Ayush Singhal

753 total citations
22 papers, 457 citations indexed

About

Ayush Singhal is a scholar working on Artificial Intelligence, Information Systems and Molecular Biology. According to data from OpenAlex, Ayush Singhal has authored 22 papers receiving a total of 457 indexed citations (citations by other indexed papers that have themselves been cited), including 8 papers in Artificial Intelligence, 6 papers in Information Systems and 5 papers in Molecular Biology. Recurrent topics in Ayush Singhal's work include Biomedical Text Mining and Ontologies (5 papers), Genomics and Rare Diseases (4 papers) and Topic Modeling (4 papers). Ayush Singhal is often cited by papers focused on Biomedical Text Mining and Ontologies (5 papers), Genomics and Rare Diseases (4 papers) and Topic Modeling (4 papers). Ayush Singhal collaborates with scholars based in United States, India and Switzerland. Ayush Singhal's co-authors include Zhiyong Lu, Michael A. Simmons, Jaideep Srivastava, Pradeep K. Sinha, Praveen Kumar, Ankush Mittal, Ajay Kumar, Johanna McEntyre, Natalie L. Catlett and Thomas Lemberger and has published in prestigious journals such as SHILAP Revista de lepidopterología, Ophthalmology and PLoS Computational Biology.

In The Last Decade

Ayush Singhal

22 papers receiving 436 citations

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
Ayush Singhal United States 10 161 149 79 74 44 22 457
An Wang China 11 108 0.7× 167 1.1× 73 0.9× 26 0.4× 11 0.3× 53 401
Abdullah Almuhaimeed Saudi Arabia 13 194 1.2× 106 0.7× 105 1.3× 27 0.4× 81 1.8× 26 554
Pedro González Spain 15 467 2.9× 123 0.8× 27 0.3× 348 4.7× 22 0.5× 45 818
Mohamed Fakir Morocco 11 113 0.7× 29 0.2× 211 2.7× 53 0.7× 79 1.8× 97 490
David J. Dittman United States 14 422 2.6× 197 1.3× 108 1.4× 137 1.9× 11 0.3× 38 612
Barbara Pes Italy 12 270 1.7× 154 1.0× 125 1.6× 77 1.0× 40 0.9× 47 563
Rasmita Dash India 12 251 1.6× 108 0.7× 73 0.9× 43 0.6× 34 0.8× 45 430
Reinel Tabares-Soto Colombia 14 176 1.1× 142 1.0× 162 2.1× 22 0.3× 115 2.6× 46 632
Murtada K. Elbashir Saudi Arabia 9 124 0.8× 121 0.8× 43 0.5× 23 0.3× 59 1.3× 45 371
Chenglang Lu China 10 207 1.3× 15 0.1× 80 1.0× 82 1.1× 66 1.5× 20 407

Countries citing papers authored by Ayush Singhal

Since Specialization
Citations

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

Fields of papers citing papers by Ayush Singhal

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Ayush Singhal

This figure shows the co-authorship network connecting the top 25 collaborators of Ayush Singhal. A scholar is included among the top collaborators of Ayush Singhal 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 Ayush Singhal. Ayush Singhal 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.
Kumari, Supriya, et al.. (2023). Fault Detection in Transmission Line Using ANN. 1–5. 6 indexed citations
2.
Singhal, Ayush, et al.. (2022). Study of deep learning techniques for medical image analysis: A review. Materials Today Proceedings. 56. 209–214. 28 indexed citations
3.
Asten, Freekje van, Michael A. Simmons, Ayush Singhal, et al.. (2017). A Deep Phenotype Association Study Reveals Specific Phenotype Associations with Genetic Variants in Age-related Macular Degeneration. Ophthalmology. 125(4). 559–568. 29 indexed citations
4.
Singhal, Ayush & Jaideep Srivastava. (2017). Research dataset discovery from research publications using web context. 15(2). 81–99. 7 indexed citations
5.
Singhal, Ayush, et al.. (2017). DFM-aware fault model and ATPG for intra-cell and inter-cell defects. 1–10. 18 indexed citations
6.
Singhal, Ayush, Kasthurirangan Gopalakrishnan, & Siddhartha Kumar Khaitan. (2017). Predicting Budget from Transportation Research Grant Description: An Exploratory Analysis of Text Mining and Machine Learning Techniques. SHILAP Revista de lepidopterología. 1(2). 89–102. 2 indexed citations
7.
Roy, Atanu Singha, Ayush Singhal, & Jaideep Srivastava. (2017). Formation and Reciprocation of Dyadic Trust. ACM Transactions on Internet Technology. 17(2). 1–24. 5 indexed citations
8.
Singhal, Ayush, et al.. (2017). Use of Deep Learning in Modern Recommendation System: A Summary of Recent Works. International Journal of Computer Applications. 180(7). 17–22. 57 indexed citations
9.
Simmons, Michael A., Ayush Singhal, & Zhiyong Lu. (2016). Text Mining for Precision Medicine: Bringing Structure to EHRs and Biomedical Literature to Understand Genes and Health. Advances in experimental medicine and biology. 939. 139–166. 41 indexed citations
10.
Singhal, Ayush, Michael A. Simmons, & Zhiyong Lu. (2016). Text Mining Genotype-Phenotype Relationships from Biomedical Literature for Database Curation and Precision Medicine. PLoS Computational Biology. 12(11). e1005017–e1005017. 70 indexed citations
11.
Singhal, Ayush, Robert Leaman, Natalie L. Catlett, et al.. (2016). Pressing needs of biomedical text mining in biocuration and beyond: opportunities and challenges. Database. 2016. baw161–baw161. 34 indexed citations
12.
Singhal, Ayush, Michael A. Simmons, & Zhiyong Lu. (2016). Text mining for precision medicine: automating disease-mutation relationship extraction from biomedical literature. Journal of the American Medical Informatics Association. 23(4). 766–772. 46 indexed citations
13.
Singhal, Ayush, Atanu Singha Roy, & Jaideep Srivastava. (2014). Understanding co-evolution in large multi-relational social networks. 733–740. 7 indexed citations
14.
Singhal, Ayush & Jaideep Srivastava. (2014). Generating Semantic Annotations For Research Datasets. 1–11. 1 indexed citations
15.
Singhal, Ayush, et al.. (2014). DataGopher: Context-based search for research datasets. 1. 749–756. 5 indexed citations
16.
Subbian, Karthik, Ayush Singhal, Tamara G. Kolda, Ali Pınar, & Jaideep Srivastava. (2013). On Reciprocity in Massively Multi-player Online Game Networks. arXiv (Cornell University). 2 indexed citations
17.
Kumar, Praveen, et al.. (2013). Real-time moving object detection algorithm on high-resolution videos using GPUs. Journal of Real-Time Image Processing. 11(1). 93–109. 63 indexed citations
18.
Singhal, Ayush, Karthik Subbian, Jaideep Srivastava, Tamara G. Kolda, & Ali Pınar. (2013). Dynamics of trust reciprocation in multi-relational networks. 661–665. 7 indexed citations
19.
Singhal, Ayush, et al.. (2013). Leveraging Web Intelligence for Finding Interesting Research Datasets. 84. 321–328. 12 indexed citations
20.
Singhal, Ayush & Jaideep Srivastava. (2013). Data Extract: Mining Context from the Web for Dataset Extraction. International Journal of Machine Learning and Computing. 219–223. 8 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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