Uday Kamath

1.5k citations
20 papers · 768 indexed · 1 hit paper · h-index 9
Topics
Machine Learning in Bioinformatics (5 papers)Evolutionary Algorithms and Applications (5 papers)RNA and protein synthesis mechanisms (5 papers)
Partner nations
United StatesChina

In The Last Decade

Uday Kamath

19 papers receiving 748 citations

Hit Papers

Deep learning improves antimicrobial peptide recognition20182026202020232018100200300

Peers

Uday Kamath
Comparison fields: 5 of 125
  • Molecular Biology 445
  • Microbiology 311
  • Artificial Intelligence 180
  • Computational Theory and Mathematics 44
  • Signal Processing 43
Replace Abhinav Kumar with:
Abhinav Kumar India
Milad Salem United States
Ritesh Sharma India
Sergio A. Álvarez United States
Xiaoli Qiang China
Paul Walsh Ireland
Maqsood Hayat Pakistan
Christoph Angerer Austria
Ritesh Kumar India
Llion Jones United States
Uday Kamath relative to Abhinav Kumar India Abhinav Kumar's profile →
Citations per field
00.5×10×15×20×23.5×
Abhinav Kumar · 1×
Citations per year

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

20 of 20 papers shown
#WorkIndexed citations
1 9
2 19
3 78
4 5
5 128
6
Deep learning improves antimicrobial peptide recognitionbreakdown →
367
7
Mastering Java Machine Learning
3
8 1
9 49
10 48
11 7
12 3
13 3
14 0
15 26
16 4
17 3
18 9
19 5
20 1

About Uday Kamath

Uday Kamath is a scholar working on Microbiology, Artificial Intelligence and Molecular Biology, having authored 20 papers that have together received 768 indexed citations. Recurring topics across this work include Machine Learning in Bioinformatics (5 papers), Evolutionary Algorithms and Applications (5 papers) and RNA and protein synthesis mechanisms (5 papers). The work is most often cited by research in Microbiology (311 citations), Health Informatics (15 citations) and Molecular Biology (445 citations). Uday Kamath has collaborated with scholars based in United States and China. Frequent 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. Their work appears in journals such as Bioinformatics, PLoS ONE and Neurocomputing.

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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