Ramakanth Kavuluru

3.5k total citations
72 papers, 1.4k citations indexed

About

Ramakanth Kavuluru is a scholar working on Artificial Intelligence, Molecular Biology and Information Systems. According to data from OpenAlex, Ramakanth Kavuluru has authored 72 papers receiving a total of 1.4k indexed citations (citations by other indexed papers that have themselves been cited), including 48 papers in Artificial Intelligence, 32 papers in Molecular Biology and 7 papers in Information Systems. Recurrent topics in Ramakanth Kavuluru's work include Topic Modeling (31 papers), Biomedical Text Mining and Ontologies (31 papers) and Natural Language Processing Techniques (14 papers). Ramakanth Kavuluru is often cited by papers focused on Topic Modeling (31 papers), Biomedical Text Mining and Ontologies (31 papers) and Natural Language Processing Techniques (14 papers). Ramakanth Kavuluru collaborates with scholars based in United States, Australia and India. Ramakanth Kavuluru's co-authors include Anthony Rios, Tung Tran, Yuan Lü, Zhiyong Lu, Amit Sheth, Krishnaprasad Thirunarayan, Yifan Peng, Sifei Han, Amanuel Alambo and Jyotishman Pathak and has published in prestigious journals such as Journal of Clinical Oncology, SHILAP Revista de lepidopterología and Bioinformatics.

In The Last Decade

Ramakanth Kavuluru

68 papers receiving 1.4k citations

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
Ramakanth Kavuluru United States 21 803 443 187 121 102 72 1.4k
Trevor Cohen United States 27 904 1.1× 778 1.8× 144 0.8× 108 0.9× 81 0.8× 141 2.6k
Jingcheng Du United States 19 729 0.9× 414 0.9× 134 0.7× 69 0.6× 54 0.5× 63 1.5k
Angus Roberts United Kingdom 23 1.2k 1.5× 794 1.8× 185 1.0× 57 0.5× 199 2.0× 89 2.4k
Rui Zhang United States 20 691 0.9× 488 1.1× 58 0.3× 30 0.2× 63 0.6× 144 1.7k
Eiji Aramaki Japan 19 694 0.9× 306 0.7× 73 0.4× 78 0.6× 123 1.2× 153 1.6k
Brett R. South United States 21 1.6k 2.0× 1.2k 2.7× 115 0.6× 50 0.4× 75 0.7× 54 2.3k
Sumithra Velupillai Sweden 23 1.1k 1.4× 625 1.4× 450 2.4× 211 1.7× 61 0.6× 96 1.9k
Yang Xiang China 19 598 0.7× 313 0.7× 72 0.4× 32 0.3× 71 0.7× 61 1.2k
Abeed Sarker United States 27 1.4k 1.7× 746 1.7× 237 1.3× 83 0.7× 269 2.6× 123 3.0k
Fernando Martín-Sánchez Spain 22 274 0.3× 523 1.2× 55 0.3× 147 1.2× 92 0.9× 103 2.1k

Countries citing papers authored by Ramakanth Kavuluru

Since Specialization
Citations

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

Fields of papers citing papers by Ramakanth Kavuluru

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Ramakanth Kavuluru

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

All Works

20 of 20 papers shown
1.
Mutharaju, Raghava, et al.. (2024). Knowledge-Driven Cross-Document Relation Extraction. PubMed. 2024. 3787–3797.
2.
Mutharaju, Raghava, et al.. (2024). Revisiting Document-Level Relation Extraction with Context-Guided Link Prediction. Proceedings of the AAAI Conference on Artificial Intelligence. 38(16). 18327–18335.
3.
Hochheiser, Harry, Sean Finan, Zhou Yuan, et al.. (2023). DeepPhe-CR: Natural Language Processing Software Services for Cancer Registrar Case Abstraction. JCO Clinical Cancer Informatics. 7(7). e2300156–e2300156. 1 indexed citations
4.
Davarpanah, Mohammad Ali, et al.. (2023). Combination of spironolactone and sitagliptin improves clinical outcomes of outpatients with COVID-19: a prospective cohort study. Journal of Endocrinological Investigation. 47(1). 235–243. 3 indexed citations
5.
Wang, Liwei, Huan He, Andrew Wen, et al.. (2023). Acquisition of a Lexicon for Family History Information: Bidirectional Encoder Representations From Transformers–Assisted Sublanguage Analysis. JMIR Medical Informatics. 11. e48072–e48072. 1 indexed citations
6.
Madlock‐Brown, Charisse, Deborah Duran, Juan Espinoza, et al.. (2022). Social Determinants of Health Factors for Gene–Environment COVID‐19 Research: Challenges and Opportunities. SHILAP Revista de lepidopterología. 3(2). 2100056–2100056. 9 indexed citations
7.
Fleischer, Anne, et al.. (2021). Twitter, Telepractice, and the COVID-19 Pandemic: A Social Media Content Analysis. American Journal of Speech-Language Pathology. 30(6). 2561–2571. 8 indexed citations
8.
Kavuluru, Ramakanth, et al.. (2021). Therapeutic Claims in Cannabidiol (CBD) Marketing Messages on Twitter. 2021 IEEE International Conference on Bioinformatics and Biomedicine (BIBM). 2021. 3083–3088. 28 indexed citations
9.
Sarker, Abeed, Maksim Belousov, Kai Hakala, et al.. (2018). Data and systems for medication-related text classification and concept normalization from Twitter: insights from the Social Media Mining for Health (SMM4H)-2017 shared task. Journal of the American Medical Informatics Association. 25(10). 1274–1283. 61 indexed citations
10.
Rios, Anthony & Ramakanth Kavuluru. (2018). Few-Shot and Zero-Shot Multi-Label Learning for Structured Label Spaces. PubMed. 2018. 3132–3142. 110 indexed citations
11.
Kavuluru, Ramakanth, et al.. (2018). Document Retrieval for Biomedical Question Answering with Neural Sentence Matching. PubMed. 16. 194–201. 3 indexed citations
12.
Kavuluru, Ramakanth, et al.. (2017). Team UKNLP at TREC 2017 Precision Medicine Track: A Knowledge-Based IR System with Tuned Query-Time Boosting.. Text REtrieval Conference. 2 indexed citations
13.
Tran, Tung & Ramakanth Kavuluru. (2017). Predicting mental conditions based on “history of present illness” in psychiatric notes with deep neural networks. Journal of Biomedical Informatics. 75. S138–S148. 61 indexed citations
14.
Han, Sifei & Ramakanth Kavuluru. (2016). Exploratory Analysis of Marketing and Non-marketing E-cigarette Themes on Twitter. Lecture notes in computer science. 10047. 307–322. 13 indexed citations
15.
Cameron, Delroy, Ramakanth Kavuluru, Thomas C. Rindflesch, et al.. (2015). Context-driven automatic subgraph creation for literature-based discovery. Journal of Biomedical Informatics. 54. 141–157. 54 indexed citations
16.
17.
Kavuluru, Ramakanth & Yuan Lü. (2014). Leveraging output term co-occurrence frequencies and latent associations in predicting medical subject headings. Data & Knowledge Engineering. 94(B). 189–201. 12 indexed citations
18.
Kavuluru, Ramakanth, et al.. (2013). Unsupervised Medical Subject Heading Assignment Using Output Label Co-occurrence Statistics and Semantic Predications. Lecture notes in computer science. 7934. 176–188. 8 indexed citations
19.
Johnson, Todd R., et al.. (2013). Phrase Based Topic Modeling for Semantic Information Processing in Biomedicine. PubMed. 2013. 440–445. 10 indexed citations
20.
Kavuluru, Ramakanth & Daniel R. Harris. (2012). A Knowledge-Based Approach to Syntactic Disambiguation of Biomedical Noun Compounds. International Conference on Computational Linguistics. 559–568. 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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