Gajendra P. S. Raghava

30.2k total citations · 8 hit papers
333 papers, 21.2k citations indexed

About

Gajendra P. S. Raghava is a scholar working on Molecular Biology, Immunology and Radiology, Nuclear Medicine and Imaging. According to data from OpenAlex, Gajendra P. S. Raghava has authored 333 papers receiving a total of 21.2k indexed citations (citations by other indexed papers that have themselves been cited), including 299 papers in Molecular Biology, 49 papers in Immunology and 46 papers in Radiology, Nuclear Medicine and Imaging. Recurrent topics in Gajendra P. S. Raghava's work include Machine Learning in Bioinformatics (106 papers), vaccines and immunoinformatics approaches (97 papers) and RNA and protein synthesis mechanisms (63 papers). Gajendra P. S. Raghava is often cited by papers focused on Machine Learning in Bioinformatics (106 papers), vaccines and immunoinformatics approaches (97 papers) and RNA and protein synthesis mechanisms (63 papers). Gajendra P. S. Raghava collaborates with scholars based in India, United States and United Kingdom. Gajendra P. S. Raghava's co-authors include Sudipto Saha, Manoj Bhasin, Kumardeep Chaudhary, Ankur Gautam, Harpreet Singh, Rahul Kumar, Sudheer Gupta, Pallavi Kapoor, Sandeep Kumar Dhanda and Sandeep Singh and has published in prestigious journals such as Nucleic Acids Research, Journal of Biological Chemistry and SHILAP Revista de lepidopterología.

In The Last Decade

Gajendra P. S. Raghava

325 papers receiving 20.7k citations

Hit Papers

In Silico Approach for Predicting Toxicity o... 2001 2026 2009 2017 2013 2006 2001 2013 2006 400 800 1.2k
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.

  1. In Silico Approach for Predicting Toxicity of Peptides and Proteins
    2013 1.4k citations Gajendra P. S. Raghava et al. profile →
  2. Prediction of continuous B‐cell epitopes in an antigen using recurrent neural network
    2006 1.2k citations Sudipto Saha, Gajendra P. S. Raghava profile →
  3. ProPred: prediction of HLA-DR binding sites
    2001 705 citations Gajendra P. S. Raghava et al. profile →
  4. Designing of interferon-gamma inducing MHC class-II binders
    2013 602 citations Gajendra P. S. Raghava et al. profile →
  5. AlgPred: prediction of allergenic proteins and mapping of IgE epitopes
    2006 589 citations Sudipto Saha, Gajendra P. S. Raghava Nucleic Acids Research profile →
  6. THPdb: Database of FDA-approved peptide and protein therapeutics
    2017 365 citations Gajendra P. S. Raghava et al. profile →
  7. ToxinPred2: an improved method for predicting toxicity of proteins
    2022 168 citations Neelam Sharma, Gajendra P. S. Raghava et al. Briefings in Bioinformatics profile →
  8. ToxinPred 3.0: An improved method for predicting the toxicity of peptides
    2024 78 citations Shubham Choudhury, Akanksha Arora et al. Computers in Biology and Medicine profile →

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
Gajendra P. S. Raghava India 73 18.0k 3.2k 3.2k 3.2k 2.1k 333 21.2k
Alexandre M. J. J. Bonvin Netherlands 68 17.6k 1.0× 1.7k 0.5× 1.1k 0.4× 1.8k 0.6× 1.5k 0.7× 265 22.5k
Darren R. Flower United Kingdom 53 9.4k 0.5× 3.4k 1.1× 577 0.2× 2.8k 0.9× 1.6k 0.8× 194 13.7k
Ole Lund Denmark 83 18.3k 1.0× 8.0k 2.5× 1.7k 0.5× 4.9k 1.5× 5.8k 2.8× 324 36.2k
Morten Nielsen Denmark 73 19.3k 1.1× 12.1k 3.8× 1.1k 0.3× 7.3k 2.3× 3.7k 1.8× 278 25.8k
Irini Doytchinova Bulgaria 38 6.3k 0.3× 2.2k 0.7× 505 0.2× 2.3k 0.7× 1.5k 0.7× 145 8.2k
Bjoern Peters United States 82 18.7k 1.0× 14.0k 4.4× 1.1k 0.3× 6.4k 2.0× 8.9k 4.3× 355 32.3k
Anne Imberty France 74 11.7k 0.7× 2.4k 0.7× 906 0.3× 1.7k 0.5× 459 0.2× 371 18.0k
Jimmy K. Eng United States 69 19.5k 1.1× 3.0k 0.9× 644 0.2× 639 0.2× 654 0.3× 144 27.6k
Elisabeth Gasteiger Switzerland 29 9.9k 0.5× 1.3k 0.4× 587 0.2× 685 0.2× 759 0.4× 39 14.4k
Gabriel Studer Switzerland 17 9.5k 0.5× 1.1k 0.4× 368 0.1× 505 0.2× 1.5k 0.7× 22 15.2k

Countries citing papers authored by Gajendra P. S. Raghava

Since Specialization
Citations

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

Fields of papers citing papers by Gajendra P. S. Raghava

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Gajendra P. S. Raghava. 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 Gajendra P. S. Raghava. The network helps show where Gajendra P. S. Raghava may publish in the future.

Co-authorship network of co-authors of Gajendra P. S. Raghava

This figure shows the co-authorship network connecting the top 25 collaborators of Gajendra P. S. Raghava. A scholar is included among the top collaborators of Gajendra P. S. Raghava 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 Gajendra P. S. Raghava. Gajendra P. S. Raghava 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
1.
Choudhury, Shubham, et al.. (2025). Prediction of hemolytic peptides and their hemolytic concentration. Communications Biology. 8(1). 176–176. 9 indexed citations
2.
Choudhury, Shubham, et al.. (2024). ToxinPred 3.0: An improved method for predicting the toxicity of peptides. Computers in Biology and Medicine. 179. 108926–108926. 78 indexed citations breakdown →
3.
Patiyal, Sumeet, et al.. (2024). HLA-DR4Pred2: An improved method for predicting HLA-DRB1*04:01 binders. Methods. 232. 18–28. 2 indexed citations
4.
Nagamani, Selvaraman, Asheesh Kumar, N. Arul Murugan, et al.. (2024). Molecular Property Diagnostic Suite for COVID-19 (MPDSCOVID-19): an open-source disease-specific drug discovery portal. SHILAP Revista de lepidopterología. 2024. 1–17. 2 indexed citations
5.
Arora, Akanksha, et al.. (2023). SalivaDB—a comprehensive database for salivary biomarkers in humans. Database. 2023. 9 indexed citations
6.
Dhall, Anjali, Sumeet Patiyal, & Gajendra P. S. Raghava. (2022). HLAncPred: a method for predicting promiscuous non-classical HLA binding sites. Briefings in Bioinformatics. 23(5). 9 indexed citations
7.
Dhall, Anjali, et al.. (2022). In silico method for predicting infectious strains of influenza A virus from its genome and protein sequences. Journal of General Virology. 103(11). 1 indexed citations
8.
Patiyal, Sumeet, Anjali Dhall, & Gajendra P. S. Raghava. (2022). A deep learning-based method for the prediction of DNA interacting residues in a protein. Briefings in Bioinformatics. 23(5). 18 indexed citations
9.
Patiyal, Sumeet, et al.. (2022). Prediction of RNA-interacting residues in a protein using CNN and evolutionary profile. Briefings in Bioinformatics. 24(1). 12 indexed citations
10.
Kaur, Harpreet, et al.. (2021). ProCanBio: A Database of Manually Curated Biomarkers for Prostate Cancer. Journal of Computational Biology. 28(12). 1248–1257. 1 indexed citations
11.
Agrawal, Piyush, et al.. (2020). AntiCP 2.0: an updated model for predicting anticancer peptides. Briefings in Bioinformatics. 22(3). 210 indexed citations
12.
Sharma, Neelam, Sumeet Patiyal, Anjali Dhall, et al.. (2020). AlgPred 2.0: an improved method for predicting allergenic proteins and mapping of IgE epitopes. Briefings in Bioinformatics. 22(4). 211 indexed citations
13.
Bhalla, Sherry, Harpreet Kaur, Anjali Dhall, & Gajendra P. S. Raghava. (2019). Prediction and Analysis of Skin Cancer Progression using Genomics Profiles of Patients. Scientific Reports. 9(1). 15790–15790. 52 indexed citations
14.
Vikram, Surendra, Janmejay Pandey, Gunjan Pandey, et al.. (2012). Branching of the p-nitrophenol (PNP) degradation pathway in burkholderia sp. Strain SJ98: Evidences from genetic characterization of PNP gene cluster. AMB Express. 2(1). 30–30. 28 indexed citations
15.
Lata, Sneh, Manoj Bhasin, & Gajendra P. S. Raghava. (2009). MHCBN 4.0: A database of MHC/TAP binding peptides and T-cell epitopes. BMC Research Notes. 2(1). 61–61. 72 indexed citations
16.
Ansari, Hifzur Rahman, Darren R. Flower, & Gajendra P. S. Raghava. (2009). AntigenDB: an immunoinformatics database of pathogen antigens. Nucleic Acids Research. 38(suppl_1). D847–D853. 62 indexed citations
17.
Vivona, Sandro, Jennifer L. Gardy, Srinivasan Ramachandran, et al.. (2008). Computer-aided biotechnology: from immuno-informatics to reverse vaccinology. Trends in biotechnology. 26(4). 190–200. 81 indexed citations
18.
Saha, Sudipto & Gajendra P. S. Raghava. (2007). Predicting Virulence Factors of Immunological Interest. Methods in molecular biology. 409. 407–415. 1 indexed citations
19.
Raghava, Gajendra P. S., et al.. (2003). OXBench: A benchmark for evaluation of protein multiple sequence alignment accuracy. BMC Bioinformatics. 4(1). 47–47. 139 indexed citations
20.
Bhasin, Manoj & Gajendra P. S. Raghava. (2003). Prediction of Promiscuous and High-Affinity Mutated MHC Binders. PubMed. 22(4). 229–234. 28 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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