Author

Mohammed Majid Akhtar

Royal Brompton & Harefield NHS Foundation TrustORCID 0000-0003-3447-6376Unclaimed ORCID profile · follow to get notified of new preprints

Biology Disciplines

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Biophysics· 1 Genetic & Genomic Medicine· 1

Signature Assays, Organisms & Methods

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Organism:human· 2 Assay:GWAS summary statistics· 1 Approach:machine learning (classical)· 1 Research Focus:polygenic risk· 1 Assay:proteomics· 1 Approach:statistical model· 1 Research Focus:variant effect prediction· 1

Frequent Co-Authors

Luís Rocha LopesiD· 2 shared Perry M ElliotiD· 2 shared Petros SyrrisiD· 2 shared Alexandros ProtonotariosiD· 1 shared Catherine JohniD· 1 shared Cayetana Barbeito‐CaamañoiD· 1 shared Chiara BatiniiD· 1 shared Dominic PühringeriD· 1 shared

Activity by Year

2023-2026 · peak 1/yr

Co-Author Collaboration Network

10 top collaborators · 35 mutual co-author links

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2 shared papersGenetic & Genomic Medicine

Luís Rocha LopesORCID

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2 papers

Recent·Most cited

Genetic & Genomic MedicineJournal of Medical GeneticsPeer-reviewedSep 4, 2026

Hypertrophic cardiomyopathy: a genome wide association meta-analysis and polygenic risk score

Luís Rocha Lopes, Nay Aung, Stefan van Duijvenboden…·Perry M Elliot

This paper identifies novel genetic loci for hypertrophic cardiomyopathy through GWAS and develops a polygenic risk score associated with HCM diagnosis and outcomes.

DOI 10.1136/jmg-2026-111722

humanGWAS summary statistics

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BiophysicsbioRxivPreprintAug 5, 2023

Unlocking Predictive Power: A Machine Learning Tool Derived from In-Depth Analysis to Forecast the Impact of Missense Variants in Human Filamin C

Michael Nagy, Georg Mlynek, Július Košťan…·Kristina Djinović‐Carugo

The paper develops a machine learning model to predict the impact of missense variants in human filamin C, achieving high accuracy in distinguishing pathogenic from non-pathogenic variants.

3 citations

humanproteomics

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