Shashikiran Umakanth

1.7k total citations
112 papers, 1.1k citations indexed

About

Shashikiran Umakanth is a scholar working on Endocrinology, Diabetes and Metabolism, Infectious Diseases and Physiology. According to data from OpenAlex, Shashikiran Umakanth has authored 112 papers receiving a total of 1.1k indexed citations (citations by other indexed papers that have themselves been cited), including 20 papers in Endocrinology, Diabetes and Metabolism, 17 papers in Infectious Diseases and 16 papers in Physiology. Recurrent topics in Shashikiran Umakanth's work include COVID-19 diagnosis using AI (12 papers), Diabetes, Cardiovascular Risks, and Lipoproteins (10 papers) and Mosquito-borne diseases and control (8 papers). Shashikiran Umakanth is often cited by papers focused on COVID-19 diagnosis using AI (12 papers), Diabetes, Cardiovascular Risks, and Lipoproteins (10 papers) and Mosquito-borne diseases and control (8 papers). Shashikiran Umakanth collaborates with scholars based in India, Malaysia and United States. Shashikiran Umakanth's co-authors include Krishnaraj Chadaga, Niranjana Sampathila, Shashikala K Bhat, Srikanth Prabhu, Raghavendra C. Kamath, Joseph M Pappachan, Vivekananda Bhat K, Rajagopala Chadaga, Kapaettu Satyamoorthy and N Girish and has published in prestigious journals such as SHILAP Revista de lepidopterología, PLoS ONE and Scientific Reports.

In The Last Decade

Shashikiran Umakanth

98 papers receiving 1.1k citations

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
Shashikiran Umakanth India 18 190 148 146 138 129 112 1.1k
Mary Southworth United States 18 271 1.4× 232 1.6× 70 0.5× 202 1.5× 217 1.7× 31 3.0k
Xinyan Liu China 18 372 2.0× 127 0.9× 87 0.6× 85 0.6× 182 1.4× 69 1.4k
Lina Zhang China 23 535 2.8× 122 0.8× 99 0.7× 289 2.1× 168 1.3× 131 1.8k
Shuting Wang China 23 421 2.2× 123 0.8× 72 0.5× 144 1.0× 229 1.8× 125 1.6k
Xinrui Li China 18 318 1.7× 51 0.3× 77 0.5× 131 0.9× 146 1.1× 59 1.3k
Smadar Shilo Israel 20 246 1.3× 167 1.1× 80 0.5× 212 1.5× 137 1.1× 41 1.4k
Maryam Taghdir Iran 17 109 0.6× 131 0.9× 83 0.6× 106 0.8× 88 0.7× 54 1.4k
Dana Carmen Zaha Romania 20 201 1.1× 61 0.4× 65 0.4× 58 0.4× 160 1.2× 60 1.1k
Nor Hayati Othman Malaysia 23 297 1.6× 140 0.9× 53 0.4× 63 0.5× 288 2.2× 87 2.1k
Pierpaolo Cavallo Italy 22 232 1.2× 75 0.5× 52 0.4× 173 1.3× 153 1.2× 72 1.2k

Countries citing papers authored by Shashikiran Umakanth

Since Specialization
Citations

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

Fields of papers citing papers by Shashikiran Umakanth

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Shashikiran Umakanth

This figure shows the co-authorship network connecting the top 25 collaborators of Shashikiran Umakanth. A scholar is included among the top collaborators of Shashikiran Umakanth 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 Shashikiran Umakanth. Shashikiran Umakanth 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
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Umakanth, Shashikiran, et al.. (2024). Four-Step Co-Designing of the Reablement Strategies Targeting Sarcopenia (ReStart-S): An Exercise-Based Multicomponent Program for Older Adults Residing in Long-Term Care Settings. Journal of Multidisciplinary Healthcare. Volume 17. 1415–1433. 8 indexed citations
4.
Chadaga, Krishnaraj, et al.. (2024). An interpretable and transparent machine learning framework for appendicitis detection in pediatric patients. Scientific Reports. 14(1). 24454–24454. 7 indexed citations
5.
Chadaga, Krishnaraj, Srikanth Prabhu, Niranjana Sampathila, Rajagopala Chadaga, & Shashikiran Umakanth. (2024). An Explainable Decision Support Framework for Differential Diagnosis Between Mild COVID-19 and Other Similar Influenzas. IEEE Access. 12. 75010–75033. 3 indexed citations
6.
Vasishta, Sampara, et al.. (2024). High glucose induces DNA methyltransferase 1 dependent epigenetic reprogramming of the endothelial exosome proteome in type 2 diabetes. The International Journal of Biochemistry & Cell Biology. 176. 106664–106664. 4 indexed citations
7.
Chadaga, Krishnaraj, Srikanth Prabhu, Vivekananda Bhat K, et al.. (2023). Artificial intelligence for diagnosis of mild–moderate COVID-19 using haematological markers. Annals of Medicine. 55(1). 2233541–2233541. 19 indexed citations
8.
Umakanth, Shashikiran, et al.. (2023). Effect of family-centered care interventions on well-being of caregivers of children with cerebral palsy: a systematic review. F1000Research. 12. 790–790. 2 indexed citations
9.
Chadaga, Krishnaraj, Srikanth Prabhu, Vivekananda Bhat K, et al.. (2023). A Decision Support System for Diagnosis of COVID-19 from Non-COVID-19 Influenza-like Illness Using Explainable Artificial Intelligence. Bioengineering. 10(4). 439–439. 25 indexed citations
10.
Chadaga, Krishnaraj, et al.. (2023). Deep learning based detection of monkeypox virus using skin lesion images. Medicine in Novel Technology and Devices. 18. 100243–100243. 49 indexed citations
11.
Umakanth, Shashikiran, et al.. (2023). Effect of targeted intervention on C-terminal agrin fragment and its association with the components of sarcopenia: a scoping review. Aging Clinical and Experimental Research. 35(6). 1161–1186. 7 indexed citations
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Chadaga, Krishnaraj, et al.. (2023). COVID-19 diagnosis using clinical markers and multiple explainable artificial intelligence approaches: A case study from Ecuador. SLAS TECHNOLOGY. 28(6). 393–410. 5 indexed citations
13.
Umakanth, Shashikiran, et al.. (2022). A review of the components of exercise prescription for sarcopenic older adults. European Geriatric Medicine. 13(6). 1245–1280. 22 indexed citations
14.
Chadaga, Krishnaraj, et al.. (2022). Diagnosing COVID-19 using artificial intelligence: a comprehensive review. Network Modeling Analysis in Health Informatics and Bioinformatics. 11(1). 30 indexed citations
15.
Chadaga, Krishnaraj, Chinmay Chakraborty, Srikanth Prabhu, et al.. (2022). Clinical and Laboratory Approach to Diagnose COVID-19 Using Machine Learning. Interdisciplinary Sciences Computational Life Sciences. 14(2). 452–470. 46 indexed citations
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Peter, Mathew, et al.. (2022). Dengue NS1 antigen detection using photoluminescence of solution phase biotylinated anti-NS1 antibody conjugated ZnO quantum dots. Materials Chemistry and Physics. 279. 125778–125778. 1 indexed citations
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Chadaga, Krishnaraj, et al.. (2021). Battling COVID-19 using machine learning: A review. Cogent Engineering. 8(1). 24 indexed citations
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Gupta, Himanshu, Manjunath Hande, Sydney C. D’Souza, et al.. (2015). Categorical complexities of Plasmodium falciparum malaria in individuals is associated with genetic variations in ADORA2A and GRK5 genes. Infection Genetics and Evolution. 34. 188–199. 13 indexed citations
20.
Vidyasagar, Sudha, Shashikiran Umakanth, Sharath K Rao, et al.. (2004). Efficacy and Tolerability of Glucosamine - Chondroitin Sulphate - Methyl Sulfonyl Methane (MSM) in Osteoarthritis of Knee in Indian Patients. TSpace (University of Toronto). 3(2). 61–65. 12 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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