Computational Biology and Bioinformatics Laboratory
Below are a selection of our recent publications in top-tier academic journals.
ac4C-AFL: A high-precision identification of human mRNA N4-acetylcytidine sites based on adaptive feature representation learning
Nhat Truong Pham, Annie Terrina Terrance, Young-Jun Jeon, Rajan Rakkiyappan, Balachandran Manavalan
H2Opred: a robust and efficient hybrid deep learning model for predicting 2'-O-methylation sites in human RNA
Nhat Truong Pham, Rajan Rakkiyapan, Jongsun Park, Adeel Malik, Balachandran Manavalan
Advancing the accuracy of SARS-CoV-2 phosphorylation site detection via meta-learning approach
Nhat Truong Pham, Le Thi Phan, Jimin Seo, Yeonwoo Kim, Minkyung Song, Sukchan Lee, Young-Jun Jeon, Balachandran Manavalan
How well does a data-driven prediction method distinguish dihydrouridine from tRNA and mRNA?
Shaherin Basith, Balachandran Manavalan
SiameseCPP: a sequence-based Siamese network to predict cell-penetrating peptides by contrastive learning.
Xin Zhang, Lesong Wei, Xiucai Ye, Kai Zhang, Saisai Teng, Zhongshen Li, Junru Jin, Min Jae Kim, Tetsuya Sakurai, Lizhen Cui, Balachandran Manavalan, Leyi Wei
An Effective Integrated Machine Learning Framework for Identifying Severity of Tomato Yellow Leaf Curl Virus and Their Experimental Validation
Nattanong Bupi, Vinoth Kumar Sangaraju, Le Thi Phan, Aamir Lal, Thuy Thi Bich Vo, Phuong Thi Ho, Muhammad Amir Qureshi, Marjia Tabassum, Sukchan Lee, Balachandran Manavalan
Deepm5C: A deep-learning-based hybrid framework for identifying human RNA N5-methylcytosine sites using a stacking strategy.
Md Mehedi Hasan, Sho Tsukiyama, Jae Youl Cho, Hiroyuki Kurata, Md Ashad Alam, Xiaowen Liu, Balachandran Manavalan, Hong-Wen Deng
iACVP: markedly enhanced identification of anti-coronavirus peptides using a dataset-specific word2vec model.
Hiroyuki Kurata, Sho Tsukiyama, Balachandran Manavalan
TACOS: a novel approach for accurate prediction of cell-specific long noncoding RNAs subcellular localization.
Young-Jun Jeon, Md Mehedi Hasan, Hyun Woo Park, Ki Wook Lee, Balachandran Manavalan
Comparative analysis of machine learning-based approaches for identifying therapeutic peptides targeting SARS-CoV-2.
Manavalan B, Basith S, Lee G.
Accelerating bioactive peptide discovery via mutual information-based meta-learning.
He W, Jiang Y, Jin J, Li Z, Zhao J, Manavalan B, Su R, Gao X, Wei L.
STALLION: a stacking-based ensemble learning framework for prokaryotic lysine acetylation site prediction.
Basith S, Lee G, Manavalan B.
Integrative machine learning framework for the identification of cell-specific enhancers from the human genome.
Basith S, Hasan MM, Lee G, Wei L, Manavalan B.
StackIL6: a stacking ensemble model for improving the prediction of IL-6 inducing peptides.
Charoenkwan P, Chiangjong W, Nantasenamat C, Hasan MM, Manavalan B, Shoombuatong W.
NeuroPred-FRL: an interpretable prediction model for identifying neuropeptide using feature representation learning.
Hasan MM, Alam MA, Shoombuatong W, Deng HW, Manavalan B, Kurata H.
Computational prediction of species-specific yeast DNA replication origin via iterative feature representation.
Manavalan B, Basith S, Shin TH, Lee G.
Computational prediction and interpretation of cell-specific replication origin sites from multiple eukaryotes by exploiting stacking framework.
Wei L, He W, Malik A, Su R, Cui L, Manavalan B.
Meta-i6mA: an interspecies predictor for identifying DNA N6-methyladenine sites of plant genomes by exploiting informative features in an integrative machine-learning framework.
Hasan MM, Basith S, Khatun MS, Lee G, Manavalan B, Kurata H.
BERT4Bitter: a bidirectional encoder representations from transformers (BERT)-based model for improving the prediction of bitter peptides.
Charoenkwan P, Nantasenamat C, Hasan MM, Manavalan B, Shoombuatong W.
Empirical Comparison and Analysis of Web-Based DNA N (4)-Methylcytosine Site Prediction Tools.
Manavalan B, Hasan MM, Basith S, Gosu V, Shin TH, Lee G.
HLPpred-Fuse: improved and robust prediction of hemolytic peptide and its activity by fusing multiple feature representation.
Hasan MM, Schaduangrat N, Basith S, Lee G, Shoombuatong W, Manavalan B.
Empirical comparison and analysis of web-based cell-penetrating peptide prediction tools.
Su R, Hu J, Zou Q, Manavalan B, Wei L.
SDM6A: A Web-Based Integrative Machine-Learning Framework for Predicting 6mA Sites in the Rice Genome.
Basith S, Manavalan B, Shin TH, Lee G.
Iterative feature representations improve N4-methylcytosine site prediction.
Wei L, Su R, Luan S, Liao Z, Manavalan B, Zou Q, Shi X.
mAHTPred: a sequence-based meta-predictor for improving the prediction of anti-hypertensive peptides using effective feature representation.
Manavalan B, Basith S, Shin TH, Wei L, Lee G.
Meta-4mCpred: A Sequence-Based Meta-Predictor for Accurate DNA 4mC Site Prediction Using Effective Feature Representation.
Manavalan B, Basith S, Shin TH, Wei L, Lee G.
SVMQA: support-vector-machine-based protein single-model quality assessment.
Manavalan B, Lee J.
HOTGpred: Enhancing human O-linked threonine glycosylation prediction using integrated pretrained protein language model-based features and multi-stage feature selection approach
Nhat Truong Pham, Ying Zhang, Rajan Rakkiyappan, Balachandran Manavalan
APLpred: A machine learning-based tool for accurate prediction and characterization of asparagine peptide lyases using sequence-derived optimal features
Adeel Malik, Majid Rasool Kamli, Jamal S M Sabir, Irfan Ahmad Rather, Le Thi Phan, Chang-Bae Kim, Balachandran Manavalan
Meta-2OM: A multi-classifier meta-model for the accurate prediction of RNA 2'-O-methylation sites in human RNA
Md Harun-Or-Roshid, Nhat Truong Pham, Balachandran Manavalan, Hiroyuki Kurata
SEP-AlgPro: An efficient allergen prediction tool utilizing traditional machine learning and deep learning techniques with protein language model features
Shaherin Basith, Nhat Truong Pham, Balachandran Manavalan, Gwang Lee
Computational prediction of phosphorylation sites of SARS-CoV-2 infection using feature fusion and optimization strategies
Mumdooh J Sabir, Majid Rasool Kamli, Ahmed Atef, Alawiah M Alhibshi, Sherif Edris, Nahid H Hajarah, Ahmed Bahieldin, Balachandran Manavalan, Jamal S M Sabir
MLm5C: A high-precision human RNA 5-methylcytosine sites predictor based on a combination of hybrid machine learning models
Hiroyuki Kurata, Md Harun-Or-Roshid, Md Mehedi Hasan, Sho Tsukiyama, Kazuhiro Maeda, Balachandran Manavalan
ac4C-AFL: A high-precision identification of human mRNA N4-acetylcytidine sites based on adaptive feature representation learning
Nhat Truong Pham, Annie Terrina Terrance, Young-Jun Jeon, Rajan Rakkiyappan, Balachandran Manavalan
Stack-DHUpred: Advancing the accuracy of dihydrouridine modification sites detection via stacking approach
Md Harun-Or-Roshid, Kazuhiro Maeda, Le Thi Phan, Balachandran Manavalan, Hiroyuki Kurata
Unveiling local and global conformational changes and allosteric communications in SOD1 systems using molecular dynamics simulation and network analyses
Shaherin Basith, Balachandran Manavalan, Gwang Lee
H2Opred: a robust and efficient hybrid deep learning model for predicting 2'-O-methylation sites in human RNA
Nhat Truong Pham, Rajan Rakkiyapan, Jongsun Park, Adeel Malik, Balachandran Manavalan
Advancing the accuracy of SARS-CoV-2 phosphorylation site detection via meta-learning approach
Nhat Truong Pham, Le Thi Phan, Jimin Seo, Yeonwoo Kim, Minkyung Song, Sukchan Lee, Young-Jun Jeon, Balachandran Manavalan
ADP-Fuse: A novel two-layer machine learning predictor to identify antidiabetic peptides and diabetes types using multiview information
Shaherin Basith, Nhat Truong Pham, Minkyung Song, Gwang Lee, Balachandran Manavalan
DrugormerDTI: Drug Graphormer for drug-target interaction prediction
Jiayue Hu, Wang Yu, Chao Pang, Junru Jin, Nhat Truong Pham, Balachandran Manavalan, Leyi Wei
Pretoria: An effective computational approach for accurate and high-throughput identification of CD8+ t-cell epitopes of eukaryotic pathogens
Phasit Charoenkwan, Nalini Schaduangrat, Nhat Truong Pham, Balachandran Manavalan, Watshara Shoombuatong
Identification of SH2 domain-containing proteins and motifs prediction by a deep learning method
Duanzhi Wu, Xin Fang, Kai Luan, Qijin Xu, Shiqi Lin, Shiying Sun, Jiaying Yang, Bingying Dong, Balachandran Manavalan, Zhijun Liao
VirPipe: an easy-to-use and customizable pipeline for detecting viral genomes from Nanopore sequencing
Kijin Kim, Kyungmin Park, Seonghyeon Lee, Seung-Hwan Baek, Tae-Hun Lim, Jongwoo Kim, Balachandran Manavalan, Jin-Won Song, Won-Keun Kim
A comprehensive revisit of the machine-learning tools developed for the identification of enhancers in the human genome
Le Thi Phan, Changmin Oh, Tao He, Balachandran Manavalan
How well does a data-driven prediction method distinguish dihydrouridine from tRNA and mRNA?
Shaherin Basith, Balachandran Manavalan
PSRQSP: An effective approach for the interpretable prediction of quorum sensing peptide using propensity score representation learning
Phasit Charoenkwan, Pramote Chumnanpuen, Nalini Schaduangrat, Changmin Oh, Balachandran Manavalan, Watshara Shoombuatong
PRR-HyPred: A two-layer hybrid framework to predict pattern recognition receptors and their families by employing sequence encoded optimal features
Ahmad Firoz, Adeel Malik, Hani Mohammed Ali, Yusuf Akhter, Balachandran Manavalan, Chang-Bae Kim
Computational prediction of protein folding rate using structural parameters and network centrality measures
Saraswathy Nithiyanandam, Vinoth Kumar Sangaraju, Balachandran Manavalan, Gwang Lee
GPApred: The first computational predictor for identifying proteins with LPXTG-like motif using sequence-based optimal features
Adeel Malik, Watshara Shoombuatong, Chang-Bae Kim, Balachandran Manavalan
MonkeyNet: A robust deep convolutional neural network for monkeypox disease detection and classification
Diponkor Bala, Md Shamim Hossain, Mohammad Alamgir Hossain, Md Ibrahim Abdullah, Md Mizanur Rahman, Balachandran Manavalan, Naijie Gu, Mohammad S Islam, Zhangjin Huang
Protection of c-Fos from autophagic degradation by PRMT1-mediated methylation fosters gastric tumorigenesis
Eunji Kim, Laily Rahmawati, Nur Aziz, Han Gyung Kim, Ji Hye Kim, Kyung-Hee Kim, Byong Chul Yoo, Narayana Parameswaran, Jong-Sun Kang, Hoon Hur, Balachandran Manavalan, Jongsung Lee, Jae Youl Cho
SiameseCPP: a sequence-based Siamese network to predict cell-penetrating peptides by contrastive learning
Xin Zhang, Lesong Wei, Xiucai Ye, Kai Zhang, Saisai Teng, Zhongshen Li, Junru Jin, Min Jae Kim, Tetsuya Sakurai, Lizhen Cui, Balachandran Manavalan, Leyi Wei
An Effective Integrated Machine Learning Framework for Identifying Severity of Tomato Yellow Leaf Curl Virus and Their Experimental Validation
Nattanong Bupi, Vinoth Kumar Sangaraju, Le Thi Phan, Aamir Lal, Thuy Thi Bich Vo, Phuong Thi Ho, Muhammad Amir Qureshi, Marjia Tabassum, Sukchan Lee, Balachandran Manavalan
Amyotrophic lateral sclerosis disease-related mutations disrupt the dimerization of superoxide dismutase 1 - A comparative molecular dynamics simulation study
Shaherin Basith, Balachandran Manavalan, Gwang Lee.
FRTpred: A novel approach for accurate prediction of protein folding rate and type
Balachandran Manavalan, Jooyoung Lee.
Computational prediction and interpretation of druggable proteins using a stacked ensemble-learning framework
Phasit Charoenkwan, Nalini Schaduangrat, Mohammad Ali Moni, Watshara Shoombuatong, Balachandran Manavalan
NEPTUNE: A novel computational approach for accurate and large-scale identification of tumor homing peptides
Phasit Charoenkwan, Nalini Schaduangrat, Mohammad Ali Moni, Balachandran Manavalan, Watshara Shoombuatong
The impact of fine particulate matter 2.5 on the cardiovascular system: A review of the invisible killer
Shaherin Basith, Balachandran Manavalan, Tae Hwan Shin, Chan Bae Park, Wang-Soo Lee, Jaetaek Kim, Gwang Lee
StackDPPIV: A novel computational approach for accurate prediction of dipeptidyl peptidase IV (DPP-IV) inhibitory peptides
Phasit Charoenkwan, Chanin Nantasenamat, Md Mehedi Hasan, Mohammad Ali Moni, Balachandran Manavalan, Watshara Shoombuatong
iACVP: markedly enhanced identification of anti-coronavirus peptides using a dataset-specific word2vec model
Hiroyuki Kurata, Sho Tsukiyama, Balachandran Manavalan
TACOS: a novel approach for accurate prediction of cell-specific long noncoding RNAs subcellular localization
Young-Jun Jeon, Md Mehedi Hasan, Hyun Woo Park, Ki Wook Lee, Balachandran Manavalan
SAPPHIRE: A stacking-based ensemble learning framework for accurate prediction of thermophilic proteins
Phasit Charoenkwan, Nalini Schaduangrat, Mohammad Ali Moni, Balachandran Manavalan, Watshara Shoombuatong
Deepm5C: A deep-learning-based hybrid framework for identifyi
StackIL6: a stacking ensemble model for improving the prediction of IL-6 inducing peptides.
Charoenkwan P, Chiangjong W, Nantasenamat C, Hasan MM, Manavalan B, Shoombuatong W.
NeuroPred-FRL: an interpretable prediction model for identifying neuropeptide using feature representation learning.
Hasan MM, Alam MA, Shoombuatong W, Deng HW, Manavalan B, Kurata H.
THRONE: a new approach for accurate prediction of human RNA N7-methylguanosine sites
Watshara Shoombuatong, Shaherin Basith, Thejkiran Pitti, Gwang Lee, Balachandran Manavalan
SCMTHP: A new approach for identifying and characterizing of tumor-homing peptides using estimated propensity scores of amino acids
Phasit Charoenkwan, Wararat Chiangjong, Chanin Nantasenamat, Mohammad Ali Moni, Pietro Lio’, Balachandran Manavalan, Watshara Shoombuatong
MLACP 2.0: An updated machine learning tool for anticancer peptide prediction
Hyun Woo Park, Thejkiran Pitti, Thirumurthy Madhavan, Young-Jun Jeon, Balachandran Manavalan
Accelerating bioactive peptide discovery via mutual information-based meta-learning
Wenjia He, Yi Jiang, Junru Jin, Zhongshen Li, Jiaojiao Zhao, Balachandran Manavalan, Ran Su, Xin Gao, Leyi Wei
Recent trends on the development of machine learning approaches for the prediction of lysine acetylation sites
Shaherin Basith, Hye J Chang, Saraswathy Nithiyanandam, Tae Hwan Shin, Balachandran Manavalan, Gwang Lee
Comparative analysis of machine learning-based approaches for identifying therapeutic peptides targeting SARS-CoV-2
Balachandran Manavalan, Shaherin Basith, Gwang Lee
STALLION: a stacking-based ensemble learning framework for prokaryotic lysine acetylation site prediction.
Basith S, Lee G, Manavalan B.
StackDPPIV: A novel computational approach for accurate prediction of dipeptidyl peptidase IV (DPP-IV) inhibitory peptides.
Charoenkwan P, Nantasenamat C, Hasan MM, Moni MA, Lio' P, Manavalan B, Shoombuatong W.
UMPred-FRL: A New Approach for Accurate Prediction of Umami Peptides Using Feature Representation Learning.
Charoenkwan P, Nantasenamat C, Hasan MM, Moni MA, Manavalan B, Shoombuatong W.
Silica-coated magnetic-nanoparticle-induced cytotoxicity is reduced in microglia by glutathione and citrate identified using integrated omics.
Shin TH, Manavalan B, Lee DY, Basith S, Seo C, Paik MJ, Kim SW, Seo H, Lee JY, Kim JY, Kim AY, Chung JM, Baik EJ, Kang SH, Choi DK, Kang Y, Mouradian MM, Lee G.
Integrative machine learning framework for the identification of cell-specific enhancers from the human genome.
Basith S, Hasan MM, Lee G, Wei L, Manavalan B.
StackIL6: a stacking ensemble model for improving the prediction of IL-6 inducing peptides.
Charoenkwan P, Chiangjong W, Nantasenamat C, Hasan MM, Manavalan B, Shoombuatong W.
NeuroPred-FRL: an interpretable prediction model for identifying neuropeptide using feature representation learning.
Hasan MM, Alam MA, Shoombuatong W, Deng HW, Manavalan B, Kurata H.
Silica-coated magnetic nanoparticles activate microglia and induce neurotoxic D-serine secretion.
Shin TH, Lee DY, Manavalan B, Basith S, Na YC, Yoon C, Lee HS, Paik MJ, Lee G.
Computational prediction of species-specific yeast DNA replication origin via iterative feature representation.
Manavalan B, Basith S, Shin TH, Lee G.
Computational prediction and interpretation of cell-specific replication origin sites from multiple eukaryotes by exploiting stacking framework.
Wei L, He W, Malik A, Su R, Cui L, Manavalan B.
Critical evaluation of web-based DNA N6-methyladenine site prediction tools.
Hasan MM, Shoombuatong W, Kurata H, Manavalan B.
Mapping the Intramolecular Communications among Different Glutamate Dehydrogenase States Using Molecular Dynamics.
Basith S, Manavalan B, Shin TH, Lee G.
Meta-i6mA: an interspecies predictor for identifying DNA N6-methyladenine sites of plant genomes by exploiting informative features in an integrative machine-learning framework.
Hasan MM, Basith S, Khatun MS, Lee G, Manavalan B, Kurata H.
BERT4Bitter: a bidirectional encoder representations from transformers (BERT)-based model for improving the prediction of bitter peptides.
Charoenkwan P, Nantasenamat C, Hasan MM, Manavalan B, Shoombuatong W.
Decrease in membrane fluidity and traction force induced by silica-coated magnetic nanoparticles.
Shin TH, Ketebo AA, Lee DY, Lee S, Kang SH, Basith S, Manavalan B, Kwon DH, Park S, Lee G.
SortPred: The first machine learning based predictor to identify bacterial sortases and their classes using sequence-derived information.
Malik A, Subramaniyam S, Kim CB, Manavalan B.
Empirical Comparison and Analysis of Web-Based DNA N (4)-Methylcytosine Site Prediction Tools.
Manavalan B, Hasan MM, Basith S, Gosu V, Shin TH, Lee G.
i4mC-ROSE, a bioinformatics tool for the identification of DNA N4-methylcytosine sites in the Rosaceae genome.
Hasan MM, Manavalan B, Khatun MS, Kurata H.
Metabolome Changes in Cerebral Ischemia.
Shin TH, Lee DY, Basith S, Manavalan B, Paik MJ, Rybinnik I, Mouradian MM, Ahn JH, Lee G.
Machine intelligence in peptide therapeutics: A next-generation tool for rapid disease screening.
Basith S, Manavalan B, Hwan Shin T, Lee G.
HLPpred-Fuse: improved and robust prediction of hemolytic peptide and its activity by fusing multiple feature representation.
Hasan MM, Schaduangrat N, Basith S, Lee G, Shoombuatong W, Manavalan B.
i6mA-Fuse: improved and robust prediction of DNA 6 mA sites in the Rosaceae genome by fusing multiple feature representation.
Hasan MM, Manavalan B, Shoombuatong W, Khatun MS, Kurata H.
Empirical comparison and analysis of web-based cell-penetrating peptide prediction tools.
Su R, Hu J, Zou Q, Manavalan B, Wei L.
Extremely-randomized-tree-based Prediction of N(6)-Methyladenosine Sites in Saccharomyces cerevisiae.
Govindaraj RG, Subramaniyam S, Manavalan B.
Evolution of Machine Learning Algorithms in the Prediction and Design of Anticancer Peptides.
Basith S, Manavalan B, Shin TH, Lee DY, Lee G.
i4mC-Mouse: Improved identification of DNA N4-methylcytosine sites in the mouse genome using multiple encoding schemes.
Hasan MM, Manavalan B, Shoombuatong W, Khatun MS, Kurata H.
SDM6A: A Web-Based Integrative Machine-Learning Framework for Predicting 6mA Sites in the Rice Genome.
Basith S, Manavalan B, Shin TH, Lee G.
Prediction of S-nitrosylation sites by integrating support vector machines and random forest.
Hasan MM, Manavalan B, Khatun MS, Kurata H.
Iterative feature representations improve N4-methylcytosine site prediction.
Wei L, Su R, Luan S, Liao Z, Manavalan B, Zou Q, Shi X.
4mCpred-EL: An Ensemble Learning Framework for Identification of DNA N(4)-methylcytosine Sites in the Mouse Genome.
Manavalan B, Basith S, Shin TH, Lee DY, Wei L, Lee G.
Silica-Coated Magnetic Nanoparticles Decrease Human Bone Marrow-Derived Mesenchymal Stem Cell Migratory Activity by Reducing Membrane Fluidity and Impairing Focal Adhesion.
Shin TH, Lee DY, Ketebo AA, Lee S, Manavalan B, Basith S, Ahn C, Kang SH, Park S, Lee G.
mAHTPred: a sequence-based meta-predictor for improving the prediction of anti-hypertensive peptides using effective feature representation.
Manavalan B, Basith S, Shin TH, Wei L, Lee G.
Meta-4mCpred: A Sequence-Based Meta-Predictor for Accurate DNA 4mC Site Prediction Using Effective Feature Representation.
Manavalan B, Basith S, Shin TH, Wei L, Lee G.
Silica-coated magnetic nanoparticles induce glucose metabolic dysfunction in vitro via the generation of reactive oxygen species.
Shin TH, Seo C, Lee DY, Ji M, Manavalan B, Basith S, Chakkarapani SK, Kang SH, Lee G, Paik MJ, Park CB.
mACPpred: A Support Vector Machine-Based Meta-Predictor for Identification of Anticancer Peptides.
Boopathi V, Subramaniyam S, Malik A, Lee G, Manavalan B, Yang DC.
A Molecular Dynamics Approach to Explore the Intramolecular Signal Transduction of PPAR-alpha.
Basith S, Manavalan B, Shin TH, Lee G.
PFDB: A standardized protein folding database with temperature correction.
Manavalan B, Kuwajima K, Lee J.
AtbPpred: A Robust Sequence-Based Prediction of Anti-Tubercular Peptides Using Extremely Randomized Trees.
Manavalan B, Basith S, Shin TH, Wei L, Lee G.
Machine-Learning-Based Prediction of Cell-Penetrating Peptides and Their Uptake Efficiency with Improved Accuracy.
Manavalan B, Subramaniyam S, Shin TH, Kim MO, Lee G.
Methods for estimation of model accuracy in CASP12.
Elofsson A, Joo K, Keasar C, Lee J, Maghrabi AHA, Manavalan B, McGuffin LJ, Menendez Hurtado D, Mirabello C, Pilstal R, Sidi T, Uziela K, Wallner B.
Protein structure modeling and refinement by global optimization in CASP12.
Hong SH, Joung I, Flores-Canales JC, Manavalan B, Cheng Q, Heo S, Kim JY, Lee SY, Nam M, Joo K, Lee IH, Lee SJ, Lee J.
DHSpred: support-vector-machine-based human DNase I hypersensitive sites prediction using the optimal features selected by random forest.
Manavalan B, Shin TH, Lee G.
Integration of metabolomics and transcriptomics in nanotoxicity studies.
Shin TH, Lee DY, Lee HS, Park HJ, Jin MS, Paik MJ, Manavalan B, Mo JS, Lee G.
PVP-SVM: Sequence-Based Prediction of Phage Virion Proteins Using a Support Vector Machine.
Manavalan B, Shin TH, Lee G.
AIPpred: Sequence-Based Prediction of Anti-inflammatory Peptides Using Random Forest.
Manavalan B, Shin TH, Kim MO, Lee G.
iBCE-EL: A New Ensemble Learning Framework for Improved Linear B-Cell Epitope Prediction.
Manavalan B, Govindaraj RG, Shin TH, Kim MO, Lee G.
PIP-EL: A New Ensemble Learning Method for Improved Proinflammatory Peptide Predictions.
Manavalan B, Shin TH, Kim MO, Lee G.
Bidirectional Transcriptome Analysis of Rat Bone Marrow-Derived Mesenchymal Stem Cells and Activated Microglia in an In Vitro Coculture System.
Lee DY, Jin MS, Manavalan B, Kim HK, Song JH, Shin TH, Lee G.
iGHBP: Computational identification of growth hormone binding proteins from sequences using extremely randomised tree.
Basith S, Manavalan B, Shin TH, Lee G.
MLACP: machine-learning-based prediction of anticancer peptides.
Manavalan B, Basith S, Shin TH, Choi S, Kim MO, Lee G.
SVMQA: support-vector-machine-based protein single-model quality assessment.
Manavalan B, Lee J.
Template based protein structure modeling by global optimization in CASP11.
Joo K, Joung I, Lee SY, Kim JY, Cheng Q, Manavalan B, Joung JY, Heo S, Lee J, Nam M, Lee IH, Lee SJ, Lee J.
Random forest-based protein model quality assessment (RFMQA) using structural features and potential energy terms.
Manavalan B, Lee J, Lee J.
Evolutionary, structural and functional interplay of the IkappaB family members.
Basith S, Manavalan B, Gosu V, Choi S.
Roles of toll-like receptors in cancer: a double-edged sword for defense and offense.
Basith S, Manavalan B, Yoo TH, Kim SG, Choi S.
Molecular modeling-based evaluation of dual function of IkappaBzeta ankyrin repeat domain in toll-like receptor signaling.
Manavalan B, Govindaraj R, Lee G, Choi S.
Toll-like receptor modulators: a patent review (2006-2010).
Basith S, Manavalan B, Lee G, Kim SG, Choi S.
Similar Structures but Different Roles - An Updated Perspective on TLR Structures.
Manavalan B, Basith S, Choi S.
In silico approach to inhibition of signaling pathways of Toll-like receptors 2 and 4 by ST2L.
Basith S, Manavalan B, Govindaraj RG, Choi S.
Comparative analysis of species-specific ligand recognition in Toll-like receptor 8 signaling: a hypothesis.
Govindaraj RG, Manavalan B, Basith S, Choi S.
Structure-function relationship of cytoplasmic and nuclear IkappaB proteins: an in silico analysis.
Manavalan B, Basith S, Choi YM, Lee G, Choi S.
Molecular modeling-based evaluation of hTLR10 and identification of potential ligands in Toll-like receptor signaling.
Govindaraj RG, Manavalan B, Lee G, Choi S.
Molecular modeling of the reductase domain to elucidate the reaction mechanism of reduction of peptidyl thioester into its corresponding alcohol in non-ribosomal peptide synthetases.
Manavalan B, Murugapiran SK, Lee G, Choi S.