Balachandran Manavalan

8.1k total citations · 1 hit paper
130 papers, 6.3k citations indexed

About

Balachandran Manavalan is a scholar working on Molecular Biology, Microbiology and Immunology. According to data from OpenAlex, Balachandran Manavalan has authored 130 papers receiving a total of 6.3k indexed citations (citations by other indexed papers that have themselves been cited), including 100 papers in Molecular Biology, 18 papers in Microbiology and 12 papers in Immunology. Recurrent topics in Balachandran Manavalan's work include Machine Learning in Bioinformatics (72 papers), RNA and protein synthesis mechanisms (34 papers) and vaccines and immunoinformatics approaches (25 papers). Balachandran Manavalan is often cited by papers focused on Machine Learning in Bioinformatics (72 papers), RNA and protein synthesis mechanisms (34 papers) and vaccines and immunoinformatics approaches (25 papers). Balachandran Manavalan collaborates with scholars based in South Korea, Japan and Thailand. Balachandran Manavalan's co-authors include Gwang Lee, Tae Hwan Shin, Shaherin Basith, Md Mehedi Hasan, Leyi Wei, Watshara Shoombuatong, Sangdun Choi, Myeong Ok Kim, Hiroyuki Kurata and Phasit Charoenkwan and has published in prestigious journals such as Bioinformatics, PLoS ONE and Journal of Molecular Biology.

In The Last Decade

Balachandran Manavalan

124 papers receiving 6.3k citations

Hit Papers

align trajectories sort by overperformance

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.

MonkeyNet: A robust deep convolutional neural network for monkeypox disease detection and classification

2023 98 citations Balachandran Manavalan et al. profile →

Peers — A (Enhanced Table)

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

Name	h						Papers	Cites
Balachandran Manavalan South Korea	46	5.0k	967	671	516	394	130	6.3k
Jiangning Song Australia	57	8.0k 1.6×	725 0.7×	1.0k 1.5×	461 0.9×	523 1.3×	329	10.3k
Gwang Lee South Korea	49	5.2k 1.1×	678 0.7×	378 0.6×	608 1.2×	648 1.6×	159	8.7k
Tzong-Yi Lee Taiwan	40	3.7k 0.7×	513 0.5×	213 0.3×	367 0.7×	377 1.0×	119	4.9k
Shaherin Basith South Korea	33	2.4k 0.5×	417 0.4×	380 0.6×	566 1.1×	275 0.7×	57	3.6k
Jianyi Yang China	37	6.3k 1.3×	274 0.3×	1.5k 2.3×	541 1.0×	147 0.4×	114	8.6k
Leyi Wei China	48	6.8k 1.4×	870 0.9×	1.2k 1.8×	132 0.3×	756 1.9×	199	8.6k
Wei Chen China	72	15.5k 3.1×	771 0.8×	1.3k 2.0×	522 1.0×	1.8k 4.5×	483	18.4k
Watshara Shoombuatong Thailand	36	2.9k 0.6×	637 0.7×	683 1.0×	94 0.2×	112 0.3×	122	3.7k
Hui Ding China	51	7.5k 1.5×	407 0.4×	759 1.1×	133 0.3×	873 2.2×	135	8.2k
Tae Hwan Shin South Korea	29	2.5k 0.5×	480 0.5×	280 0.4×	139 0.3×	191 0.5×	58	3.3k

Countries citing papers authored by Balachandran Manavalan

Since Specialization

Citations

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

Fields of papers citing papers by Balachandran Manavalan

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Balachandran Manavalan

This figure shows the co-authorship network connecting the top 25 collaborators of Balachandran Manavalan. A scholar is included among the top collaborators of Balachandran Manavalan 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 Balachandran Manavalan. Balachandran Manavalan is excluded from the visualization to improve readability, since they are connected to all nodes in the network.

All Works

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20 of 20 papers shown

Li, Kaixiang, et al.. (2025). ERNIE-ac4C: A Novel Deep Learning Model for Effectively Predicting N4-acetylcytidine Sites. Journal of Molecular Biology. 437(6). 168978–168978. 2 indexed citations

Firoz, Ahmad, et al.. (2025). TP-ML: A Machine-Learning-Based Tool to Identify Threonine Proteases Using Sequence-Derived Optimal Features. PubMed. 22(3). 984–996.

Rakkiyappan, R., et al.. (2025). Cost-sensitive feature selection for multi-label classification: multi-criteria decision-making approach. Applied Computing and Informatics. 3 indexed citations

Charoenkwan, Phasit, et al.. (2024). M3S-ALG: Improved and robust prediction of allergenicity of chemical compounds by using a novel multi-step stacking strategy. Future Generation Computer Systems. 162. 107455–107455. 11 indexed citations

Kamli, Majid Rasool, Ahmed Atef, Sherif Edris, et al.. (2024). Computational prediction of phosphorylation sites of SARS-CoV-2 infection using feature fusion and optimization strategies. Methods. 229. 1–8. 3 indexed citations

Malik, Adeel, Majid Rasool Kamli, Jamal S. M. Sabir, et al.. (2024). APLpred: A machine learning-based tool for accurate prediction and characterization of asparagine peptide lyases using sequence-derived optimal features. Methods. 229. 133–146. 5 indexed citations

Cui, Yanyan, et al.. (2024). CODENET: A deep learning model for COVID-19 detection. Computers in Biology and Medicine. 171. 108229–108229. 11 indexed citations

Pham, Nhat Truong, et al.. (2024). MST-m6A: A Novel Multi-Scale Transformer-based Framework for Accurate Prediction of m6A Modification Sites Across Diverse Cellular Contexts. Journal of Molecular Biology. 437(6). 168856–168856. 1 indexed citations

Pham, Nhat Truong, et al.. (2024). Meta-2OM: A multi-classifier meta-model for the accurate prediction of RNA 2′-O-methylation sites in human RNA. PLoS ONE. 19(6). e0305406–e0305406. 6 indexed citations

10.

Basith, Shaherin, Balachandran Manavalan, & Gwang Lee. (2023). Unveiling local and global conformational changes and allosteric communications in SOD1 systems using molecular dynamics simulation and network analyses. Computers in Biology and Medicine. 168. 107688–107688. 3 indexed citations

11.

Pang, Chao, et al.. (2023). DrugormerDTI: Drug Graphormer for drug–target interaction prediction. Computers in Biology and Medicine. 161. 106946–106946. 15 indexed citations

12.

Fang, Xin, Qijin Xu, Shiqi Lin, et al.. (2023). Identification of SH2 domain-containing proteins and motifs prediction by a deep learning method. Computers in Biology and Medicine. 162. 107065–107065. 3 indexed citations

13.

Maeda, Kazuhiro, et al.. (2023). Stack-DHUpred: Advancing the accuracy of dihydrouridine modification sites detection via stacking approach. Computers in Biology and Medicine. 169. 107848–107848. 12 indexed citations

14.

Charoenkwan, Phasit, Wararat Chiangjong, Chanin Nantasenamat, et al.. (2022). SCMTHP: A New Approach for Identifying and Characterizing of Tumor-Homing Peptides Using Estimated Propensity Scores of Amino Acids. Pharmaceutics. 14(1). 122–122. 19 indexed citations

15.

Wei, Lesong, Xiucai Ye, Kai Zhang, et al.. (2022). SiameseCPP: a sequence-based Siamese network to predict cell-penetrating peptides by contrastive learning. Briefings in Bioinformatics. 24(1). 37 indexed citations

16.

He, Wenjia, Yi Jiang, Junru Jin, et al.. (2021). Accelerating bioactive peptide discovery via mutual information-based meta-learning. Briefings in Bioinformatics. 23(1). 45 indexed citations

17.

Wei, Leyi, Wenjia He, Adeel Malik, et al.. (2020). Computational prediction and interpretation of cell-specific replication origin sites from multiple eukaryotes by exploiting stacking framework. Briefings in Bioinformatics. 22(4). 118 indexed citations

18.

Manavalan, Balachandran, Shaherin Basith, Tae Hwan Shin, et al.. (2019). 4mCpred-EL: An Ensemble Learning Framework for Identification of DNA N4-Methylcytosine Sites in the Mouse Genome. Cells. 8(11). 1332–1332. 83 indexed citations

19.

Manavalan, Balachandran, Shaherin Basith, Tae Hwan Shin, Leyi Wei, & Gwang Lee. (2018). mAHTPred: a sequence-based meta-predictor for improving the prediction of anti-hypertensive peptides using effective feature representation. Bioinformatics. 35(16). 2757–2765. 221 indexed citations

20.

Govindaraj, Rajiv Gandhi, Balachandran Manavalan, Shaherin Basith, & Sangdun Choi. (2011). Comparative Analysis of Species-Specific Ligand Recognition in Toll-Like Receptor 8 Signaling: A Hypothesis. PLoS ONE. 6(9). e25118–e25118. 44 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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