Kent Shefchek

1.5k total citations
9 papers, 163 citations indexed

About

Kent Shefchek is a scholar working on Molecular Biology, Genetics and Artificial Intelligence. According to data from OpenAlex, Kent Shefchek has authored 9 papers receiving a total of 163 indexed citations (citations by other indexed papers that have themselves been cited), including 4 papers in Molecular Biology, 4 papers in Genetics and 2 papers in Artificial Intelligence. Recurrent topics in Kent Shefchek's work include Biomedical Text Mining and Ontologies (4 papers), Genomics and Rare Diseases (3 papers) and Bioinformatics and Genomic Networks (2 papers). Kent Shefchek is often cited by papers focused on Biomedical Text Mining and Ontologies (4 papers), Genomics and Rare Diseases (3 papers) and Bioinformatics and Genomic Networks (2 papers). Kent Shefchek collaborates with scholars based in United States, United Kingdom and Germany. Kent Shefchek's co-authors include Silke Ruppel, Katja Witzel, Suvarna Nadendla, Melissa Haendel, Peter N. Robinson, Chris Mungall, Mónica Muñoz-Torres, Luca Cappelletti, Nomi L. Harris and Deepak Unni and has published in prestigious journals such as Journal of Bacteriology, Journal of Medical Internet Research and Human Mutation.

In The Last Decade

Kent Shefchek

9 papers receiving 159 citations

Peers — A (Enhanced Table)

Peers by citation overlap · career bar shows stage (early→late) cites · hero ref

Name	h						Papers	Cites
Kent Shefchek United States	6	81	40	36	18	17	9	163
George Armstrong United States	8	106 1.3×	13 0.3×	14 0.4×	20 1.1×	16 0.9×	14	232
Anna Paola Carrieri United Kingdom	9	230 2.8×	23 0.6×	41 1.1×	24 1.3×	32 1.9×	18	382
J. Harry Caufield United States	13	223 2.8×	25 0.6×	74 2.1×	23 1.3×	37 2.2×	31	360
Pushkala Jayaraman United States	10	154 1.9×	11 0.3×	56 1.6×	31 1.7×	47 2.8×	22	322
Ankita Singh India	10	250 3.1×	27 0.7×	9 0.3×	13 0.7×	26 1.5×	23	338
Alexander Liu United States	8	166 2.0×	60 1.5×	29 0.8×	47 2.6×	30 1.8×	17	324
Hamid D. Ismail United States	12	169 2.1×	8 0.2×	9 0.3×	7 0.4×	12 0.7×	29	290
Minseung Kim United States	10	260 3.2×	9 0.2×	24 0.7×	10 0.6×	73 4.3×	16	394
Kristen L. Beck United States	8	91 1.1×	7 0.2×	23 0.6×	33 1.8×	23 1.4×	14	232
Ulykbek Kairov Kazakhstan	9	144 1.8×	18 0.5×	21 0.6×	26 1.4×	28 1.6×	47	284

Countries citing papers authored by Kent Shefchek

Since Specialization

Citations

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

Fields of papers citing papers by Kent Shefchek

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Kent Shefchek

This figure shows the co-authorship network connecting the top 25 collaborators of Kent Shefchek. A scholar is included among the top collaborators of Kent Shefchek based on the total number of citations received by their joint publications. Widths of edges represent the number of papers authors have co-authored together. Node borders signify the number of papers an author published with Kent Shefchek. Kent Shefchek is excluded from the visualization to improve readability, since they are connected to all nodes in the network.

All Works

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9 of 9 papers shown

Jacobsen, Julius O.B., Christèle du Souich, Kent Shefchek, et al.. (2022). The Clinical Variant Analysis Tool: Analyzing the evidence supporting reported genomic variation in clinical practice. Genetics in Medicine. 24(7). 1512–1522. 3 indexed citations

Li, Jianqiao, Margaret A. Hojlo, Kent Shefchek, et al.. (2021). Underrepresentation of Phenotypic Variability of 16p13.11 Microduplication Syndrome Assessed With an Online Self-Phenotyping Tool (Phenotypr): Cohort Study. Journal of Medical Internet Research. 23(3). e21023–e21023. 3 indexed citations

Reese, Justin, Deepak Unni, Tiffany J. Callahan, et al.. (2020). KG-COVID-19: A Framework to Produce Customized Knowledge Graphs for COVID-19 Response. Patterns. 2(1). 100155–100155. 54 indexed citations

Gourdine, Jean-Philippe F., Matthew Brush, Nicole Vasilevsky, et al.. (2019). Representing glycophenotypes: semantic unification of glycobiology resources for disease discovery. Database. 2019. 3 indexed citations

Brush, Matthew, Kent Shefchek, & Melissa Haendel. (2016). SEPIO: A semantic model for the integration and analysis of scientific evidence. 1747. 6 indexed citations

Mungall, Chris, Nicole Washington, Damian Smedley, et al.. (2015). Use of Model Organism and Disease Databases to Support Matchmaking for Human Disease Gene Discovery. Human Mutation. 36(10). 979–984. 19 indexed citations

Blanchard, Thomas G., Steven J. Czinn, Pelayo Correa, et al.. (2013). Genome sequences of 65Helicobacter pyloristrains isolated from asymptomatic individuals and patients with gastric cancer, peptic ulcer disease, or gastritis. Pathogens and Disease. 68(2). 39–43. 14 indexed citations

Witzel, Katja, et al.. (2012). Genome Sequence of Enterobacter radicincitans DSM16656 ^T , a Plant Growth-Promoting Endophyte. Journal of Bacteriology. 194(19). 5469–5469. 46 indexed citations

Tettelin, Hervé, Elizabeth P. Sampaio, Sean C. Daugherty, et al.. (2012). Genomic Insights into the Emerging Human Pathogen Mycobacterium massiliense. Journal of Bacteriology. 194(19). 5450–5450. 15 indexed citations

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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