Seth Carbon

13.7k total citations · 1 hit paper
16 papers, 1.8k citations indexed

About

Seth Carbon is a scholar working on Molecular Biology, Artificial Intelligence and Sociology and Political Science. According to data from OpenAlex, Seth Carbon has authored 16 papers receiving a total of 1.8k indexed citations (citations by other indexed papers that have themselves been cited), including 14 papers in Molecular Biology, 5 papers in Artificial Intelligence and 1 paper in Sociology and Political Science. Recurrent topics in Seth Carbon's work include Biomedical Text Mining and Ontologies (11 papers), Bioinformatics and Genomic Networks (11 papers) and Semantic Web and Ontologies (3 papers). Seth Carbon is often cited by papers focused on Biomedical Text Mining and Ontologies (11 papers), Bioinformatics and Genomic Networks (11 papers) and Semantic Web and Ontologies (3 papers). Seth Carbon collaborates with scholars based in United States, United Kingdom and Switzerland. Seth Carbon's co-authors include Chris Mungall, Suzanna Lewis, Shengqiang Shu, Amelia Ireland, Brad Marshall, Mónica Muñoz-Torres, Kimberly Van Auken, David P. Hill, Benjamin M. Good and James P. Balhoff and has published in prestigious journals such as Nucleic Acids Research, Nature Genetics and Bioinformatics.

In The Last Decade

Seth Carbon

16 papers receiving 1.8k citations

Hit Papers

align trajectories

What are hit papers?

Hit papers significantly outperform the citation benchmark for their cohort. A paper qualifies if it has ≥500 total citations, achieves ≥1.5× the top-1% citation threshold for papers in the same subfield and year (this is the minimum needed to enter the top 1%, not the average within it), or reaches the top citation threshold in at least one of its specific research topics.

AmiGO: online access to ontology and annotation data

2008 1.4k citations Seth Carbon, Amelia Ireland et al. Bioinformatics profile →

Peers — A (Enhanced Table)

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

Name	h						Papers	Cites
Seth Carbon United States	9	1.2k	271	270	166	132	16	1.8k
Claudia Angelini Italy	27	1.2k 1.0×	306 1.1×	247 0.9×	197 1.2×	144 1.1×	88	1.9k
Daniel Barrell United Kingdom	12	1.6k 1.3×	230 0.8×	255 0.9×	108 0.7×	96 0.7×	14	2.1k
Mónica Chagoyen Spain	21	1.5k 1.2×	151 0.6×	291 1.1×	186 1.1×	95 0.7×	56	2.2k
Aurélien Naldi France	18	1.9k 1.5×	227 0.8×	249 0.9×	136 0.8×	193 1.5×	42	2.5k
Brad Marshall United States	4	954 0.8×	209 0.8×	212 0.8×	146 0.9×	119 0.9×	4	1.4k
Ignacio Medina Spain	21	1.5k 1.2×	382 1.4×	273 1.0×	221 1.3×	123 0.9×	52	2.3k
Andrew Yates United Kingdom	14	1.1k 0.9×	317 1.2×	321 1.2×	126 0.8×	74 0.6×	30	1.6k
Uğis Sarkans United Kingdom	18	1.9k 1.5×	254 0.9×	158 0.6×	289 1.7×	126 1.0×	29	2.5k
Mary Mangan United States	10	1.1k 0.9×	247 0.9×	167 0.6×	135 0.8×	93 0.7×	13	1.6k
Justin Chu Canada	17	1.3k 1.0×	286 1.1×	356 1.3×	108 0.7×	79 0.6×	33	1.8k

Countries citing papers authored by Seth Carbon

Since Specialization

Citations

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

Fields of papers citing papers by Seth Carbon

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Seth Carbon

This figure shows the co-authorship network connecting the top 25 collaborators of Seth Carbon. A scholar is included among the top collaborators of Seth Carbon 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 Seth Carbon. Seth Carbon 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:

16 of 16 papers shown

Good, Benjamin M., Kimberly Van Auken, David P. Hill, et al.. (2021). Reactome and the Gene Ontology: digital convergence of data resources. Bioinformatics. 37(19). 3343–3348. 12 indexed citations

Wood, Valerie, Seth Carbon, Midori A. Harris, et al.. (2020). Term Matrix: a novel Gene Ontology annotation quality control system based on ontology term co-annotation patterns. Open Biology. 10(9). 200149–200149. 6 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

Reese, Justin, Deepak Unni, Tiffany J. Callahan, et al.. (2020). KG-COVID-19: A Framework to Produce Customized Knowledge Graphs for COVID-19 Response. SSRN Electronic Journal. 2 indexed citations

Thomas, Paul D., David P. Hill, Huaiyu Mi, et al.. (2019). Gene Ontology Causal Activity Modeling (GO-CAM) moves beyond GO annotations to structured descriptions of biological functions and systems. Nature Genetics. 51(10). 1429–1433. 67 indexed citations

Carbon, Seth, et al.. (2019). An analysis and metric of reusable data licensing practices for biomedical resources. PLoS ONE. 14(3). e0213090–e0213090. 12 indexed citations

Balhoff, James P., Benjamin M. Good, Seth Carbon, & Chris Mungall. (2018). Arachne: an OWL RL reasoner applied to Gene Ontology Causal Activity Models (and beyond). eScholarship (California Digital Library). 1 indexed citations

Balhoff, James P., Benjamin M. Good, Seth Carbon, & Chris Mungall. (2018). Arachne: an OWL RL reasoner applied to gene ontology causal activity models (and beyond). Faculty of 1000 Research Ltd. 7. 3 indexed citations

Cooper, Laurel, Marie‐Angélique Laporte, Justin Elser, et al.. (2017). The Planteome database: an integrated resource for reference ontologies, plant genomics and phenomics. Nucleic Acids Research. 46(D1). D1168–D1180. 114 indexed citations

10.

Jaiswal, Pankaj, Laurel Cooper, Justin Elser, et al.. (2016). Planteome: A resource for Common Reference Ontologies and Applications for Plant Biology. CGSPace A Repository of Agricultural Research Outputs (Consultative Group for International Agricultural Research). 2 indexed citations

11.

Muñoz-Torres, Mónica & Seth Carbon. (2016). Get GO! Retrieving GO Data Using AmiGO, QuickGO, API, Files, and Tools. Methods in molecular biology. 1446. 149–160. 22 indexed citations

12.

Cooper, Laurel, Justin Elser, Justin Preece, et al.. (2016). The Planteome project. University of Birmingham Research Portal (University of Birmingham). 1 indexed citations

13.

Huntley, Rachael P., Midori A. Harris, Yasmin Alam-Faruque, et al.. (2014). A method for increasing expressivity of Gene Ontology annotations using a compositional approach. BMC Bioinformatics. 15(1). 155–155. 67 indexed citations

14.

Contrino, Sergio, Richard Smith, Daniela Butano, et al.. (2011). modMine: flexible access to modENCODE data. Nucleic Acids Research. 40(D1). D1082–D1088. 91 indexed citations

15.

Carbon, Seth, Amelia Ireland, Chris Mungall, et al.. (2008). AmiGO: online access to ontology and annotation data. Bioinformatics. 25(2). 288–289. 1370 indexed citations breakdown →

16.

Carbon, Seth, et al.. (1983). Effect of interferon on Qa-1 expression.. PubMed. 36(3). 352–3. 1 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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