Kumardeep Chaudhary

11.7k citations
56 papers · 6.4k indexed · 3 hit papers · h-index 34

Kumardeep Chaudhary

54 papers receiving 6.3k citations

Hit Papers

Deep Learning–Based Multi-Omics Integration Robustly Pred...68020132026201720214008001.2k

Peers

Kumardeep Chaudhary
Comparison fields: 5 of 147
  • Microbiology 1.3k
  • Molecular Biology 5.1k
  • Health Informatics 86
  • Radiology, Nuclear Medicine and Imaging 830
  • Computational Theory and Mathematics 562
Replace Nina M. Donghia with:
Nina M. Donghia United States
Yongqun He United States
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Kumardeep Chaudhary relative to Nina M. Donghia United States Nina M. Donghia's profile →
Citations per field
00.5×9.4×
Nina M. Donghia · 1×
Citations per year

Countries citing papers authored by Kumardeep Chaudhary

Since Specialization
Citations

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

Fields of papers citing papers by Kumardeep Chaudhary

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network

The 25 scholars most cited alongside Kumardeep Chaudhary, linked wherever they have co-authored with each other. Click a name or a connecting line to browse the papers they share.

Border = papers with Kumardeep Chaudhary Line = papers co-authored together Kumardeep Chaudhary links everyone, so they are left out of the graph.

All Works

20 of 20 papers shown
#Work
1 20250
2 202222
3 20216
4 20212
5 202024
6 202066
7 201930
8 2018129
9
Deep Learning–Based Multi-Omics Integration Robustly Predicts Survival in Liver Cancerbreakdown →
2017680
10 201646
11 2016178
12 2015169
13 201567
14 2014172
15 2014171
16 2013248
17 201394
18 2013258
19 2012114
20 2012165

About Kumardeep Chaudhary

Kumardeep Chaudhary is a scholar working on Microbiology, Nephrology and Molecular Biology, having authored 56 papers that have together received 6.4k indexed citations. Recurring topics across this work include Antimicrobial Peptides and Activities (13 papers), vaccines and immunoinformatics approaches (11 papers), Machine Learning in Bioinformatics (8 papers), RNA Interference and Gene Delivery (8 papers), Computational Drug Discovery Methods (7 papers), Cancer Genomics and Diagnostics (6 papers), Ferroptosis and cancer prognosis (5 papers) and Biochemical and Structural Characterization (5 papers). The work is most often cited by research in Microbiology (1.3k citations), Molecular Biology (5.1k citations) and Health Informatics (86 citations). Kumardeep Chaudhary has collaborated with scholars based in India, United States and Thailand. Frequent co-authors include Gajendra P. S. Raghava, Ankur Gautam, Rahul Kumar, Pallavi Kapoor, Sudheer Gupta, Olivier Poirion, Sijia Huang, Lana X. Garmire, Liangqun Lu and Lana X. Garmire. Their work appears in journals such as JAMA, Nucleic Acids Research and Journal of the American College of Cardiology.

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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