H-Index & Metrics Top Publications

H-Index & Metrics

Discipline name H-index Citations Publications World Ranking National Ranking
Biology and Biochemistry H-index 70 Citations 18,114 257 World Ranking 3061 National Ranking 5

Overview

What is he best known for?

The fields of study he is best known for:

  • Gene
  • Enzyme
  • DNA

His primary scientific interests are in Computational biology, Support vector machine, Bioinformatics, Epitope and Peptide sequence. His studies deal with areas such as Genetics, Small molecule, Genome project, In silico and RNA-binding protein as well as Computational biology. His work carried out in the field of Support vector machine brings together such families of science as Amino acid, Protein sequencing, Subcellular localization and Dipeptide composition.

His Amino acid study deals with Peptide intersecting with Drug. As a member of one scientific family, Gajendra P. S. Raghava mostly works in the field of Epitope, focusing on MHC class II and, on occasion, HLA-DRB1. His Peptide sequence study incorporates themes from Protein structure, DNA microarray, Plasma protein binding and Data mining.

His most cited work include:

  • ProPred: prediction of HLA-DR binding sites (744 citations)
  • Prediction of continuous B-cell epitopes in an antigen using recurrent neural network. (666 citations)
  • AlgPred: prediction of allergenic proteins and mapping of IgE epitopes. (381 citations)

What are the main themes of his work throughout his whole career to date?

Gajendra P. S. Raghava mostly deals with Computational biology, Bioinformatics, Peptide, Genetics and Database. The Computational biology study combines topics in areas such as Support vector machine, Epitope, Antigen, Peptide sequence and In silico. He has researched Support vector machine in several fields, including Nearest neighbor search and Dipeptide composition.

The various areas that he examines in his Peptide sequence study include Sequence analysis and Binding site. In Bioinformatics, Gajendra P. S. Raghava works on issues like Web server, which are connected to Data mining. His biological study spans a wide range of topics, including Amino acid, Protein tertiary structure and Drug delivery.

He most often published in these fields:

  • Computational biology (38.58%)
  • Bioinformatics (14.84%)
  • Peptide (17.51%)

What were the highlights of his more recent work (between 2018-2021)?

  • Computational biology (38.58%)
  • Peptide (17.51%)
  • Immune system (5.64%)

In recent papers he was focusing on the following fields of study:

The scientist’s investigation covers issues in Computational biology, Peptide, Immune system, Receiver operating characteristic and Oncology. His Computational biology study combines topics from a wide range of disciplines, such as In silico, Gene, A protein, Epitope and Survival analysis. His work deals with themes such as Amino acid composition, Support vector machine and Genomics, which intersect with In silico.

His Epitope research includes themes of Oncovirus and Basic Local Alignment Search Tool. Gajendra P. S. Raghava has included themes like Amino acid, Nucleic acid, Docking and Autodock vina in his Peptide study. His work carried out in the field of Amino acid brings together such families of science as Immunosuppression and Protein tertiary structure.

Between 2018 and 2021, his most popular works were:

  • Benchmarking of different molecular docking methods for protein-peptide docking. (29 citations)
  • Benchmarking of different molecular docking methods for protein-peptide docking. (29 citations)
  • HumCFS: a database of fragile sites in human chromosomes (25 citations)

In his most recent research, the most cited papers focused on:

  • Gene
  • Enzyme
  • DNA

His scientific interests lie mostly in Computational biology, Peptide, Receiver operating characteristic, DNA microarray and In silico. His Computational biology research includes elements of Small peptide, Protein secondary structure prediction, Dipeptide, Posttranslational modification and Complete protein. His study in Peptide is interdisciplinary in nature, drawing from both Amino acid, DNA, A protein and Ligand.

His Receiver operating characteristic research also works with subjects such as

  • Matthews correlation coefficient which connect with Primary sequence, Algorithm and Data set,
  • Transcriptome which connect with CpG site, Epigenomics, Genomics, DNA methylation and Methylation. Gajendra P. S. Raghava interconnects Nucleic acid, Docking, Autodock vina and Human genome in the investigation of issues within DNA microarray. His In silico research is multidisciplinary, incorporating perspectives in Gene expression, Gene regulatory network, Pancreatic cancer, Copy-number variation and Support vector machine.

This overview was generated by a machine learning system which analysed the scientist’s body of work. If you have any feedback, you can contact us here.

Top Publications

Prediction of continuous B-cell epitopes in an antigen using recurrent neural network.

Sudipto Saha;G. P. S. Raghava.
Proteins (2006)

980 Citations

ProPred: prediction of HLA-DR binding sites

Harpreet Singh;G. P. S. Raghava.
Bioinformatics (2001)

910 Citations

AlgPred: prediction of allergenic proteins and mapping of IgE epitopes.

Sudipto Saha;G. P. S. Raghava.
Nucleic Acids Research (2006)

480 Citations

In Silico Approach for Predicting Toxicity of Peptides and Proteins

Sudheer Gupta;Pallavi Kapoor;Kumardeep Chaudhary;Ankur Gautam.
PLOS ONE (2013)

416 Citations

ProPred1: prediction of promiscuous MHC Class-I binding sites.

Harpreet Singh;G.P.S. Raghava.
Bioinformatics (2003)

409 Citations

BcePred: Prediction of Continuous B-Cell Epitopes in Antigenic Sequences Using Physico-chemical Properties

Sudipto Saha;G. P. S. Raghava.
international conference on artificial immune systems (2004)

377 Citations

ESLpred: SVM-based method for subcellular localization of eukaryotic proteins using dipeptide composition and PSI-BLAST

Manoj Bhasin;G. P. S. Raghava.
Nucleic Acids Research (2004)

373 Citations

Prediction of CTL epitopes using QM, SVM and ANN techniques.

Manoj Bhasin;G.P.S. Raghava.
Vaccine (2004)

356 Citations

Support Vector Machine-based Method for Subcellular Localization of Human Proteins Using Amino Acid Compositions, Their Order, and Similarity Search

Aarti Garg;Manoj Bhasin;Gajendra P.S. Raghava.
Journal of Biological Chemistry (2005)

282 Citations

Prediction of RNA binding sites in a protein using SVM and PSSM profile

Manish Kumar;M. Michael Gromiha;G. P. S. Raghava.
Proteins (2008)

272 Citations

Profile was last updated on December 6th, 2021.
Research.com Ranking is based on data retrieved from the Microsoft Academic Graph (MAG).
The ranking h-index is inferred from publications deemed to belong to the considered discipline.

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