Kalpana Raja

1.6k total citations
38 papers, 528 citations indexed

About

Kalpana Raja is a scholar working on Molecular Biology, Artificial Intelligence and Computational Theory and Mathematics. According to data from OpenAlex, Kalpana Raja has authored 38 papers receiving a total of 528 indexed citations (citations by other indexed papers that have themselves been cited), including 27 papers in Molecular Biology, 12 papers in Artificial Intelligence and 5 papers in Computational Theory and Mathematics. Recurrent topics in Kalpana Raja's work include Biomedical Text Mining and Ontologies (18 papers), Topic Modeling (9 papers) and Bioinformatics and Genomic Networks (6 papers). Kalpana Raja is often cited by papers focused on Biomedical Text Mining and Ontologies (18 papers), Topic Modeling (9 papers) and Bioinformatics and Genomic Networks (6 papers). Kalpana Raja collaborates with scholars based in United States, India and Malaysia. Kalpana Raja's co-authors include Lam C. Tsoi, Matthew T. Patrick, Jeyakumar Natarajan, Suresh Subramani, James T. Elder, Siddhartha Jonnalagadda, Jóhann E. Guðjónsson, Guilherme Del Fiol, Marcelo Fiszman and Mohammad Amin Morid and has published in prestigious journals such as Bioinformatics, The Journal of Immunology and Scientific Reports.

In The Last Decade

Kalpana Raja

35 papers receiving 505 citations

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
Kalpana Raja United States 12 203 148 75 68 65 38 528
Simon Jupp United Kingdom 15 604 3.0× 219 1.5× 38 0.5× 45 0.7× 25 0.4× 45 1.0k
Emily Jefferson United Kingdom 10 423 2.1× 57 0.4× 35 0.5× 50 0.7× 14 0.2× 33 761
Xiaoyang Ruan United States 11 151 0.7× 60 0.4× 19 0.3× 49 0.7× 24 0.4× 20 399
Malika Smaïl‐Tabbone France 14 330 1.6× 149 1.0× 25 0.3× 125 1.8× 30 0.5× 49 947
Weisong Liu United States 16 419 2.1× 286 1.9× 25 0.3× 52 0.8× 8 0.1× 45 880
Ravi Shankar United States 12 416 2.0× 96 0.6× 82 1.1× 12 0.2× 28 0.4× 41 905
Katharine Bisordi United States 6 387 1.9× 85 0.6× 14 0.2× 60 0.9× 12 0.2× 9 532
Mark Minie United States 6 246 1.2× 17 0.1× 52 0.7× 37 0.5× 71 1.1× 7 547
Naiem T. Issa United States 11 323 1.6× 31 0.2× 22 0.3× 194 2.9× 17 0.3× 49 749
Lauren B. Becnel United States 10 126 0.6× 58 0.4× 76 1.0× 13 0.2× 6 0.1× 18 437

Countries citing papers authored by Kalpana Raja

Since Specialization
Citations

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

Fields of papers citing papers by Kalpana Raja

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Kalpana Raja

This figure shows the co-authorship network connecting the top 25 collaborators of Kalpana Raja. A scholar is included among the top collaborators of Kalpana Raja 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 Kalpana Raja. Kalpana Raja 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.
Keloth, Vipina K., Yan Hu, Qianqian Xie, et al.. (2024). Advancing entity recognition in biomedicine via instruction tuning of large language models. Bioinformatics. 40(4). 40 indexed citations
2.
3.
Raja, Kalpana, et al.. (2024). Prokaryotic cell membrane‑based protein technologies (Review). World Academy of Sciences Journal. 6(2). 3 indexed citations
4.
Millikin, Robert J., Kalpana Raja, I. Ross, et al.. (2023). Serial KinderMiner (SKiM) discovers and annotates biomedical knowledge using co-occurrence and transformer models. BMC Bioinformatics. 24(1). 412–412. 2 indexed citations
5.
Hu, Yan, Vipina K. Keloth, Kalpana Raja, Yong Chen, & Hua Xu. (2023). Towards precise PICO extraction from abstracts of randomized controlled trials using a section-specific learning approach. Bioinformatics. 39(9). 10 indexed citations
6.
Raja, Kalpana, et al.. (2022). A Simple Computational Approach to Identify Potential Drugs for Multiple Sclerosis and Cognitive Disorders from Expert Curated Resources. Methods in molecular biology. 2496. 111–121. 1 indexed citations
7.
8.
Raja, Kalpana, et al.. (2022). Extracting Significant Comorbid Diseases from MeSH Index of PubMed. Methods in molecular biology. 2496. 283–299. 1 indexed citations
9.
Baharuldin, Mohamad Taufik Hidayat, et al.. (2022). A Text Mining Protocol for Extracting Drug–Drug Interaction and Adverse Drug Reactions Specific to Patient Population, Pharmacokinetics, Pharmacodynamics, and Disease. Methods in molecular biology. 2496. 259–282. 4 indexed citations
10.
Raja, Kalpana, et al.. (2022). A Text Mining Protocol for Predicting Drug–Drug Interaction and Adverse Drug Reactions from PubMed Articles. Methods in molecular biology. 2496. 237–258. 4 indexed citations
11.
Chipurupalli, Sandhya, et al.. (2020). Caenorhabditis elegans as a possible model to screen anti-Alzheimer's therapeutics. Journal of Pharmacological and Toxicological Methods. 106. 106932–106932. 19 indexed citations
12.
Patrick, Matthew T., Philip E. Stuart, Kalpana Raja, et al.. (2019). Integrative Approach to Reveal Cell Type Specificity and Gene Candidates for Psoriatic Arthritis Outside the MHC. Frontiers in Genetics. 10. 304–304. 5 indexed citations
13.
Tsoi, Lam C., Grace A. Hile, Céline C. Berthier, et al.. (2019). Hypersensitive IFN Responses in Lupus Keratinocytes Reveal Key Mechanistic Determinants in Cutaneous Lupus. The Journal of Immunology. 202(7). 2121–2130. 51 indexed citations
14.
Raja, Kalpana, et al.. (2018). Molecular dynamics simulation analysis of conessine against multi drug resistant Serratia marcescens. Infection Genetics and Evolution. 67. 101–111. 5 indexed citations
15.
Raja, Kalpana & Jeyakumar Natarajan. (2018). Mining protein phosphorylation information from biomedical literature using NLP parsing and Support Vector Machines. Computer Methods and Programs in Biomedicine. 160. 57–64. 7 indexed citations
16.
Raja, Kalpana, Matthew T. Patrick, James T. Elder, & Lam C. Tsoi. (2017). Machine learning workflow to enhance predictions of Adverse Drug Reactions (ADRs) through drug-gene interactions: application to drugs for cutaneous diseases. Scientific Reports. 7(1). 3690–3690. 54 indexed citations
17.
Morid, Mohammad Amin, Marcelo Fiszman, Kalpana Raja, Siddhartha Jonnalagadda, & Guilherme Del Fiol. (2016). Classification of clinically useful sentences in clinical evidence resources. Journal of Biomedical Informatics. 60. 14–22. 24 indexed citations
18.
Stöber, Jakob, Bret S.E. Heale, Guilherme Del Fiol, et al.. (2015). Concept based Information Retrieval for Clinical Case Summaries.. Text REtrieval Conference. 1 indexed citations
19.
Milward, David, et al.. (2015). Agile text mining for the 2014 i2b2/UTHealth Cardiac risk factors challenge. Journal of Biomedical Informatics. 58. S120–S127. 32 indexed citations
20.
Raja, Kalpana, et al.. (1998). Unusual clinical variants of cutaneous leishmaniasis in Pakistan. British Journal of Dermatology. 139(1). 111–113. 67 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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