Utkarsh Raj

1.3k citations
52 papers · 889 · h-index 15

Impact in

Papers in

    • Bioinformatics and Genomic Networks 6
    • vaccines and immunoinformatics approaches 4
    • Epigenetics and DNA Methylation 3
    • Machine Learning in Bioinformatics 3
    • Gene expression and cancer classification 3
    • Computational Drug Discovery Methods 14

Utkarsh Raj

47 papers receiving 862 citations

Peers

Utkarsh Raj
Comparison fields: 5 of 116
  • Computational Theory and Mathematics 198
  • Pharmacology 52
  • Molecular Biology 401
  • Infectious Diseases 92
  • Toxicology 16
Replace Bilal Shaker with:
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Citations per field
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Citations per year

Countries citing papers authored by Utkarsh Raj

Since Specialization
Citations

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

Fields of papers citing papers by Utkarsh Raj

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authors

The 25 scholars most cited alongside Utkarsh Raj, 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 Utkarsh Raj Line = papers co-authored together Utkarsh Raj links everyone, so they are left out of the graph.

All Works

20 of 20 papers shown

Showing the 20 most-cited of 52 papers — load more, or switch the sort, to bring in the rest.

#Work
1 2016324
2 201572
3 201642
4 201937
5 201735
6 201832
7 201532
8 201830
9 201826
10 202026
11 201521
12 201519
13 201516
14 201615
15 201814
16 202312
17 201512
18 201711
19 201810
20 201810

About Utkarsh Raj

Utkarsh Raj is a scholar working on Molecular Biology, Computational Theory and Mathematics, Oncology, Hematology and Plant Science, having authored 52 papers that have together received 889 indexed citations. Recurring topics across this work include Computational Drug Discovery Methods (14 papers), Bioinformatics and Genomic Networks (6 papers), Chronic Myeloid Leukemia Treatments (5 papers), vaccines and immunoinformatics approaches (4 papers), HER2/EGFR in Cancer Research (3 papers), Epigenetics and DNA Methylation (3 papers), Machine Learning in Bioinformatics (3 papers) and Gene expression and cancer classification (3 papers). The work is most often cited by research in Computational Theory and Mathematics (198 citations), Pharmacology (52 citations), Molecular Biology (401 citations), Infectious Diseases (92 citations) and Toxicology (16 citations). Utkarsh Raj has collaborated with scholars based in India, Germany and China. Frequent co-authors include Pritish Kumar Varadwaj, Imlimaong Aier, Himansu Kumar, Saurabh Gupta, Aman Chandra Kaushik, Vikas Pruthi, Tara Chand Yadav, A. R. Rao, Shiri Freilich and Rahul Semwal. Their work appears in journals such as Journal of Biomolecular Structure and Dynamics, Scientific Reports, Current Topics in Medicinal Chemistry, Current Alzheimer Research and Frontiers in Plant Science.

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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