Uday Kamath

1.5k total citations · 1 hit paper
20 papers, 768 citations indexed

About

Uday Kamath is a scholar working on Artificial Intelligence, Molecular Biology and Microbiology. According to data from OpenAlex, Uday Kamath has authored 20 papers receiving a total of 768 indexed citations (citations by other indexed papers that have themselves been cited), including 9 papers in Artificial Intelligence, 8 papers in Molecular Biology and 3 papers in Microbiology. Recurrent topics in Uday Kamath's work include Machine Learning in Bioinformatics (5 papers), Evolutionary Algorithms and Applications (5 papers) and RNA and protein synthesis mechanisms (5 papers). Uday Kamath is often cited by papers focused on Machine Learning in Bioinformatics (5 papers), Evolutionary Algorithms and Applications (5 papers) and RNA and protein synthesis mechanisms (5 papers). Uday Kamath collaborates with scholars based in United States and China. Uday Kamath's co-authors include Amarda Shehu, Daniel Veltri, John Liu, James Whitaker, John Liu, Kenneth De Jong, Kenneth Graham, Rezarta Islamaj, Carlotta Domeniconi and Jessica Lin and has published in prestigious journals such as Bioinformatics, PLoS ONE and Neurocomputing.

In The Last Decade

Uday Kamath

19 papers receiving 748 citations

Hit Papers

align trajectories

What are hit papers?

Hit papers significantly outperform the citation benchmark for their cohort. A paper qualifies if it has ≥500 total citations, achieves ≥1.5× the top-1% citation threshold for papers in the same subfield and year (this is the minimum needed to enter the top 1%, not the average within it), or reaches the top citation threshold in at least one of its specific research topics.

Deep learning improves antimicrobial peptide recognition

2018 367 citations Daniel Veltri, Uday Kamath et al. Bioinformatics profile →

Peers

Uday Kamath

MB

MICRO

AI

CTM

SP

Abhinav Kumar India

Jim Z.C. Lai Taiwan

Ritesh Kumar India

Tonghai Jiang China

Wenbo Mao China

Tobias Petri Germany

Ritesh Sharma India

Jun Meng China

Paul Walsh Ireland

Abhinav Kumar India

Uday Kamath

355 ×0.8

MB

209 ×0.7

MICRO

381 ×2.1

AI

58 ×1.3

CTM

18 ×0.4

SP

Citations per year, relative to Uday Kamath Uday Kamath (= 1×) peers Abhinav Kumar

Countries citing papers authored by Uday Kamath

Since Specialization

Citations

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

Fields of papers citing papers by Uday Kamath

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Uday Kamath

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

All Works

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20 of 20 papers shown

1.

Kamath, Uday, et al.. (2024). Large Language Models: A Deep Dive. 9 indexed citations

2.

Kamath, Uday, et al.. (2022). Transformers for Machine Learning. 19 indexed citations

3.

Kamath, Uday & John Liu. (2021). Explainable Artificial Intelligence: An Introduction to Interpretable Machine Learning. 78 indexed citations

4.

Ren, Yazhou, Uday Kamath, Carlotta Domeniconi, & Zenglin Xu. (2019). Parallel boosted clustering. Neurocomputing. 351. 87–100. 5 indexed citations

5.

Kamath, Uday, John Liu, & James Whitaker. (2019). Deep Learning for NLP and Speech Recognition. 128 indexed citations

6.

Veltri, Daniel, Uday Kamath, & Amarda Shehu. (2018). Deep learning improves antimicrobial peptide recognition. Bioinformatics. 34(16). 2740–2747. 367 indexed citations breakdown →

7.

Kamath, Uday, et al.. (2017). Mastering Java Machine Learning. CERN Document Server (European Organization for Nuclear Research). 3 indexed citations

8.

Kamath, Uday, Carlotta Domeniconi, & Kenneth De Jong. (2016). Theoretical and Empirical Analysis of a Spatial EA Parallel Boosting Algorithm. Evolutionary Computation. 26(1). 43–66. 1 indexed citations

9.

Veltri, Daniel, Uday Kamath, & Amarda Shehu. (2015). Improving Recognition of Antimicrobial Peptides and Target Selectivity through Machine Learning and Genetic Programming. IEEE/ACM Transactions on Computational Biology and Bioinformatics. 14(2). 300–313. 49 indexed citations

10.

Kamath, Uday, Kenneth De Jong, & Amarda Shehu. (2014). Effective Automated Feature Construction and Selection for Classification of Biological Sequences. PLoS ONE. 9(7). e99982–e99982. 48 indexed citations

11.

Kamath, Uday, Jessica Lin, & Kenneth De Jong. (2014). SAX-EFG. 13. 533–540. 7 indexed citations

12.

Veltri, Daniel, Uday Kamath, & Amarda Shehu. (2014). A novel method to improve recognition of antimicrobial peptides through distal sequence-based features. 7. 371–378. 3 indexed citations

13.

Kamath, Uday, Carlotta Domeniconi, & Kenneth De Jong. (2013). An analysis of a spatial EA parallel boosting algorithm. 1053–1060. 3 indexed citations

14.

Kamath, Uday, et al.. (2012). A new methodology for the GP theory toolbox. 719–726.

15.

Kamath, Uday, et al.. (2012). An Evolutionary Algorithm Approach for Feature Generation from Sequence Data and Its Application to DNA Splice Site Prediction. IEEE/ACM Transactions on Computational Biology and Bioinformatics. 9(5). 1387–1398. 26 indexed citations

16.

Kamath, Uday, Amarda Shehu, & Kenneth De Jong. (2011). A TWO-STAGE EVOLUTIONARY APPROACH FOR EFFECTIVE CLASSIFICATION OF HYPERSENSITIVE DNA SEQUENCES. Journal of Bioinformatics and Computational Biology. 9(3). 399–413. 4 indexed citations

17.

Kamath, Uday, Kenneth De Jong, & Amarda Shehu. (2011). An evolutionary-based approach for feature generation: Eukaryotic promoter recognition. 2. 277–284. 3 indexed citations

18.

Kamath, Uday, Amarda Shehu, & Kenneth De Jong. (2010). Using evolutionary computation to improve SVM classification. 8. 1–8. 9 indexed citations

19.

Kamath, Uday, Kenneth De Jong, & Amarda Shehu. (2010). Selecting predictive features for recognition of hypersensitive sites of regulatory genomic sequences with an evolutionary algorithm. 179–186. 5 indexed citations

20.

Kamath, Uday. (2008). Self-Learning Expert Systems using Rule Classifier in Detection Engines.. 174(1). 224–227. 1 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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