Kevin Schaper

1.6k total citations
9 papers, 346 citations indexed

About

Kevin Schaper is a scholar working on Molecular Biology, Genetics and Obstetrics and Gynecology. According to data from OpenAlex, Kevin Schaper has authored 9 papers receiving a total of 346 indexed citations (citations by other indexed papers that have themselves been cited), including 8 papers in Molecular Biology, 3 papers in Genetics and 1 paper in Obstetrics and Gynecology. Recurrent topics in Kevin Schaper's work include Biomedical Text Mining and Ontologies (6 papers), Bioinformatics and Genomic Networks (4 papers) and Genomics and Phylogenetic Studies (3 papers). Kevin Schaper is often cited by papers focused on Biomedical Text Mining and Ontologies (6 papers), Bioinformatics and Genomic Networks (4 papers) and Genomics and Phylogenetic Studies (3 papers). Kevin Schaper collaborates with scholars based in United States and Italy. Kevin Schaper's co-authors include Douglas G. Howe, Sridhar Ramachandran, Sabrina Toro, Ryan Martin, Yvonne M. Bradford, Monte Westerfield, Anne Eagle, Leyla Ruzicka, Patrick Kalita and Sierra Moxon and has published in prestigious journals such as Nucleic Acids Research, Bioinformatics and International Journal of Medical Informatics.

In The Last Decade

Kevin Schaper

7 papers receiving 340 citations

Peers — A (Enhanced Table)

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

Name	h						Papers	Cites
Kevin Schaper United States	6	213	110	59	33	26	9	346
Patrick Kalita United States	6	212 1.0×	110 1.0×	58 1.0×	33 1.0×	26 1.0×	6	342
Anne Eagle United States	7	285 1.3×	146 1.3×	80 1.4×	47 1.4×	37 1.4×	7	456
Leyla Ruzicka United States	7	298 1.4×	149 1.4×	85 1.4×	48 1.5×	38 1.5×	11	473
Prita Mani United States	6	280 1.3×	56 0.5×	48 0.8×	19 0.6×	32 1.2×	6	353
Yusuke Nagao Japan	8	124 0.6×	122 1.1×	89 1.5×	55 1.7×	15 0.6×	11	278
Sierra Moxon United States	5	140 0.7×	71 0.6×	34 0.6×	20 0.6×	16 0.6×	5	238
Zsofia Digby United Kingdom	4	211 1.0×	53 0.5×	35 0.6×	31 0.9×	13 0.5×	7	281
Meghan Kelly United States	6	169 0.8×	79 0.7×	34 0.6×	42 1.3×	55 2.1×	6	324
Thibaut Hourlier United Kingdom	4	238 1.1×	62 0.6×	48 0.8×	15 0.5×	12 0.5×	4	336
Andreas Zaucker United Kingdom	9	190 0.9×	68 0.6×	51 0.9×	11 0.3×	10 0.4×	12	299

Countries citing papers authored by Kevin Schaper

Since Specialization

Citations

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

Fields of papers citing papers by Kevin Schaper

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Kevin Schaper

This figure shows the co-authorship network connecting the top 25 collaborators of Kevin Schaper. A scholar is included among the top collaborators of Kevin Schaper 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 Kevin Schaper. Kevin Schaper is excluded from the visualization to improve readability, since they are connected to all nodes in the network.

All Works

Sort: Min cites: Since: Top N: Style:

9 of 9 papers shown

1.

O’Neil, Shawn T., Brian M. Schilder, Kevin Schaper, et al.. (2025). monarchr: an R package for querying biomedical knowledge graphs. Bioinformatics. 41(10).

2.

Matentzoglu, Nicolas, et al.. (2024). Increased discoverability of rare disease datasets through knowledge graph integration. JAMIA Open. 8(1). ooaf001–ooaf001.

3.

Chan, Lauren, Elena Casiraghi, Justin Reese, et al.. (2024). Predicting nutrition and environmental factors associated with female reproductive disorders using a knowledge graph and random forests. International Journal of Medical Informatics. 187. 105461–105461. 4 indexed citations

4.

Howe, Douglas G., Sridhar Ramachandran, Yvonne M. Bradford, et al.. (2020). The Zebrafish Information Network: major gene page and home page updates. Nucleic Acids Research. 49(D1). D1058–D1064. 10 indexed citations

5.

Ruzicka, Leyla, Douglas G. Howe, Sridhar Ramachandran, et al.. (2018). The Zebrafish Information Network: new support for non-coding genes, richer Gene Ontology annotations and the Alliance of Genome Resources. Nucleic Acids Research. 47(D1). D867–D873. 98 indexed citations

6.

Howe, Douglas G., Yvonne M. Bradford, Anne Eagle, et al.. (2016). A scientist's guide for submitting data to ZFIN. Methods in cell biology. 135. 451–481. 6 indexed citations

7.

Bradford, Yvonne M., Sabrina Toro, Sridhar Ramachandran, et al.. (2016). Zebrafish Models of Human Disease: Gaining Insight into Human Disease at ZFIN. ILAR Journal. 58(1). 4–16. 113 indexed citations

8.

Howe, Douglas G., Yvonne M. Bradford, Anne Eagle, et al.. (2016). The Zebrafish Model Organism Database: new support for human disease models, mutation details, gene expression phenotypes and searching. Nucleic Acids Research. 45(D1). D758–D768. 62 indexed citations

9.

Ruzicka, Leyla, Yvonne M. Bradford, Ken Frazer, et al.. (2015). ZFIN, The zebrafish model organism database: Updates and new directions. genesis. 53(8). 498–509. 53 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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