Decision making based ensemble feature selection approach through a new score function in q-rung orthopair hesitant fuzzy environment
S Kavitha, N Kendra, J Satheeshkumar, T Amudha

DOGpred: a novel deep learning framework for accurate identification of human o-linked threonine glycosylation sites
Ki Wook Lee, Nhat Truong Pham, Hye Jung Min, Hyun Woo Park, Ji Won Lee, Han-En Lo, Na Young Kwon, Jimin Seo, Illia Shaginyan, Heeje Cho, Leyi Wei, Balachandran Manavalan, Young-Jun Jeon

ERNIE-ac4C: A Novel Deep Learning Model for Effectively Predicting N4-acetylcytidine Sites
Ronglin Lu, Jianbo Qiao, Kefei Li, Yanxi Zhao, Junru Jin, Feifei Cui, Zilong Zhang, Balachandran Manavalan, Leyi Wei

Optimization of preprocessing strategies for developing AI-based disease diagnosis model using whole transcriptomic data
Ki Wook Lee, Hye Jung Min, Hyun Woo Park, Balachandran Manavalan, Young-Jun Jeon

HyPepTox-Fuse: An interpretable hybrid framework for accurate peptide toxicity prediction fusing protein language model-based embeddings with conventional descriptors
Duong Thanh Tran, Nhat Truong Pham, Nguyen Doan Hieu Nguyen, Leyi Wei, Balachandran Manavalan

Cost-sensitive feature selection for multi-label classification: multi-criteria decision-making approach
SS Mohanrasu, Le Thi Phan, Rakkiyappan Rajan, Balachandran Manavalan

REMED-T2D: A robust ensemble learning model for early detection of type 2 diabetes using healthcare dataset
Rajan Rakkiyappan, Balachandran Manavalan

MST-m6A: a novel multi-scale transformer-based framework for accurate prediction of m6A modification sites across diverse cellular contexts
Qiaosen Su, Nhat Truong Pham, Leyi Wei, Balachandran Manavalan

XMolCap: Advancing Molecular Captioning through Multimodal Fusion and Explainable Graph Neural Networks
Duong Thanh Tran, Nguyen Doan Hieu Nguyen, Nhat Truong Pham, Rajan Rakkiyappan, Rajendra Karki, Balachandran Manavalan

M3S-ALG: Improved and robust prediction of allergenicity of chemical compounds by using a novel multi-step stacking strategy
Phasit Charoenkwan, Nalini Schaduangrat, Balachandran Manavalan, Watshara Shoombuatong

Molecular pretraining models towards molecular property prediction
Jianbo Qiao, Wenjia Gao, Junru Jin, Ding Wang, Xu Guo, Balachandran Manavalan, Leyi Wei

AntiT2DMP-Pred: Leveraging feature fusion and optimization for superior machine learning prediction of type 2 diabetes mellitus
Shaherin Basith, Balachandran Manavalan, Gwang Lee

xBitterT5: an explainable transformer-based framework with multimodal inputs for identifying bitter-taste peptides
Nguyen Doan Hieu Nguyen, Nhat Truong Pham, Duong Thanh Tran, Leyi Wei, Adeel Malik, Balachandran Manavalan

TP-ML: a Machine-learning-based Tool to Identify Threonine Proteases Using Sequence-derived Optimal Features
Ahmad Firoz, Adeel Malik, Nitin Mahajan, Le Thi Phan, Hani Mohammed Ali, Chang-Bae Kim, Balachandran Manavalan

Leveraging deep transfer learning and explainable AI for accurate COVID-19 diagnosis: Insights from a multi-national chest CT scan study
Nhat Truong Pham, Jinsol Ko, Masaud Shah, Rajan Rakkiyappan, Hyun Goo Woo, Balachandran Manavalan

CODENET: A deep learning model for COVID-19 detection
Hong Ju, Yanyan Cui, Qiaosen Su, Liran Juan, 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

mACPpred 2.0: Stacked deep learning for anticancer peptide prediction with integrated spatial and probabilistic feature representations
Vinoth Kumar Sangaraju, Nhat Truong Pham, Leyi Wei, Xue Yu, Balachandran Manavalan

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

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

RDR100: A Robust Computational Method for Identification of Krüppel-like Factors
Adeel Malik, Majid R Kamli, Jamal SM Sabir, Le Thi Phan, Chang-Bae Kim, Balachandran Manavalan

METTL18 functions as a Phenotypic Regulator in Src-Dependent Oncogenic Responses of HER2-Negative Breast Cancer
Han Gyung Kim, Ji Hye Kim, Kyung-Hee Kim, Byong Chul Yoo, Sung-Ung Kang, Young Bong Kim, Sangmin Kim, Hyun-June Paik, Jeong Eon Lee, Seok Jin Nam, Narayanan Parameswaran, Jeung-Whan Han, Balachandran Manavalan, Jae Youl Cho

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

Lang2Mol-Diff: A Diffusion-Based Generative Model for Language-to-Molecule Translation Leveraging SELFIES Representation
Nguyen Nguyen, Nhat Truong Pham, Duong Tran, Balachandran Manavalan

mHPpred: Accurate identification of peptide hormones using multi-view feature learning
Shaherin Basith, Vinoth Kumar Sangaraju, Balachandran Manavalan, Gwang Lee

CFCN: An HLA-peptide Prediction Model based on Taylor Extension Theory and Multi-view Learning
Bing Rao, Bing Han, Leyi Wei, Zeyu Zhang, Xinbo Jiang, Balachandran Manavalan

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

Mol2lang-vlm: Vision-and text-guided generative pre-trained language models for advancing molecule captioning through multimodal fusion
Duong Tran, Nhat Truong Pham, Nguyen Nguyen, Balachandran Manavalan

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 SM Sabir

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 SM Sabir, Irfan A 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

Stack-DHUpred: Advancing the accuracy of dihydrouridine modification sites detection via stacking approach
Md Harun-Or-Roshid, Kazuhiro Maeda, Balachandran Manavalan, Hiroyuki Kurata

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

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

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

Hybrid data augmentation and deep attention-based dilated convolutional-recurrent neural networks for speech emotion recognition
Nhat Truong Pham, Duc Ngoc Minh Dang, Ngoc Duy Nguyen, Thanh Thi Nguyen, Hai Nguyen, Balachandran Manavalan, Chee Peng Lim, Sy Dzung Nguyen

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

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

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

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

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

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

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

Drugormerdti: Drug graphormer for drug–target interaction prediction
Jiayue Hu, Wang Yu, Chao Pang, Junru Jin, Nhat Truong Pham, Balachandran Manavalan, Leyi Wei

Ensemble feature selection using Bonferroni, OWA and Induced OWA aggregation operators
Kesavan Janani, SS Mohanrasu, Chee Peng Lim, Balachandran Manavalan, R Rakkiyappan

Computational prediction of protein folding rate using structural parameters and network centrality measures
Saraswathy Nithiyanandam, Vinoth Kumar Sangaraju, Balachandran Manavalan, Gwang Lee

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

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

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

Ser-fuse: An emotion recognition application utilizing multi-modal, multi-lingual, and multi-feature fusion
Nhat Truong Pham, Le Thi Phan, Duc Ngoc Minh Dang, Balachandran Manavalan

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

Ensemble Feature Selection Using Dual Hesitant Fuzzy Einstein Aggregation Operators
S Kavitha, J Satheeshkumar, Balachandran Manavalan, T Amudha

How well does a data-driven prediction method distinguish dihydrouridine from tRNA and mRNA?
Shaherin Basith, Balachandran Manavalan

Computational prediction and interpretation of druggable proteins using a stacked ensemble-learning framework
Phasit Charoenkwan, Nalini Schaduangrat, Mohammad Ali Moni, Watshara Shoombuatong, Balachandran Manavalan

MLCPP 2.0: an updated cell-penetrating peptides and their uptake efficiency predictor
Balachandran Manavalan, Mahesh Chandra Patra

Comparative analysis of machine learning-based approaches for identifying therapeutic peptides targeting SARS-CoV-2
Balachandran Manavalan, Shaherin Basith, Gwang Lee

FRTpred: A novel approach for accurate prediction of protein folding rate and type
Balachandran Manavalan, Jooyoung Lee

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

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

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

iACVP: markedly enhanced identification of anti-coronavirus peptides using a dataset-specific word2vec model
Hiroyuki Kurata, Sho Tsukiyama, Balachandran Manavalan

MLACP 2.0: An updated machine learning tool for anticancer peptide prediction
Hyun Woo Park, Thejkiran Pitti, Thirumurthy Madhavan, Young-Jun Jeon, Balachandran Manavalan

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

SAPPHIRE: A stacking-based ensemble learning framework for accurate prediction of thermophilic proteins
Phasit Charoenkwan, Nalini Schaduangrat, Mohammad Ali Moni, Balachandran Manavalan, Watshara Shoombuatong

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

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

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

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

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

THRONE: a new approach for accurate prediction of human RNA N7-methylguanosine sites
Watshara Shoombuatong, Shaherin Basith, Thejkiran Pitti, Gwang Lee, Balachandran Manavalan

NeuroPred-FRL: an interpretable prediction model for identifying neuropeptide using feature representation learning
Md Mehedi Hasan, Md Ashad Alam, Watshara Shoombuatong, Hong-Wen Deng, Balachandran Manavalan, Hiroyuki Kurata

Critical evaluation of web-based DNA N6-methyladenine site prediction tools
Md Mehedi Hasan, Watshara Shoombuatong, Hiroyuki Kurata, Balachandran Manavalan

Decrease in membrane fluidity and traction force induced by silica-coated magnetic nanoparticles
Tae Hwan Shin, Abdurazak Aman Ketebo, Da Yeon Lee, Seungah Lee, Seong Ho Kang, Shaherin Basith, Balachandran Manavalan, Do Hyeon Kwon, Sungsu Park, Gwang Lee

SortPred: The first machine learning based predictor to identify bacterial sortases and their classes using sequence-derived information
Adeel Malik, Sathiyamoorthy Subramaniyan, Chang-Bae Kim, Balachandran Manavalan

Computational prediction of species-specific yeast DNA replication origin via iterative feature representation
Balachandran Manavalan, Shaherin Basith, Tae Hwan Shin, Gwang Lee

Silica-coated magnetic nanoparticles activate microglia and induce neurotoxic d-serine secretion
Tae Hwan Shin, Da Yeon Lee, Balachandran Manavalan, Shaherin Basith, Yun-Cheol Na, Cheolho Yoon, Hyeon-Seong Lee, Man Jeong Paik, Gwang Lee

Silica-coated magnetic-nanoparticle-induced cytotoxicity is reduced in microglia by glutathione and citrate identified using integrated omics
Tae Hwan Shin, Balachandran Manavalan, Da Yeon Lee, Shaherin Basith, Chan Seo, Man Jeong Paik, Sang-Wook Kim, Haewoon Seo, Ju Yeon Lee, Jin Young Kim, A Young Kim, Jee Min Chung, Eun Joo Baik, Seong Ho Kang, Dong-Kug Choi, Yup Kang, M Maral Mouradian, Gwang Lee

UMPred-FRL: A new approach for accurate prediction of umami peptides using feature representation learning
Phasit Charoenkwan, Chanin Nantasenamat, Md Mehedi Hasan, Mohammad Ali Moni, Balachandran Manavalan, Watshara Shoombuatong

Silica-Coated Magnetic Nanoparticles Decrease Human Bone Marrow-Derived Mesenchymal Stem Cell Migratory Activity by Reducing Membrane Fluidity and Impairing Focal Adhesion
Balachandran Manavalan, Shaherin Basith, Chanyoung Ahn, Seong Ho Kang, Sungsu Park, Gwang Lee

TearsWaiting/MIMML: The implement of MIMML in paper: Accelerating Bioactive Peptides 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

Mapping the intramolecular communications among different glutamate dehydrogenase states using molecular dynamics
Shaherin Basith, Balachandran Manavalan, Tae Hwan Shin, Gwang Lee

Meta-i6mA: an interspecies predictor for identifying DNA N 6-methyladenine sites of plant genomes by exploiting informative features in an integrative machine-learning framework
Md Mehedi Hasan, Shaherin Basith, Mst Shamima Khatun, Gwang Lee, Balachandran Manavalan, Hiroyuki Kurata

StackIL6: a stacking ensemble model for improving the prediction of IL-6 inducing peptides
Phasit Charoenkwan, Wararat Chiangjong, Chanin Nantasenamat, Md Mehedi Hasan, Balachandran Manavalan, Watshara Shoombuatong

Computational prediction and interpretation of cell-specific replication origin sites from multiple eukaryotes by exploiting stacking framework
Leyi Wei, Wenjia He, Adeel Malik, Ran Su, Lizhen Cui, Balachandran Manavalan

BERT4Bitter: a bidirectional encoder representations from transformers (BERT)-based model for improving the prediction of bitter peptides
Phasit Charoenkwan, Chanin Nantasenamat, Md Mehedi Hasan, Balachandran Manavalan, Watshara Shoombuatong

STALLION: A Stacking-based Ensemble Learning Framework for Prokaryotic Lysine Acetylation Site Prediction
Shaherin Basith, Gwang Lee, Balachandran Manavalan

Integrative machine learning framework for the identification of cell-specific enhancers from the human genome
Shaherin Basith, Md Mehedi Hasan, Gwang Lee, Leyi Wei, Balachandran Manavalan

Evolution of machine learning algorithms in the prediction and design of anticancer peptides
Shaherin Basith, Balachandran Manavalan, Tae H Shin, Da Yeon Lee, Gwang Lee

i4mC-ROSE, a bioinformatics tool for the identification of DNA N4-methylcytosine sites in the Rosaceae genome
Md Mehedi Hasan, Balachandran Manavalan, Mst Shamima Khatun, Hiroyuki Kurata

Extremely-randomized-tree-based Prediction of N6-Methyladenosine Sites in Saccharomyces cerevisiae
Rajiv G Govindaraj, Sathiyamoorthy Subramaniyam, Balachandran Manavalan

Machine intelligence in peptide therapeutics: A next‐generation tool for rapid disease screening
Shaherin Basith, Balachandran Manavalan, Tae Hwan Shin, Gwang Lee

HLPpred-Fuse: improved and robust prediction of hemolytic peptide and its activity by fusing multiple feature representation
Md Mehedi Hasan, Nalini Schaduangrat, Shaherin Basith, Gwang Lee, Watshara Shoombuatong, Balachandran Manavalan

Empirical comparison and analysis of web-based DNA N4-methylcytosine site prediction tools
Balachandran Manavalan, Md Mehedi Hasan, Shaherin Basith, Vijayakumar Gosu, Tae-Hwan Shin, Gwang Lee

Metabolome changes in cerebral ischemia
Tae Hwan Shin, Da Yeon Lee, Shaherin Basith, Balachandran Manavalan, Man Jeong Paik, Igor Rybinnik, M Maral Mouradian, Jung Hwan Ahn, Gwang Lee

i4mC-Mouse: Improved identification of DNA N4-methylcytosine sites in the mouse genome using multiple encoding schemes
Md Mehedi Hasan, Balachandran Manavalan, Watshara Shoombuatong, Mst Shamima Khatun, Hiroyuki Kurata

i6mA-Fuse: improved and robust prediction of DNA 6 mA sites in the Rosaceae genome by fusing multiple feature representation
Md Mehedi Hasan, Balachandran Manavalan, Watshara Shoombuatong, Mst Shamima Khatun, Hiroyuki Kurata

A molecular dynamics approach to explore the intramolecular signal transduction of PPAR-α
Shaherin Basith, Balachandran Manavalan, Tae Hwan Shin, Gwang Lee

PFDB: A standardized protein folding database with temperature correction
Balachandran Manavalan, Kunihiro Kuwajima, Jooyoung Lee

Prediction of S-nitrosylation sites by integrating support vector machines and random forest
Md Mehedi Hasan, Balachandran Manavalan, Mst Shamima Khatun, Hiroyuki Kurata

Meta-4mCpred: a sequence-based meta-predictor for accurate DNA 4mC site prediction using effective feature representation
Balachandran Manavalan, Shaherin Basith, Tae Hwan Shin, Leyi Wei, Gwang Lee

AtbPpred: a robust sequence-based prediction of anti-tubercular peptides using extremely randomized trees
Balachandran Manavalan, Shaherin Basith, Tae Hwan Shin, Leyi Wei, Gwang Lee

Monitoring of glucose metabolism in vitro following treatment with silica-coated magnetic nanoparticles
Moongi Ji, Chan Seo, Tae Hwan Shin, Da Yeon Lee, Balachandran Manavalan, Shaherin Basith, Suresh Kumar Chakkarapani, Seong Ho Kang, Gwang Lee, Man Jeong Paik, Chan Bae Park

Silica-coated magnetic nanoparticles decrease human bone marrow-derived mesenchymal stem cell migratory activity by reducing membrane fluidity and impairing focal adhesion
Tae Hwan Shin, Da Yeon Lee, Abdurazak Aman Ketebo, Seungah Lee, Balachandran Manavalan, Shaherin Basith, Chanyoung Ahn, Seong Ho Kang, Sungsu Park, Gwang Lee

Bidirectional transcriptome analysis of activated microglia and rat bone marrow-derived mesenchymal stem cells in an in vitro coculture system
Tae Hwan Shin, Shaherin Basith, Balachandran Manavalan, Gwang Lee

mACPpred: a support vector machine-based meta-predictor for identification of anticancer peptides
Vinothini Boopathi, Sathiyamoorthy Subramaniyam, Adeel Malik, Gwang Lee, Balachandran Manavalan, Deok-Chun Yang

Iterative feature representations improve N4-methylcytosine site prediction
Leyi Wei, Ran Su, Shasha Luan, Zhijun Liao, Balachandran Manavalan, Quan Zou, Xiaolong Shi

SDM6A: a web-based integrative machine-learning framework for predicting 6mA sites in the rice genome
Shaherin Basith, Balachandran Manavalan, Tae Hwan Shin, Gwang Lee

mAHTPred: a sequence-based meta-predictor for improving the prediction of anti-hypertensive peptides using effective feature representation
Balachandran Manavalan, Shaherin Basith, Tae Hwan Shin, Leyi Wei, Gwang Lee

4mCpred-EL: An Ensemble Learning Framework for Identification of DNA N4-Methylcytosine Sites in the Mouse Genome
Balachandran Manavalan, Shaherin Basith, Tae Hwan Shin, Da Yeon Lee, Leyi Wei, Gwang Lee

Bidirectional transcriptome analysis of rat bone marrow-derived mesenchymal stem cells and activated microglia in an in vitro co-culture system
Moon Suk Jin Da Yeon Lee, Balachandran Manavalan, Hak Kyun Kim, Jun Hyeok, Tae Hwan Shin Song, Gwang Lee

Protein-carbohydrate interactions
Adeel Malik, Mohammad H Baig, Balachandran Manavalan

Machine-learning-based prediction of cell-penetrating peptides and their uptake efficiency with improved accuracy
Balachandran Manavalan, Sathiyamoorthy Subramaniyam, Tae Hwan Shin, Myeong Ok Kim, Gwang Lee

IkBz
Balachandran Manavalan, Shaherin Basith, Sangdun Choi

iGHBP: computational identification of growth hormone binding proteins from sequences using extremely randomised tree
Shaherin Basith, Balachandran Manavalan, Tae Hwan Shin, Gwang Lee

iBCE-EL: a new ensemble learning framework for improved linear B-cell epitope prediction
Balachandran Manavalan, Rajiv Gandhi Govindaraj, Tae Hwan Shin, Myeong Ok Kim, Gwang Lee

PVP-SVM: sequence-based prediction of phage virion proteins using a support vector machine
Balachandran Manavalan, Tae H Shin, Gwang Lee

PIP-EL: A New Ensemble Learning Method for Improved Proinflammatory Peptide Predictions
Balachandran Manavalan, Shin Tae Hwan, Kim Myeong Ok, and Gwang Lee

MyD88 (Myeloid Differentiation Primary Response Gene 88)
Shaherin Basith, Balachandran Manavalan, Sangdun Choi

Integration of metabolomics and transcriptomics in nanotoxicity studies
Lee G. Shin TH, Lee DY, Lee HS, Park HJ, Jin MS, Paik MJ, Manavalan B, Mo JS

Methods for estimation of model accuracy in CASP12
Arne Elofsson, Keehyoung Joo, Chen Keasar, Jooyoung Lee, Ali HA Maghrabi, Balachandran Manavalan, Liam J McGuffin, David Ménendez Hurtado, Claudio Mirabello, Robert Pilstål, Tomer Sidi, Karolis Uziela, Björn Wallner

AIPpred: sequence-based prediction of anti-inflammatory peptides using random forest
Balachandran Manavalan, Tae H Shin, Myeong O Kim, Gwang Lee

Protein structure modeling and refinement by global optimization in CASP12
Seung Hwan Hong, InSuk Joung, Jose C Flores‐Canales, Balachandran Manavalan, Qianyi Cheng, Seungryong Heo, Jong Yun Kim, Sun Young Lee, Mikyung Nam, Keehyoung Joo, In‐Ho Lee, Sung Jong Lee, Jooyoung Lee

MLACP: machine-learning-based prediction of anticancer peptides
Balachandran Manavalan, Shaherin Basith, Tae Hwan Shin, Sun Choi, Myeong Ok Kim, Gwang Lee

DHSpred: support-vector-machine-based human DNase I hypersensitive sites prediction using the optimal features selected by random forest
Balachandran Manavalan, Tae Hwan Shin, Gwang Lee

SVMQA: support–vector-machine-based protein single-model quality assessment
Balachandran Manavalan, Jooyoung Lee

SVMQA: Support-vector-machine-based protein single-model quality assessment [J]
Manavalan Balachandran, Lee Jooyoung

MyD88 (Myeloid Differentiation Primary Response Gene 88)
Shaherin Basith, Balachandran Manavalan, Sangdun Choi

IkBz
Balachandran Manavalan, Shaherin Basith, Sangdun Choi

Template based protein structure modeling by global optimization in CASP 11
Keehyoung Joo, InSuk Joung, Sun Young Lee, Jong Yun Kim, Qianyi Cheng, Balachandran Manavalan, Jong Young Joung, Seungryong Heo, Juyong Lee, Mikyung Nam, In‐Ho Lee, Sung Jong Lee, Jooyoung Lee

Structure-based protein folding type classification and folding rate prediction
Balachandran Manavalan, Kunihiro Kuwajima, Insuk Joung, Jooyoung Lee

Random forest-based protein model quality assessment (RFMQA) using structural features and potential energy terms
Balachandran Manavalan, Juyong Lee, Jooyoung Lee

Evolutionary, structural and functional interplay of the IκB family members
Shaherin Basith, Balachandran Manavalan, Vijayakumar Gosu, Sangdun Choi

Roles of toll-like receptors in cancer: a double-edged sword for defense and offense
Shaherin Basith, Balachandran Manavalan, Tae Hyeon Yoo, Sang Geon Kim, Sangdun Choi

Molecular modeling‐based evaluation of dual function of IκBζ ankyrin repeat domain in toll‐like receptor signaling
Balachandran Manavalan, Rajivgandhi Govindaraj, Gwang Lee, Sangdun Choi

In silico approach to inhibition of signaling pathways of Toll-like receptors 2 and 4 by ST2L
Shaherin Basith, Balachandran Manavalan, Rajiv Gandhi Govindaraj, Sangdun Choi

Comparative analysis of species-specific ligand recognition in Toll-like receptor 8 signaling: a hypothesis
Rajiv Gandhi Govindaraj, Balachandran Manavalan, Shaherin Basith, Sangdun Choi

Similar structures but different roles–an updated perspective on TLR structures
Balachandran Manavalan, Shaherin Basith, Sangdun Choi

Toll-like receptor modulators: a patent review (2006–2010)
Shaherin Basith, Balachandran Manavalan, Gwang Lee, Sang Geon Kim, Sangdun Choi

Computational Approaches to Identify the Structure-Function Relationships of IκB Proteins in Toll-like Receptor Signaling
Balachandran Manavalan

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
Balachandran Manavalan, Senthil K Murugapiran, Gwang Lee, Sangdun Choi

Structure-function relationship of cytoplasmic and nuclear IκB proteins: an in silico analysis
Balachandran Manavalan, Shaherin Basith, Yong-Min Choi, Gwang Lee, Sangdun Choi

Molecular modeling-based evaluation of hTLR10 and identification of potential ligands in Toll-like receptor signaling
Rajiv Gandhi Govindaraj, Balachandran Manavalan, Gwang Lee, Sangdun Choi