Reagon Karki

525 total citations
18 papers, 292 citations indexed

About

Reagon Karki is a scholar working on Molecular Biology, Artificial Intelligence and Computational Theory and Mathematics. According to data from OpenAlex, Reagon Karki has authored 18 papers receiving a total of 292 indexed citations (citations by other indexed papers that have themselves been cited), including 14 papers in Molecular Biology, 6 papers in Artificial Intelligence and 5 papers in Computational Theory and Mathematics. Recurrent topics in Reagon Karki's work include Bioinformatics and Genomic Networks (9 papers), Biomedical Text Mining and Ontologies (5 papers) and Computational Drug Discovery Methods (5 papers). Reagon Karki is often cited by papers focused on Bioinformatics and Genomic Networks (9 papers), Biomedical Text Mining and Ontologies (5 papers) and Computational Drug Discovery Methods (5 papers). Reagon Karki collaborates with scholars based in Germany, Netherlands and Italy. Reagon Karki's co-authors include Martin Hofmann‐Apitius, Alpha Tom Kodamullil, Yojana Gadiya, Daniel Domingo‐Fernándéz, Tamara Raschka, Christian Ebeling, Shounak Baksi, Bruce Schultz, Sumit Madan and Holger Fröhlich and has published in prestigious journals such as SHILAP Revista de lepidopterología, Bioinformatics and Scientific Reports.

In The Last Decade

Reagon Karki

16 papers receiving 289 citations

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
Reagon Karki Germany 10 151 97 51 44 23 18 292
Yejin Kim United States 10 127 0.8× 89 0.9× 29 0.6× 106 2.4× 29 1.3× 31 373
Humayan Kabir Rana Bangladesh 13 172 1.1× 40 0.4× 23 0.5× 39 0.9× 20 0.9× 33 413
Alpha Tom Kodamullil Germany 15 348 2.3× 129 1.3× 123 2.4× 108 2.5× 22 1.0× 39 583
Shounak Baksi United States 10 164 1.1× 51 0.5× 41 0.8× 19 0.4× 29 1.3× 16 349
Ludwig Lausser Germany 12 228 1.5× 78 0.8× 82 1.6× 23 0.5× 20 0.9× 42 431
Jonathan L. Lustgarten United States 8 194 1.3× 92 0.9× 43 0.8× 14 0.3× 21 0.9× 12 414
Cheng Zhu China 9 240 1.6× 42 0.4× 19 0.4× 118 2.7× 24 1.0× 39 475
N. D. Jones Canada 8 119 0.8× 145 1.5× 23 0.5× 100 2.3× 16 0.7× 19 393
Giovanna Maria Dimitri Italy 12 81 0.5× 94 1.0× 10 0.2× 51 1.2× 14 0.6× 42 374
Erfan Younesi Germany 14 365 2.4× 131 1.4× 121 2.4× 109 2.5× 57 2.5× 35 643

Countries citing papers authored by Reagon Karki

Since Specialization
Citations

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

Fields of papers citing papers by Reagon Karki

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Reagon Karki

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

All Works

18 of 18 papers shown
1.
Karki, Reagon, Yojana Gadiya, Andrea Zaliani, et al.. (2025). KGG: a fully automated workflow for creating disease-specific knowledge graphs. Bioinformatics. 41(7).
2.
Greco, Alessandro, et al.. (2024). Pharmacological profiles of neglected tropical disease drugs. SHILAP Revista de lepidopterología. 6. 100116–100116. 1 indexed citations
3.
Russo, Maria Francesca, Daniel Domingo‐Fernándéz, Andrea Zaliani, et al.. (2024). Curating, Collecting, and Cataloguing Global COVID-19 Datasets for the Aim of Predicting Personalized Risk. Data. 9(2). 25–25.
4.
Karki, Reagon, Yojana Gadiya, Andrea Zaliani, & Philip Gribbon. (2023). Mpox Knowledge Graph: a comprehensive representation embedding chemical entities and associated biology of Mpox. Bioinformatics Advances. 3(1). vbad045–vbad045. 2 indexed citations
5.
Karki, Reagon, et al.. (2023). Pharmacophore-based ML model to filter candidate E3 ligands and predict E3 Ligase binding probabilities. Informatics in Medicine Unlocked. 44. 101424–101424. 1 indexed citations
6.
Ohmann, Christian, Maria Panagiotopoulou, Steve Canham, et al.. (2023). Proposal for a framework of contextual metadata in selected research infrastructures of the life sciences and the social sciences & humanities. International Journal of Metadata Semantics and Ontologies. 16(4). 261–277. 2 indexed citations
7.
Karki, Reagon, Yojana Gadiya, Philip Gribbon, & Andrea Zaliani. (2023). Pharmacophore-Based Machine Learning Model To Predict Ligand Selectivity for E3 Ligase Binders. ACS Omega. 8(33). 30177–30185. 10 indexed citations
8.
Domingo‐Fernándéz, Daniel, Sarah Mubeen, Charles Tapley Hoyt, et al.. (2021). A Systems Biology Approach for Hypothesizing the Effect of Genetic Variants on Neuroimaging Features in Alzheimer’s Disease. Journal of Alzheimer s Disease. 80(2). 831–840. 3 indexed citations
9.
Domingo‐Fernándéz, Daniel, Shounak Baksi, Bruce Schultz, et al.. (2020). COVID-19 Knowledge Graph: a computable, multi-modal, cause-and-effect knowledge model of COVID-19 pathophysiology. Bioinformatics. 37(9). 1332–1334. 72 indexed citations
10.
Karki, Reagon, et al.. (2020). Realistic simulation of virtual multi-scale, multi-modal patient trajectories using Bayesian networks and sparse auto-encoders. Scientific Reports. 10(1). 10971–10971. 9 indexed citations
11.
Karki, Reagon, Sumit Madan, Yojana Gadiya, et al.. (2020). Data-Driven Modeling of Knowledge Assemblies in Understanding Comorbidity Between Type 2 Diabetes Mellitus and Alzheimer’s Disease. Journal of Alzheimer s Disease. 78(1). 87–95. 14 indexed citations
12.
Karki, Reagon, Alpha Tom Kodamullil, Charles Tapley Hoyt, & Martin Hofmann‐Apitius. (2019). Quantifying mechanisms in neurodegenerative diseases (NDDs) using candidate mechanism perturbation amplitude (CMPA) algorithm. BMC Bioinformatics. 20(1). 494–494. 4 indexed citations
13.
Jong, Johann de, Ping Wu, Reagon Karki, et al.. (2019). Deep learning for clustering of multivariate clinical patient trajectories with missing values. GigaScience. 8(11). 48 indexed citations
14.
Kodamullil, Alpha Tom, et al.. (2017). Of Mice and Men: Comparative Analysis of Neuro-Inflammatory Mechanisms in Human and Mouse Using Cause-and-Effect Models. Journal of Alzheimer s Disease. 59(3). 1045–1055. 19 indexed citations
15.
Karki, Reagon, Alpha Tom Kodamullil, & Martin Hofmann‐Apitius. (2017). Comorbidity Analysis between Alzheimer’s Disease and Type 2 Diabetes Mellitus (T2DM) Based on Shared Pathways and the Role of T2DM Drugs. Journal of Alzheimer s Disease. 60(2). 721–731. 45 indexed citations
16.
Domingo‐Fernándéz, Daniel, Alpha Tom Kodamullil, Tamara Raschka, et al.. (2017). Multimodal mechanistic signatures for neurodegenerative diseases (NeuroMMSig): a web server for mechanism enrichment. Bioinformatics. 33(22). 3679–3681. 29 indexed citations
17.
Kodamullil, Alpha Tom, et al.. (2016). Using Drugs as Molecular Probes: A Computational Chemical Biology Approach in Neurodegenerative Diseases. Journal of Alzheimer s Disease. 56(2). 677–686. 13 indexed citations
18.
Fluck, Juliane, Sumit Madan, Sam Ansari, et al.. (2016). Training and evaluation corpora for the extraction of causal relationships encoded in biological expression language (BEL). Database. 2016. baw113–baw113. 20 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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