Matthew E. Mort
Impact in
- Genetics top 0.2%
- Genomics and Rare Diseases
- Genetic Associations and Epidemiology
- Genomic variations and chromosomal abnormalities
- Molecular Biology top 1%
- RNA and protein synthesis mechanisms
- Genomics and Phylogenetic Studies
- RNA modifications and cancer
- RNA Research and Splicing
Papers in
- Genetics 47
- Genomics and Rare Diseases 42
- Genomic variations and chromosomal abnormalities 12
- Genetic Associations and Epidemiology 11
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- RNA and protein synthesis mechanisms 21
- RNA modifications and cancer 18
- Genomics and Phylogenetic Studies 14
- RNA Research and Splicing 14
- Bioinformatics and Genomic Networks 10
- Co-authors
- D.N. Cooper (69 shared papers)Peter D. Stenson (20 shared papers)Edward V. Ball (16 shared papers)Andrew D. Phillips (10 shared papers)Nick S. T. Thomas (4 shared papers)Katy Shaw (3 shared papers)Michael Krawczak (6 shared papers)Sean D. Mooney (12 shared papers)
- Journals
- Human Mutation (18 papers)Bioinformatics (7 papers)Human Genetics (6 papers)Human Genomics (6 papers)Genome biology (5 papers)
- Partner nations
- United KingdomUnited StatesChina
In The Last Decade
Matthew E. Mort
88 papers receiving 12.2k citations
Matthew E. Mort's Hit Papers
Peers
Comparison fields: 5 of 165
- Genetics 4.7k
- Molecular Biology 6.6k
- Cancer Research 1.0k
- Clinical Biochemistry 317
- Genetics 417
Countries citing papers authored by Matthew E. Mort
This map shows the geographic impact of Matthew E. Mort's research. It shows the number of citations coming from papers published by authors working in each country. You can also color the map by specialization and compare the number of citations received by Matthew E. Mort with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Matthew E. Mort more than expected).
Fields of papers citing papers by Matthew E. Mort
This network shows the impact of papers produced by Matthew E. Mort. Nodes represent research fields, and links connect fields that are likely to share authors. Colored nodes show fields that tend to cite the papers produced by Matthew E. Mort. The network helps show where Matthew E. Mort may publish in the future.
Co-authors
The 25 scholars most cited alongside Matthew E. Mort, linked wherever they have co-authored with each other. Click a name or a connecting line to browse the papers they share.
All Works
Showing the 20 most-cited of 90 papers — load more, or switch the sort, to bring in the rest.
| # | Work | ||
|---|---|---|---|
| 1 | Human Gene Mutation Database (HGMD®): 2003 update Hit paper breakdown → | 2003 | 1498 |
| 2 | The Human Gene Mutation Database: building a comprehensive mutation repository for clinical and molecular genetics, diagnostic testing and personalized genomic medicine Hit paper breakdown → | 2013 | 1095 |
| 3 | The Human Gene Mutation Database: towards a comprehensive repository of inherited mutation data for medical research, genetic diagnosis and next-generation sequencing studies Hit paper breakdown → | 2017 | 1065 |
| 4 | The Human Gene Mutation Database: 2008 update Hit paper breakdown → | 2009 | 710 |
| 5 | Automated inference of molecular mechanisms of disease from amino acid substitutions Hit paper breakdown → | 2009 | 668 |
| 6 | Insights into hominid evolution from the gorilla genome sequence Hit paper breakdown → | 2012 | 534 |
| 7 | Gene expression across mammalian organ development Hit paper breakdown → | 2019 | 519 |
| 8 | An integrative approach to predicting the functional effects of non-coding and coding sequence variation Hit paper breakdown → | 2015 | 510 |
| 9 | The Human Gene Mutation Database (HGMD®): optimizing its use in a clinical diagnostic or research setting Hit paper breakdown → | 2020 | 508 |
| 10 | Inferring the molecular and phenotypic impact of amino acid variants with MutPred2 Hit paper breakdown → | 2020 | 493 |
| 11 | Demographic history and rare allele sharing among human populations Hit paper breakdown → | 2011 | 475 |
| 12 | 2010 | 416 | |
| 13 | 2008 | 383 | |
| 14 | FATHMM-XF: accurate prediction of pathogenic point mutations via extended features Hit paper breakdown → | 2017 | 324 |
| 15 | 2006 | 294 | |
| 16 | 2012 | 217 | |
| 17 | 2012 | 190 | |
| 18 | 2014 | 157 | |
| 19 | 2010 | 142 | |
| 20 | 2009 | 129 |
About Matthew E. Mort
Matthew E. Mort is a scholar working on Genetics, Molecular Biology, Cancer Research, Cellular and Molecular Neuroscience and Pathology and Forensic Medicine, having authored 90 papers that have together received 12.4k indexed citations. Recurring topics across this work include Genomics and Rare Diseases (42 papers), RNA and protein synthesis mechanisms (21 papers), RNA modifications and cancer (18 papers), Genomics and Phylogenetic Studies (14 papers), RNA Research and Splicing (14 papers), Genomic variations and chromosomal abnormalities (12 papers), Genetic Associations and Epidemiology (11 papers) and Bioinformatics and Genomic Networks (10 papers). The work is most often cited by research in Genetics (4.7k citations), Molecular Biology (6.6k citations), Cancer Research (1.0k citations), Clinical Biochemistry (317 citations) and Genetics (417 citations). Matthew E. Mort has collaborated with scholars based in United Kingdom, United States and China. Frequent co-authors include D.N. Cooper, Peter D. Stenson, Edward V. Ball, Andrew D. Phillips, Nick S. T. Thomas, Katy Shaw, Michael Krawczak, Sean D. Mooney, Predrag Radivojac and Shaun S. Abeysinghe. Their work appears in journals such as Human Mutation, Bioinformatics, Human Genetics, Human Genomics and Genome biology.
Rankless uses publication and citation data sourced from OpenAlex, an open and comprehensive bibliographic database. While OpenAlex provides broad and valuable coverage of the global research landscape, it—like all bibliographic datasets—has inherent limitations. These include incomplete records, variations in author disambiguation, differences in journal indexing, and delays in data updates. As a result, some metrics and network relationships displayed in Rankless may not fully capture the entirety of a scholar's output or impact.