Yojana Gadiya

736 total citations
25 papers, 235 citations indexed

About

Yojana Gadiya is a scholar working on Molecular Biology, Computational Theory and Mathematics and Artificial Intelligence. According to data from OpenAlex, Yojana Gadiya has authored 25 papers receiving a total of 235 indexed citations (citations by other indexed papers that have themselves been cited), including 18 papers in Molecular Biology, 12 papers in Computational Theory and Mathematics and 4 papers in Artificial Intelligence. Recurrent topics in Yojana Gadiya's work include Computational Drug Discovery Methods (12 papers), Bioinformatics and Genomic Networks (8 papers) and Plant biochemistry and biosynthesis (4 papers). Yojana Gadiya is often cited by papers focused on Computational Drug Discovery Methods (12 papers), Bioinformatics and Genomic Networks (8 papers) and Plant biochemistry and biosynthesis (4 papers). Yojana Gadiya collaborates with scholars based in Germany, United Kingdom and Luxembourg. Yojana Gadiya's co-authors include Daniel Domingo‐Fernándéz, Martin Hofmann‐Apitius, Reagon Karki, Alpha Tom Kodamullil, Andrea Zaliani, Sarah Mubeen, Philip Gribbon, Bruce Schultz, Christian Ebeling and Shounak Baksi and has published in prestigious journals such as SHILAP Revista de lepidopterología, Bioinformatics and PLoS Computational Biology.

In The Last Decade

Yojana Gadiya

22 papers receiving 230 citations

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
Yojana Gadiya Germany 9 128 54 44 16 16 25 235
Peter Woollard United Kingdom 10 177 1.4× 45 0.8× 48 1.1× 10 0.6× 8 0.5× 11 302
Mohammad Mahdi Jaghoori Netherlands 8 97 0.8× 138 2.6× 47 1.1× 16 1.0× 12 0.8× 23 297
Youngmi Yoon South Korea 11 271 2.1× 147 2.7× 39 0.9× 14 0.9× 18 1.1× 52 387
Piotr Grabowski United Kingdom 10 238 1.9× 56 1.0× 13 0.3× 4 0.3× 18 1.1× 20 403
Shantao Li United States 12 397 3.1× 83 1.5× 29 0.7× 10 0.6× 63 3.9× 14 610
Dandan Huang China 7 114 0.9× 86 1.6× 17 0.4× 20 1.3× 3 0.2× 8 329
René van Schaik Netherlands 5 215 1.7× 61 1.1× 31 0.7× 13 0.8× 6 0.4× 5 288
Diego Galeano Paraguay 7 91 0.7× 102 1.9× 22 0.5× 13 0.8× 5 0.3× 16 242
Daniel Tang United States 9 103 0.8× 35 0.6× 73 1.7× 47 2.9× 7 0.4× 17 445
Patricia M. Palagi Switzerland 11 481 3.8× 27 0.5× 16 0.4× 7 0.4× 26 1.6× 26 667

Countries citing papers authored by Yojana Gadiya

Since Specialization
Citations

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

Fields of papers citing papers by Yojana Gadiya

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Yojana Gadiya

This figure shows the co-authorship network connecting the top 25 collaborators of Yojana Gadiya. A scholar is included among the top collaborators of Yojana Gadiya 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 Yojana Gadiya. Yojana Gadiya 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.
Gadiya, Yojana, Olga Genilloud, Ursula Bilitewski, et al.. (2025). Predicting Antimicrobial Class Specificity of Small Molecules Using Machine Learning. Journal of Chemical Information and Modeling. 65(5). 2416–2431. 1 indexed citations
2.
Gadiya, Yojana, et al.. (2025). Defining the limits of plant chemical space: challenges and estimations. GigaScience. 14. 1 indexed citations
3.
Karki, Reagon, Yojana Gadiya, Andrea Zaliani, et al.. (2025). KGG: a fully automated workflow for creating disease-specific knowledge graphs. Bioinformatics. 41(7).
4.
Reinshagen, Jeanette, Brinton Seashore‐Ludlow, Yojana Gadiya, et al.. (2025). From library to landscape: integrative annotation workflows for compound libraries in drug repurposing. Database. 2025.
5.
Gadiya, Yojana, et al.. (2024). Exploring SureChEMBL from a drug discovery perspective. Scientific Data. 11(1). 507–507. 6 indexed citations
6.
Balaur, Irina, Hanna Ćwiek‐Kupczyńska, Yojana Gadiya, et al.. (2024). Getting ready for the European Health Data Space (EHDS): IDERHA's plan to align with the latest EHDS requirements for the secondary use of health data. SHILAP Revista de lepidopterología. 4. 160–160. 8 indexed citations
7.
Greco, Alessandro, et al.. (2024). Pharmacological profiles of neglected tropical disease drugs. SHILAP Revista de lepidopterología. 6. 100116–100116. 1 indexed citations
8.
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.
9.
Domingo‐Fernándéz, Daniel, et al.. (2024). Natural Products Have Increased Rates of Clinical Trial Success throughout the Drug Development Process. Journal of Natural Products. 87(7). 1844–1851. 31 indexed citations
10.
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
11.
Gadiya, Yojana, Vassilios Ioannidis, David Henderson, et al.. (2023). FAIR data management: what does it mean for drug discovery?. SHILAP Revista de lepidopterología. 3. 5 indexed citations
12.
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
13.
Gadiya, Yojana, Philip Gribbon, Martin Hofmann‐Apitius, & Andrea Zaliani. (2023). Pharmaceutical patent landscaping: A novel approach to understand patents from the drug discovery perspective. SHILAP Revista de lepidopterología. 3. 100069–100069. 5 indexed citations
14.
Domingo‐Fernándéz, Daniel, et al.. (2023). Exploring the known chemical space of the plant kingdom: insights into taxonomic patterns, knowledge gaps, and bioactive regions. Journal of Cheminformatics. 15(1). 107–107. 12 indexed citations
15.
Gadiya, Yojana, Andrea Zaliani, Philip Gribbon, & Martin Hofmann‐Apitius. (2022). PEMT: a patent enrichment tool for drug discovery. Bioinformatics. 39(1). 5 indexed citations
16.
Gadiya, Yojana, David Henderson, Andrea Zaliani, et al.. (2022). Selection of data sets for FAIRification in drug discovery and development: Which, why, and how?. Drug Discovery Today. 27(8). 2080–2085. 9 indexed citations
17.
Domingo‐Fernándéz, Daniel, et al.. (2022). Causal reasoning over knowledge graphs leveraging drug-perturbed and disease-specific transcriptomic signatures for drug discovery. PLoS Computational Biology. 18(2). e1009909–e1009909. 8 indexed citations
18.
Gadiya, Yojana, et al.. (2022). SASC: A simple approach to synthetic cohorts for generating longitudinal observational patient cohorts from COVID-19 clinical data. Patterns. 3(4). 100453–100453. 5 indexed citations
19.
Mubeen, Sarah, et al.. (2021). DecoPath: a web application for decoding pathway enrichment analysis. NAR Genomics and Bioinformatics. 3(3). lqab087–lqab087. 3 indexed citations
20.
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

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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