Thomas Colthurst

2.4k citations
10 papers · 1.1k · 1 hit paper · h-index 7

Impact in

Papers in

Thomas Colthurst

10 papers receiving 1.0k citations

Thomas Colthurst's Hit Papers

A universal SNP and small-indel variant caller using deep neural networks 2018 · 757 citations
7570+2+5Years since publication250500750

Peers

Thomas Colthurst
Comparison fields: 5 of 103
  • Health Informatics 24
  • Signal Processing 182
  • Artificial Intelligence 365
  • Genetics 313
  • Cancer Research 147
Replace Yannis Assael with:
Yannis Assael United Kingdom
Il‐Youp Kwak South Korea
Sean Whalen United States
Serge Gueroussov Canada
Sayed Mohammad Ebrahim Sahraeian United States
Jörg Hakenberg United States
Patrick Erñst United States
Alexander Ku United States
Minghua Deng China
Andreas Mitterecker Austria
Thomas Colthurst relative to Yannis Assael United Kingdom Yannis Assael's profile →
Citations per field
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Citations per year

Countries citing papers authored by Thomas Colthurst

Since Specialization
Citations

This map shows the geographic impact of Thomas Colthurst'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 Thomas Colthurst with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Thomas Colthurst more than expected).

Fields of papers citing papers by Thomas Colthurst

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Thomas Colthurst. 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 Thomas Colthurst. The network helps show where Thomas Colthurst may publish in the future.

Co-authors

The 25 scholars most cited alongside Thomas Colthurst, linked wherever they have co-authored with each other. Click a name or a connecting line to browse the papers they share.

Border = papers with Thomas Colthurst Line = papers co-authored together Thomas Colthurst links everyone, so they are left out of the graph.

All Works

10 of 10 papers shown
#Work
1
A universal SNP and small-indel variant caller using deep neural networks
Hit paper breakdown →
2018757
2 2007191
3 200531
4 200627
5 199827
6 202218
7 20026
8
BBN CTS English System
20036
9 20004
10 20074

About Thomas Colthurst

Thomas Colthurst is a scholar working on Signal Processing, Artificial Intelligence, Computer Vision and Pattern Recognition, Genetics and Molecular Biology, having authored 10 papers that have together received 1.1k indexed citations. Recurring topics across this work include Speech and Audio Processing (6 papers), Speech Recognition and Synthesis (6 papers), Advanced Data Compression Techniques (4 papers), Genetic Mapping and Diversity in Plants and Animals (1 paper), Genetic Associations and Epidemiology (1 paper), Genetic and phenotypic traits in livestock (1 paper), Genomics and Rare Diseases (1 paper) and Natural Language Processing Techniques (1 paper). The work is most often cited by research in Health Informatics (24 citations), Signal Processing (182 citations), Artificial Intelligence (365 citations), Genetics (313 citations) and Cancer Research (147 citations). Thomas Colthurst has collaborated with scholars based in United States and France. Frequent co-authors include Cory Y. McLean, Nam V. Nguyen, Sam Gross, David H. Alexander, Scott Schwartz, Alexander Ku, Ryan Poplin, Pi-Chuan Chang, Pegah Tootoonchi Afshar and Mark A. DePristo. Their work appears in journals such as IEEE Transactions on Audio Speech and Language Processing, Nature Communications, Nature Biotechnology, Conference of the International Speech Communication Association and IEEE International Conference on Acoustics Speech and Signal Processing.

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.

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