DOGpred: A Novel Deep Learning Framework for Accurate Identification of Human O-linked Threonine Glycosylation Sites.
Lee KW, Pham NT, Min HJ, Park HW, Lee JW, Lo HE, Kwon NY, Seo J, Shaginyan I, Cho H, Wei L, Manavalan B, Jeon YJ.

ERNIE-ac4C: A Novel Deep Learning Model for Effectively Predicting N4-acetylcytidine Sites.
Lu R, Qiao J, Li K, Zhao Y, Jin J, Cui F, Zhang Z, Manavalan B, Wei L.

REMED-T2D: A robust ensemble learning model for early detection of type 2 diabetes using healthcare dataset.
Phan LT, Rakkiyappan R, Manavalan B.

AntiT2DMP-Pred: Leveraging feature fusion and optimization for superior machine learning prediction of type 2 diabetes mellitus.
Basith S, Manavalan B, Lee G.

Leveraging deep transfer learning and explainable AI for accurate COVID-19 diagnosis: Insights from a multi-national chest CT scan study.
Pham NT, Ko J, Shah M, Rakkiyappan R, Woo HG, Manavalan B.

MST-m6A: A Novel Multi-Scale Transformer-based Framework for Accurate Prediction of m6A Modification Sites Across Diverse Cellular Contexts.
Su Q, Phan LT, Pham NT, Wei L, Manavalan B.

mHPpred: Accurate identification of peptide hormones using multi-view feature learning.
Basith S, Sangaraju VK, Manavalan B, Lee G.

mACPpred 2.0: Stacked Deep Learning for Anticancer Peptide Prediction with Integrated Spatial and Probabilistic Feature Representations.
Sangaraju VK, Pham NT, Wei L, Yu X, Manavalan B.

Computational prediction of phosphorylation sites of SARS-CoV-2 infection using feature fusion and optimization strategies.
Sabir MJ, Kamli MR, Atef A, Alhibshi AM, Edris S, Hajarah NH, Bahieldin A, Manavalan B, Sabir JSM.

HOTGpred: Enhancing human O-linked threonine glycosylation prediction using integrated pretrained protein language model-based features and multi-stage feature selection approach.
Pham NT, Zhang Y, Rakkiyappan R, Manavalan B.

APLpred: A machine learning-based tool for accurate prediction and characterization of asparagine peptide lyases using sequence-derived optimal features.
Malik A, Kamli MR, Sabir JSM, Rather IA, Phan LT, Kim CB, Manavalan B.

MLm5C: A high-precision human RNA 5-methylcytosine sites predictor based on a combination of hybrid machine learning models.
Kurata H, Harun-Or-Roshid M, Mehedi Hasan M, Tsukiyama S, Maeda K, Manavalan B.

SEP-AlgPro: An efficient allergen prediction tool utilizing traditional machine learning and deep learning techniques with protein language model features.
Basith S, Pham NT, Manavalan B, Lee G.

ac4C-AFL: A high-precision identification of human mRNA N4-acetylcytidine sites based on adaptive feature representation learning.
Pham NT, Terrance AT, Jeon YJ, Rakkiyappan R, Manavalan B.

CODENET: A deep learning model for COVID-19 detection.
Ju H, Cui Y, Su Q, Juan L, Manavalan B.

Stack-DHUpred: Advancing the accuracy of dihydrouridine modification sites detection via stacking approach.
Harun-Or-Roshid M, Maeda K, Phan LT, Manavalan B, Kurata H.

Citations: 10

Unveiling local and global conformational changes and allosteric communications in SOD1 systems using molecular dynamics simulation and network analyses.
Basith S, Manavalan B, Lee G.

Citations: 1

METTL18 functions as a Phenotypic Regulator in Src-Dependent Oncogenic Responses of HER2-Negative Breast Cancer.
Kim HG, Kim JH, Kim KH, Yoo BC, Kang SU, Kim YB, Kim S, Paik HJ, Lee JE, Nam SJ, Parameswaran N, Han JW, Manavalan B, Cho JY.

Meta-2OM: A multi-classifier meta-model for the accurate prediction of RNA 2'-O-methylation sites in human RNA.
Harun-Or-Roshid M, Pham NT, Manavalan B, Kurata H.

H2Opred: a robust and efficient hybrid deep learning model for predicting 2'-O-methylation sites in human RNA.
Pham NT, Rakkiyapan R, Park J, Malik A, Manavalan B.

Citations: 27

Advancing the accuracy of SARS-CoV-2 phosphorylation site detection via meta-learning approach.
Pham NT, Phan LT, Seo J, Kim Y, Song M, Lee S, Jeon YJ, Manavalan B.

Citations: 21

ADP-Fuse: A novel two-layer machine learning predictor to identify antidiabetic peptides and diabetes types using multiview information.
Basith S, Pham NT, Song M, Lee G, Manavalan B.

Citations: 20

Identification of SH2 domain-containing proteins and motifs prediction by a deep learning method.
Wu D, Fang X, Luan K, Xu Q, Lin S, Sun S, Yang J, Dong B, Manavalan B, Liao Z.

DrugormerDTI: Drug Graphormer for drug-target interaction prediction.
Hu J, Yu W, Pang C, Jin J, Pham NT, Manavalan B, Wei L.

A comprehensive revisit of the machine-learning tools developed for the identification of enhancers in the human genome.
Phan LT, Oh C, He T, Manavalan B.

Pretoria: An effective computational approach for accurate and high-throughput identification of CD8(+) t-cell epitopes of eukaryotic pathogens.
Charoenkwan P, Schaduangrat N, Pham NT, Manavalan B, Shoombuatong W.

VirPipe: an easy-to-use and customizable pipeline for detecting viral genomes from Nanopore sequencing.
Kim K, Park K, Lee S, Baek SH, Lim TH, Kim J, Manavalan B, Song JW, Kim WK.

PSRQSP: An effective approach for the interpretable prediction of quorum sensing peptide using propensity score representation learning.
Charoenkwan P, Chumnanpuen P, Schaduangrat N, Oh C, Manavalan B, Shoombuatong W.

PRR-HyPred: A two-layer hybrid framework to predict pattern recognition receptors and their families by employing sequence encoded optimal features.
Firoz A, Malik A, Ali HM, Akhter Y, Manavalan B, Kim CB.

MonkeyNet: A robust deep convolutional neural network for monkeypox disease detection and classification.
Bala D, Hossain MS, Hossain MA, Abdullah MI, Rahman MM, Manavalan B, Gu N, Islam MS, Huang Z.

How well does a data-driven prediction method distinguish dihydrouridine from tRNA and mRNA?
Basith S, Manavalan B.

Computational prediction of protein folding rate using structural parameters and network centrality measures.
Nithiyanandam S, Sangaraju VK, Manavalan B, Lee G.

GPApred: The first computational predictor for identifying proteins with LPXTG-like motif using sequence-based optimal features.
Malik A, Shoombuatong W, Kim CB, Manavalan B.

SiameseCPP: a sequence-based Siamese network to predict cell-penetrating peptides by contrastive learning.
Zhang X, Wei L, Ye X, Zhang K, Teng S, Li Z, Jin J, Kim MJ, Sakurai T, Cui L, Manavalan B, Wei L.

An Effective Integrated Machine Learning Framework for Identifying Severity of Tomato Yellow Leaf Curl Virus and Their Experimental Validation.
Bupi N, Sangaraju VK, Phan LT, Lal A, Vo TTB, Ho PT, Qureshi MA, Tabassum M, Lee S, Manavalan B.

Protection of c-Fos from autophagic degradation by PRMT1-mediated methylation fosters gastric tumorigenesis.
Kim E, Rahmawati L, Aziz N, Kim HG, Kim JH, Kim KH, Yoo BC, Parameswaran N, Kang JS, Hur H, Manavalan B, Lee J, Cho JY.

Citations: 21

Amyotrophic lateral sclerosis disease-related mutations disrupt the dimerization of superoxide dismutase 1 - A comparative molecular dynamics simulation study.
Basith S, Manavalan B, Lee G.

FRTpred: A novel approach for accurate prediction of protein folding rate and type.
Manavalan B, Lee J.

Computational prediction and interpretation of druggable proteins using a stacked ensemble-learning framework.
Charoenkwan P, Schaduangrat N, Lio' P, Moni MA, Shoombuatong W, Manavalan B.

NEPTUNE: A novel computational approach for accurate and large-scale identification of tumor homing peptides.
Charoenkwan P, Schaduangrat N, Lio' P, Moni MA, Manavalan B, Shoombuatong W.

Deepm5C: A deep-learning-based hybrid framework for identifying human RNA N5-methylcytosine sites using a stacking strategy.
Hasan MM, Tsukiyama S, Cho JY, Kurata H, Alam MA, Liu X, Manavalan B, Deng HW.

The Impact of Fine Particulate Matter 2.5 on the Cardiovascular System: A Review of the Invisible Killer.
Basith S, Manavalan B, Shin TH, Park CB, Lee WS, Kim J, Lee G.

iACVP: markedly enhanced identification of anti-coronavirus peptides using a dataset-specific word2vec model.
Kurata H, Tsukiyama S, Manavalan B.

TACOS: a novel approach for accurate prediction of cell-specific long noncoding RNAs subcellular localization.
Jeon YJ, Hasan MM, Park HW, Lee KW, Manavalan B.

SAPPHIRE: A stacking-based ensemble learning framework for accurate prediction of thermophilic proteins.
Charoenkwan P, Schaduangrat N, Moni MA, Lio' P, Manavalan B, Shoombuatong W.

THRONE: A New Approach for Accurate Prediction of Human RNA N7-Methylguanosine Sites.
Shoombuatong W, Basith S, Pitti T, Lee G, Manavalan B.

MLCPP 2.0: An Updated Cell-penetrating Peptides and Their Uptake Efficiency Predictor.
Manavalan B, Patra MC.

STALLION: a stacking-based ensemble learning framework for prokaryotic lysine acetylation site prediction.
Basith S, Lee G, Manavalan B.

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.

SCMTHP: A New Approach for Identifying and Characterizing of Tumor-Homing Peptides Using Estimated Propensity Scores of Amino Acids.
Charoenkwan P, Chiangjong W, Nantasenamat C, Moni MA, Lio' P, Manavalan B, Shoombuatong W.

MLACP 2.0: An updated machine learning tool for anticancer peptide prediction.
Thi Phan L, Woo Park H, Pitti T, Madhavan T, Jeon YJ, Manavalan B.

Recent Trends on the Development of Machine Learning Approaches for the Prediction of Lysine Acetylation Sites.
Basith S, Chang HJ, Nithiyanandam S, Shin TH, Manavalan B, 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.

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.

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.

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.

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.

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.

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.

Evolution of Machine Learning Algorithms in the Prediction and Design of Anticancer Peptides.
Basith S, Manavalan B, Shin TH, Lee DY, Lee G.

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.

PIP-EL: A New Ensemble Learning Method for Improved Proinflammatory Peptide Predictions.
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.

AIPpred: Sequence-Based Prediction of Anti-inflammatory Peptides Using Random Forest.
Manavalan B, Shin TH, Kim MO, Lee G.

PVP-SVM: Sequence-Based Prediction of Phage Virion Proteins Using a Support Vector Machine.
Manavalan B, 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.

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.

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.

Comparative analysis of species-specific ligand recognition in Toll-like receptor 8 signaling: a hypothesis.
Govindaraj RG, 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.

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.