Sunil Mohan

422 total citations
9 papers, 183 citations indexed

About

Sunil Mohan is a scholar working on Artificial Intelligence, Molecular Biology and Information Systems. According to data from OpenAlex, Sunil Mohan has authored 9 papers receiving a total of 183 indexed citations (citations by other indexed papers that have themselves been cited), including 7 papers in Artificial Intelligence, 5 papers in Molecular Biology and 2 papers in Information Systems. Recurrent topics in Sunil Mohan's work include Topic Modeling (7 papers), Biomedical Text Mining and Ontologies (5 papers) and Natural Language Processing Techniques (3 papers). Sunil Mohan is often cited by papers focused on Topic Modeling (7 papers), Biomedical Text Mining and Ontologies (5 papers) and Natural Language Processing Techniques (3 papers). Sunil Mohan collaborates with scholars based in United States. Sunil Mohan's co-authors include Nicolas Fiorini, Zhiyong Lu, Donghui Li, Sun Kim, Jun Yu, Weng‐Keen Wong, Kathi Canese, Won Bae Kim, Maxim Osipov and Vadim Miller and has published in prestigious journals such as PLoS Biology, Machine Learning and arXiv (Cornell University).

In The Last Decade

Sunil Mohan

9 papers receiving 171 citations

Peers — A (Enhanced Table)

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

Name	h						Papers	Cites
Sunil Mohan United States	7	120	73	35	19	13	9	183
Rebecka Weegar Sweden	8	164 1.4×	82 1.1×	22 0.6×	8 0.4×	2 0.2×	23	259
Oana Frunza Canada	8	161 1.3×	95 1.3×	36 1.0×	7 0.4×	58 4.5×	13	238
O Bodenreider United States	6	182 1.5×	202 2.8×	24 0.7×	5 0.3×	4 0.3×	8	264
Artem Shelmanov Russia	9	189 1.6×	22 0.3×	20 0.6×	33 1.7×	2 0.2×	32	254
Qiubin Yu China	8	184 1.5×	99 1.4×	14 0.4×	11 0.6×	5 0.4×	10	251
Charles Jochim Ireland	8	174 1.4×	38 0.5×	33 0.9×	13 0.7×	16 1.2×	25	197
Monica Lestari Paramita United Kingdom	8	149 1.2×	21 0.3×	110 3.1×	37 1.9×	12 0.9×	19	246
Shikhar Vashishth India	7	207 1.7×	49 0.7×	13 0.4×	20 1.1×		13	229
J E Rogers United Kingdom	9	184 1.5×	202 2.8×	23 0.7×	8 0.4×	3 0.2×	11	280
Q. Liu China	3	188 1.6×	14 0.2×	42 1.2×	20 1.1×	10 0.8×	11	237

Countries citing papers authored by Sunil Mohan

Since Specialization

Citations

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

Fields of papers citing papers by Sunil Mohan

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Sunil Mohan

This figure shows the co-authorship network connecting the top 25 collaborators of Sunil Mohan. A scholar is included among the top collaborators of Sunil Mohan 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 Sunil Mohan. Sunil Mohan 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.

Mohan, Sunil, et al.. (2021). Clustering-based Inference for Biomedical Entity Linking. arXiv (Cornell University). 2598–2608. 3 indexed citations

2.

Chang, Haw-Shiuan, et al.. (2020). Using error decay prediction to overcome practical issues of deep active learning for named entity recognition. Machine Learning. 109(9-10). 1749–1778. 8 indexed citations

3.

Chang, Haw-Shiuan, et al.. (2019). Overcoming Practical Issues of Deep Active Learning and its Applications on Named Entity Recognition.. arXiv (Cornell University). 1 indexed citations

4.

Mohan, Sunil & Donghui Li. (2019). MedMentions: A Large Biomedical Corpus Annotated with UMLS Concepts. arXiv (Cornell University). 20 indexed citations

5.

Fiorini, Nicolas, Kathi Canese, Won Bae Kim, et al.. (2018). Best Match: New relevance search for PubMed. PLoS Biology. 16(8). e2005343–e2005343. 76 indexed citations

6.

Mohan, Sunil, Nicolas Fiorini, Sun Kim, & Zhiyong Lu. (2018). A Fast Deep Learning Model for Textual Relevance in Biomedical Information Retrieval. 77–86. 23 indexed citations

7.

Mohan, Sunil, Nicolas Fiorini, Sun Kim, & Zhiyong Lu. (2017). Deep Learning for Biomedical Information Retrieval: Learning Textual Relevance from Click Logs. 222–231. 16 indexed citations

8.

Yu, Jun, et al.. (2014). Latent dirichlet allocation based diversified retrieval for e-commerce search. 463–472. 30 indexed citations

9.

Muscettola, Nicola, et al.. (1998). Automating Mission Scheduling for Space-Based Observatories. NASA Technical Reports Server (NASA). 79. 148. 6 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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