Shantanu Jain

403 total citations
17 papers, 101 citations indexed

About

Shantanu Jain is a scholar working on Artificial Intelligence, Surgery and Molecular Biology. According to data from OpenAlex, Shantanu Jain has authored 17 papers receiving a total of 101 indexed citations (citations by other indexed papers that have themselves been cited), including 7 papers in Artificial Intelligence, 3 papers in Surgery and 3 papers in Molecular Biology. Recurrent topics in Shantanu Jain's work include Machine Learning and Data Classification (6 papers), Machine Learning and Algorithms (5 papers) and Imbalanced Data Classification Techniques (5 papers). Shantanu Jain is often cited by papers focused on Machine Learning and Data Classification (6 papers), Machine Learning and Algorithms (5 papers) and Imbalanced Data Classification Techniques (5 papers). Shantanu Jain collaborates with scholars based in United States, India and Mexico. Shantanu Jain's co-authors include Predrag Radivojac, Martha White, Vikas Pejaver, Sean D. Mooney, Jose Lugo-Martinez, Matthew Mort, Kymberleigh A. Pagel, D.N. Cooper, Himanshu Sharma and Olga Vitek and has published in prestigious journals such as Bioinformatics, PLoS Computational Biology and Cardiology in the Young.

In The Last Decade

Shantanu Jain

13 papers receiving 100 citations

Peers — A (Enhanced Table)

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

Name	h						Papers	Cites
Shantanu Jain United States	7	39	39	17	12	9	17	101
Karim Beguir Germany	6	96 2.5×	32 0.8×	17 1.0×	12 1.0×	4 0.4×	11	179
Nicolás López Carranza United Kingdom	5	113 2.9×	23 0.6×	22 1.3×	7 0.6×	4 0.4×	7	181
Hoan Nguyen France	4	70 1.8×	11 0.3×	29 1.7×	7 0.6×	5 0.6×	11	91
Yasmin Alam-Faruque United Kingdom	6	126 3.2×	27 0.7×	14 0.8×	5 0.4×	1 0.1×	6	145
Yotam Frank Israel	2	96 2.5×	11 0.3×	11 0.6×	14 1.2×	25 2.8×	2	146
Sebastien Baur France	2	99 2.5×	7 0.2×	13 0.8×	16 1.3×	13 1.4×	2	117
Iaroslav Popov Russia	5	59 1.5×	30 0.8×	11 0.6×	11 0.9×	2 0.2×	9	107
Laia Codó Spain	5	99 2.5×	7 0.2×	9 0.5×	6 0.5×	15 1.7×	6	140
Guillaume Richard Germany	4	60 1.5×	23 0.6×	7 0.4×	10 0.8×	4 0.4×	7	119
Ravi Mathur United States	4	58 1.5×	14 0.4×	12 0.7×	11 0.9×		9	101

Countries citing papers authored by Shantanu Jain

Since Specialization

Citations

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

Fields of papers citing papers by Shantanu Jain

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Shantanu Jain

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

All Works

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17 of 17 papers shown

Roy, Mahua, et al.. (2025). Transcatheter closure of ventricular septal defect with aortic cusp prolapse with or without mild aortic regurgitation: experience from tertiary care referral hospital from Eastern India. Cardiology in the Young. 35(9). 1889–1898. 1 indexed citations

Baeza‐Yates, Ricardo, et al.. (2025). Are Your Fairness Metrics Accurate? A Semi-Supervised Approach to Improving Fairness Estimates Under Sample Selection Bias. 439–450.

Jain, Shantanu, et al.. (2024). An algorithm for decoy-free false discovery rate estimation in XL-MS/MS proteomics. Bioinformatics. 40(Supplement_1). i428–i436.

Jain, Shantanu, et al.. (2024). Transcatheter salvage of dying Glenn – An innovative strategy for postGlenn thrombosis. Annals of Pediatric Cardiology. 17(5). 372–376.

Jain, Shantanu, et al.. (2023). Leveraging Structure for Improved Classification of Grouped Biased Data. Proceedings of the AAAI Conference on Artificial Intelligence. 37(9). 11113–11120. 1 indexed citations

Jain, Shantanu, et al.. (2022). An Approach to Identifying and Quantifying Bias in Biomedical Data. PubMed. 28. 311–322. 2 indexed citations

Chen, Yile, Shantanu Jain, Lilia M. Iakoucheva, et al.. (2022). Multi-objective prioritization of genes for high-throughput functional assays towards improved clinical variant classification. PubMed. 28. 323–334.

Jain, Shantanu, et al.. (2022). Using Association Rules to Understand the Risk of Adverse Pregnancy Outcomes in a Diverse Population. PubMed. 28. 209–220. 1 indexed citations

Shah, Chirag, et al.. (2021). Study of extrapulmonary tuberculosis in tertiary care hospital children with reference to cartridge based nucleic acid amplification test. International Journal of Contemporary Pediatrics. 8(12). 1947–1947. 1 indexed citations

10.

Jain, Shantanu, Yong Fuga Li, Michal Greguš, et al.. (2020). New mixture models for decoy-free false discovery rate estimation in mass spectrometry proteomics. Bioinformatics. 36(Supplement_2). i745–i753. 7 indexed citations

11.

Jain, Shantanu, et al.. (2020). Class Prior Estimation with Biased Positives and Unlabeled Examples. Proceedings of the AAAI Conference on Artificial Intelligence. 34(4). 4255–4263. 8 indexed citations

12.

Jain, Shantanu, et al.. (2020). Fast Nonparametric Estimation of Class Proportions in the Positive-Unlabeled Classification Setting. Proceedings of the AAAI Conference on Artificial Intelligence. 34(4). 6729–6736. 12 indexed citations

13.

Jain, Shantanu, et al.. (2018). Estimating classification accuracy in positive-unlabeled learning: characterization and correction strategies. PubMed. 24. 124–135. 11 indexed citations

14.

Jain, Shantanu, Martha White, & Predrag Radivojac. (2017). Recovering True Classifier Performance in Positive-Unlabeled Learning. Proceedings of the AAAI Conference on Artificial Intelligence. 31(1). 29 indexed citations

15.

Jain, Shantanu, Martha White, & Predrag Radivojac. (2017). Recovering True Classifier Performance in Positive-Unlabeled Learning. arXiv (Cornell University). 31(1). 2066–2072. 2 indexed citations

16.

Jain, Shantanu, Martha White, & Predrag Radivojac. (2016). Estimating the class prior and posterior from noisy positives and unlabeled data. Neural Information Processing Systems. 29. 2685–2693. 11 indexed citations

17.

Lugo-Martinez, Jose, Vikas Pejaver, Kymberleigh A. Pagel, et al.. (2016). The Loss and Gain of Functional Amino Acid Residues Is a Common Mechanism Causing Human Inherited Disease. PLoS Computational Biology. 12(8). e1005091–e1005091. 15 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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