Jayaraman Valadi

402 total citations
26 papers, 213 citations indexed

About

Jayaraman Valadi is a scholar working on Molecular Biology, Microbiology and Artificial Intelligence. According to data from OpenAlex, Jayaraman Valadi has authored 26 papers receiving a total of 213 indexed citations (citations by other indexed papers that have themselves been cited), including 12 papers in Molecular Biology, 5 papers in Microbiology and 4 papers in Artificial Intelligence. Recurrent topics in Jayaraman Valadi's work include Machine Learning in Bioinformatics (9 papers), vaccines and immunoinformatics approaches (6 papers) and Antimicrobial Peptides and Activities (5 papers). Jayaraman Valadi is often cited by papers focused on Machine Learning in Bioinformatics (9 papers), vaccines and immunoinformatics approaches (6 papers) and Antimicrobial Peptides and Activities (5 papers). Jayaraman Valadi collaborates with scholars based in India, France and Sri Lanka. Jayaraman Valadi's co-authors include Deepak Sehgal, Aamod Sane, Prashanth Suravajhala, Vijayaraghava Seshadri Sundararajan, Urmila Kulkarni‐Kale, P. Das, Patrick Siarry, Mayur Pandya, C. Ravishankar and P. B. Kavi Kishor and has published in prestigious journals such as SHILAP Revista de lepidopterología, Scientific Reports and Industrial & Engineering Chemistry Research.

In The Last Decade

Jayaraman Valadi

24 papers receiving 210 citations

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
Jayaraman Valadi India 7 66 45 32 28 27 26 213
Georges Hattab Germany 10 153 2.3× 36 0.8× 9 0.3× 35 1.3× 8 0.3× 40 286
Abhijit Kulkarni India 6 100 1.5× 44 1.0× 101 3.2× 21 0.8× 9 0.3× 16 295
Chenchen Wu China 9 77 1.2× 46 1.0× 12 0.4× 14 0.5× 37 1.4× 27 250
Zhicheng Wu China 6 169 2.6× 19 0.4× 13 0.4× 41 1.5× 7 0.3× 25 349
Boyang Xia China 5 40 0.6× 81 1.8× 14 0.4× 6 0.2× 4 0.1× 9 246
Rongchao Zhang China 11 58 0.9× 6 0.1× 77 2.4× 7 0.3× 8 0.3× 32 374
Sophia Ulonska Austria 8 273 4.1× 7 0.2× 97 3.0× 14 0.5× 5 0.2× 8 396
Xinyu Lu China 8 91 1.4× 10 0.2× 6 0.2× 8 0.3× 5 0.2× 24 296
Zahra Mohammadi Iran 8 116 1.8× 52 1.2× 9 0.3× 8 0.3× 2 0.1× 11 320
Jingyu Shao Australia 6 66 1.0× 37 0.8× 18 0.6× 18 0.6× 1 0.0× 16 230

Countries citing papers authored by Jayaraman Valadi

Since Specialization
Citations

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

Fields of papers citing papers by Jayaraman Valadi

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Jayaraman Valadi

This figure shows the co-authorship network connecting the top 25 collaborators of Jayaraman Valadi. A scholar is included among the top collaborators of Jayaraman Valadi 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 Jayaraman Valadi. Jayaraman Valadi 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.
Sharma, Amita, et al.. (2025). Generative Artificial Intelligence for Virology. Methods in molecular biology. 2927. 195–220.
2.
3.
Sane, Aamod, et al.. (2024). Protein feature engineering framework for AMPylation site prediction. Scientific Reports. 14(1). 8695–8695. 2 indexed citations
4.
Valadi, Jayaraman, et al.. (2024). Uncovering blood–brain barrier permeability: a comparative study of machine learning models using molecular fingerprints, and SHAP explainability. SAR and QSAR in environmental research. 35(12). 1155–1171. 3 indexed citations
5.
Valadi, Jayaraman, et al.. (2024). Advanced Machine Learning with Evolutionary and Metaheuristic Techniques. 1 indexed citations
6.
Valadi, Jayaraman, et al.. (2023). PandoraGAN: Generating Antiviral Peptides Using Generative Adversarial Network. SN Computer Science. 4(5). 26 indexed citations
7.
Shukla, Nidhi, et al.. (2023). Identification of Plausible Candidates in Prostate Cancer Using IntegratedMachine Learning Approaches. Current Genomics. 24(5). 287–306. 3 indexed citations
8.
9.
Polavarapu, Rathnagiri, et al.. (2022). Machine Learning Heuristics on Gingivobuccal Cancer Gene Datasets Reveals Key Candidate Attributes for Prognosis. Genes. 13(12). 2379–2379. 3 indexed citations
10.
Abbasi, Bilal Ahmed, et al.. (2022). In Silico Characterization of Uncharacterized Proteins From Multiple Strains of Clostridium Difficile. Frontiers in Genetics. 13. 878012–878012. 3 indexed citations
11.
Valadi, Jayaraman, et al.. (2021). Antibody Class(es) Predictor for Epitopes (AbCPE): A Multi-Label Classification Algorithm. SHILAP Revista de lepidopterología. 1. 709951–709951. 6 indexed citations
12.
Valadi, Jayaraman, et al.. (2020). Random Forest and Autoencoder Data-Driven Models for Prediction of Dispersed-Phase Holdup and Drop Size in Rotating Disc Contactors. Industrial & Engineering Chemistry Research. 60(1). 425–435. 14 indexed citations
13.
14.
Valadi, Jayaraman, et al.. (2020). Twin and Multiple Black Holes Algorithm for Feature Selection. 3 2. 1–6. 1 indexed citations
15.
16.
Sehgal, Deepak, et al.. (2017). Recent trends in antimicrobial peptide prediction using machine learning techniques. Bioinformation. 13(12). 415–416. 9 indexed citations
17.
Das, P., et al.. (2016). Data Mining and Pattern Recognition Models for Identifying Inherited Diseases: Challenges and Implications. Frontiers in Genetics. 7. 136–136. 6 indexed citations
18.
Sehgal, Deepak, et al.. (2016). Classification of anti hepatitis peptides using Support Vector Machine with hybrid Ant Colony Optimization. Bioinformation. 12(1). 12–14. 3 indexed citations
19.
Valadi, Jayaraman, et al.. (2014). Applications of Metaheuristics in Process Engineering. HAL (Le Centre pour la Communication Scientifique Directe). 73 indexed citations
20.
Valadi, Jayaraman, et al.. (2013). Hybrid feature selection and peptide binding affinity prediction using an EDA based algorithm. 3559. 2384–2389. 8 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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