Royal Hospital, Muscat, PO 111, Oman.
Mohamed Al-RawahiNational Heart Center, Royal Hospital, Muscat, PO 111, Oman.
Nadia AlhashmiNational Genetic Center, Royal Hospital, Muscat, PO 111, Oman.
Ahmed Al-AmriNational Genetic Center, Royal Hospital, Muscat, PO 111, Oman.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Motivation: CardioVar is a machine learning framework developed to predict the pathogenicity of cardiomyopathy-associated genetic variants, providing a rapid and disease-focused classification score to support variant interpretation in cardiogenetic workflows. Results: This approach supports variant interpretation within cardiac-related genes and may complement existing pan-disease models within a cardiomyopathy-specific context. In prospective case study settings, CardioVar demonstrated an 85% concordance with conventional tertiary analysis results. Availability and implementation: https://github.com/nibrasissa/CardioVar.

Identifiers

PMID42211343
PMCPMC13215092

What Socratic holds

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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.