Ajit Gupta

431 total citations
26 papers, 276 citations indexed

About

Ajit Gupta is a scholar working on Molecular Biology, Plant Science and Genetics. According to data from OpenAlex, Ajit Gupta has authored 26 papers receiving a total of 276 indexed citations (citations by other indexed papers that have themselves been cited), including 11 papers in Molecular Biology, 11 papers in Plant Science and 4 papers in Genetics. Recurrent topics in Ajit Gupta's work include Genomics and Phylogenetic Studies (8 papers), Machine Learning in Bioinformatics (6 papers) and Plant Molecular Biology Research (5 papers). Ajit Gupta is often cited by papers focused on Genomics and Phylogenetic Studies (8 papers), Machine Learning in Bioinformatics (6 papers) and Plant Molecular Biology Research (5 papers). Ajit Gupta collaborates with scholars based in India, United States and Canada. Ajit Gupta's co-authors include Amar Nath Pandey, Prabina Kumar Meher, Ashok Shukla, O. P. Chaturvedi, Anil Kumar, Pramod Kumar, A. K. Paul, A. R. Rao, Rajender Parsad and Ranjit Kumar Paul and has published in prestigious journals such as SHILAP Revista de lepidopterología, PLoS ONE and International Journal of Molecular Sciences.

In The Last Decade

Ajit Gupta

23 papers receiving 262 citations

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
Ajit Gupta India 10 139 81 52 27 22 26 276
Thomas G. Jephcott Australia 6 152 1.1× 108 1.3× 233 4.5× 18 0.7× 5 0.2× 6 410
Alessandro Craparo Vietnam 7 211 1.5× 42 0.5× 48 0.9× 3 0.1× 5 0.2× 9 490
K. Chandran India 9 223 1.6× 81 1.0× 29 0.6× 8 0.3× 1 0.0× 63 354
Seon‐Ae Kim South Korea 8 69 0.5× 33 0.4× 35 0.7× 4 0.1× 17 0.8× 26 275
S. Ganeshanandam Australia 6 127 0.9× 27 0.3× 36 0.7× 11 0.4× 1 0.0× 8 346
Bowen Xue China 9 173 1.2× 31 0.4× 90 1.7× 5 0.2× 15 0.7× 42 309
Renata Corrêa Martins Brazil 8 59 0.4× 41 0.5× 15 0.3× 9 0.4× 16 158
Mathias Dillen Belgium 10 176 1.3× 64 0.8× 57 1.1× 1 0.0× 3 0.1× 28 387
Achim Kunz Germany 13 504 3.6× 191 2.4× 19 0.4× 2 0.1× 5 0.2× 39 560

Countries citing papers authored by Ajit Gupta

Since Specialization
Citations

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

Fields of papers citing papers by Ajit Gupta

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Ajit Gupta

This figure shows the co-authorship network connecting the top 25 collaborators of Ajit Gupta. A scholar is included among the top collaborators of Ajit Gupta 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 Ajit Gupta. Ajit Gupta 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.
Meher, Prabina Kumar, et al.. (2025). Ensemble of Bayesian alphabets via constraint weight optimization strategy improves genomic prediction accuracy. G3 Genes Genomes Genetics. 15(9).
2.
Ansari, Anees A., et al.. (2024). Microstructure, dielectric and ferroelectric properties of CaCu3-xZnxTi4-xCexO12 ceramics prepared via semi-wet route. Processing and Application of Ceramics. 18(1). 117–122. 2 indexed citations
3.
Meher, Prabina Kumar, et al.. (2024). PredPSP: a novel computational tool to discover pathway-specific photosynthetic proteins in plants. Plant Molecular Biology. 114(5). 106–106. 1 indexed citations
4.
Gupta, Ajit, et al.. (2024). ASPTF: A computational tool to predict abiotic stress-responsive transcription factors in plants by employing machine learning algorithms. Biochimica et Biophysica Acta (BBA) - General Subjects. 1868(6). 130597–130597. 3 indexed citations
5.
Gupta, Ajit, et al.. (2024). AScirRNA: A novel computational approach to discover abiotic stress-responsive circular RNAs in plant genome. Computational Biology and Chemistry. 113. 108205–108205.
6.
Gupta, Ajit, et al.. (2024). RBProkCNN: Deep learning on appropriate contextual evolutionary information for RNA binding protein discovery in prokaryotes. Computational and Structural Biotechnology Journal. 23. 1631–1640. 3 indexed citations
7.
Meher, Prabina Kumar, et al.. (2024). ProkDBP: Toward more precise identification of prokaryoticDNAbinding proteins. Protein Science. 33(6). e5015–e5015. 2 indexed citations
8.
Meher, Prabina Kumar, et al.. (2023). ASLncR: a novel computational tool for prediction of abiotic stress-responsive long non-coding RNAs in plants. Functional & Integrative Genomics. 23(2). 113–113. 9 indexed citations
9.
Meher, Prabina Kumar, Ajit Gupta, Sachin Rustgi, et al.. (2023). Evaluation of eight Bayesian genomic prediction models for three micronutrient traits in bread wheat ( Triticum aestivum L.). The Plant Genome. 16(4). e20332–e20332. 1 indexed citations
10.
Meher, Prabina Kumar, et al.. (2023). DBPMod: a supervised learning model for computational recognition of DNA-binding proteins in model organisms. Briefings in Functional Genomics. 23(4). 363–372. 4 indexed citations
11.
Meher, Prabina Kumar, et al.. (2023). RBPLight: a computational tool for discovery of plant-specific RNA-binding proteins using light gradient boosting machine and ensemble of evolutionary features. Briefings in Functional Genomics. 22(5). 401–410. 7 indexed citations
12.
Meher, Prabina Kumar, et al.. (2023). ASmiR: a machine learning framework for prediction of abiotic stress–specific miRNAs in plants. Functional & Integrative Genomics. 23(2). 92–92. 15 indexed citations
13.
Meher, Prabina Kumar, et al.. (2023). SVM-Root: Identification of Root-Associated Proteins in Plants byEmploying the Support Vector Machine with Sequence-Derived Features. Current Bioinformatics. 19(1). 91–102. 14 indexed citations
14.
Meher, Prabina Kumar, Tanmaya Kumar Sahu, Ajit Gupta, Anuj Kumar, & Sachin Rustgi. (2022). ASRpro: A machine‐learning computational model for identifying proteins associated with multiple abiotic stress in plants. The Plant Genome. 17(1). e20259–e20259. 10 indexed citations
15.
Paul, Ranjit Kumar, et al.. (2022). Machine learning techniques for forecasting agricultural prices: A case of brinjal in Odisha, India. PLoS ONE. 17(7). e0270553–e0270553. 46 indexed citations
16.
Meher, Prabina Kumar, et al.. (2022). PlDBPred: a novel computational model for discovery of DNA binding proteins in plants. Briefings in Bioinformatics. 24(1). 16 indexed citations
17.
Gupta, Ajit & P. K. Singh. (2022). How has evolution of corporate governance practices of banks in India affected quality of their financial reporting. International Journal of Indian Culture and Business Management. 26(1). 82–82. 1 indexed citations
18.
Gupta, Ajit, et al.. (2010). Salinity tolerance of Avicennia marina (Forssk.) Vierh. from Gujarat coasts of India. Aquatic Botany. 93(1). 9–16. 43 indexed citations
19.
Shukla, Ashok, et al.. (2008). Effects of shade on arbuscular mycorrhizal colonization and growth of crops and tree seedlings in Central India. Agroforestry Systems. 76(1). 95–109. 38 indexed citations
20.
Gupta, Ajit. (2002). Tracheo Oesophageal Fistula Oesophageal Atresia And Anaesthetic Management.. SHILAP Revista de lepidopterología.

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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