M. Michael Gromiha

发表

Bioinformatics on beta-Barrel Membrane Proteins: Sequence and Structural Analysis, Discrimination and Prediction

M. Michael Gromiha, 2007, PRIB.

Intelligent Computing Theories and Applications: 8th International Conference, ICIC 2012, Huangshan, China, July 25-29, 2012

Jianhua Ma, Kang-Hyun Jo, De-Shuang Huang, 2012 .

Database Development and Discrimination Algorithms for Membrane Protein Functions

Paul Horton, M. Michael Gromiha, Yukimitsu Yabuki, 2009 .

TMFunction: database for functional residues in membrane proteins

M. Michael Gromiha, Yukimitsu Yabuki, Kazuhiko Fukui, 2008, Nucleic Acids Res..

A Statistical Model for Predicting Protein Folding Rates from Amino Acid Sequence with Structural Class Information

M. Michael Gromiha, M. Gromiha, M. Gromiha, 2005, J. Chem. Inf. Model..

Importance of Native-State Topology for Determining the Folding Rate of Two-State Proteins

M. Michael Gromiha, M. Gromiha, M. Gromiha, 2003, J. Chem. Inf. Comput. Sci..

Gene Ontology term prediction based upon amino acid occurrence

Y-h. Taguchi, M. Michael Gromiha, 2008, 2008 IEEE International Joint Conference on Neural Networks (IEEE World Congress on Computational Intelligence).

Intelligent Computing Methodologies

Yuzuru Tanaka, Kyungsook Han, De-Shuang Huang, 2018, Lecture Notes in Computer Science.

A Statistical Method for Predicting Protein Unfolding Rates from Amino Acid Sequence

M. Michael Gromiha, A. Mary Thangakani, Samuel Selvaraj, 2006, J. Chem. Inf. Model..

Intelligent Computing Methodologies

Kyungsook Han, De-Shuang Huang, M. Michael Gromiha, 2017, Lecture Notes in Computer Science.

PINT: Protein–protein Interactions Thermodynamic Database

M. Michael Gromiha, M. D. Shaji Kumar, M. Gromiha, 2005, Nucleic Acids Res..

Analysis and prediction of protein folding rates using quadratic response surface models

Liang-Tsung Huang, M. Michael Gromiha, M. Gromiha, 2008, J. Comput. Chem..

Multicanonical Monte Carlo calculation of the free-energy map of the base-amino acid interaction

Akinori Sarai, M. Michael Gromiha, Hidetoshi Kono, 2000, J. Comput. Chem..

Discrimination and Prediction of Protein-Protein Binding Affinity Using Deep Learning Approach

M. Michael Gromiha, K. Yugandhar, Rahul Nikam, 2018, ICIC.

NETASA: neural network based prediction of solvent accessibility

Shandar Ahmad, M. Michael Gromiha, M. Gromiha, 2002, Bioinform..

ProTherm, Thermodynamic Database for Proteins and Mutants: developments in version 3.0

Akinori Sarai, M. Michael Gromiha, Jianghong An, 2002, Nucleic Acids Res..

Influence of Amino Acid Properties for Characterizing Amyloid Peptides in Human Proteome

M. Michael Gromiha, Sandeep Kumar, Rahul Nikam, 2017, ICIC.

ProTherm and ProNIT: thermodynamic databases for proteins and protein–nucleic acid interactions

Akinori Sarai, M. Michael Gromiha, M. D. Shaji Kumar, 2005, Nucleic Acids Res..

Identifying suitable spliced mapper and genomic assembly for variant detection

Y-h. Taguchi, M. Michael Gromiha, S. Akila Parvathy Ranjith, 2018 .

Introduction: advanced intelligent computing theories and their applications in bioinformatics

De-Shuang Huang, M. Michael Gromiha, De-shuang Huang, 2012, BMC Bioinformatics.

Computational Analysis of Similar Protein-DNA Complexes from Different Organisms to Understand Organism Specific Recognition

M. Michael Gromiha, R. Nagarajan, 2016, ICIC.

RVP-net: online prediction of real valued accessible surface area of proteins from single sequences

Shandar Ahmad, Akinori Sarai, M. Michael Gromiha, 2003, Bioinform..

TMBETA-NET: discrimination and prediction of membrane spanning β-strands in outer membrane proteins

Shandar Ahmad, M. Michael Gromiha, Makiko Suwa, 2005, Nucleic Acids Res..

Biopharmaceutical Informatics: Applications of Computation in Biologic Drug Development

Li Li, Sandeep Kumar, M. Michael Gromiha, 2015 .

Identification of Novel c-Yes Kinase Inhibitors

M. Michael Gromiha, A. Mary Thangakani, C. Ramakrishnan, 2014, ICIC.

GAP: towards almost 100 percent prediction for β-strand-mediated aggregating peptides with distinct morphologies

Sandeep Kumar, M. Michael Gromiha, A. Mary Thangakani, 2014, Bioinform..

Guest Editorial for Special Section on the 10th International Conference on Intelligent Computing (ICIC)

De-Shuang Huang, Vitoantonio Bevilacqua, M. Michael Gromiha, 2016, TCBB.

TMB Finding Pipeline: Novel Approach for Detecting β-Barrel Membrane Proteins in Genomic Sequences

M. Michael Gromiha, Yukimitsu Yabuki, Makiko Suwa, 2007, J. Chem. Inf. Model..

PROXiMATE: a database of mutant protein-protein complex thermodynamics and kinetics

M. Michael Gromiha, K. Yugandhar, Sherlyn Jemimah, 2017, Bioinform..

Application of residue distribution along the sequence for discriminating outer membrane proteins

Shandar Ahmad, M. Michael Gromiha, Makiko Suwa, 2005, Comput. Biol. Chem..

Guest Editorial for Special Section on the 14th International Conference on Intelligent Computing (ICIC)

Vitoantonio Bevilacqua, M. Michael Gromiha, De-Shuang Huang, 2020, IEEE ACM Trans. Comput. Biol. Bioinform..

CUPSAT: prediction of protein stability upon point mutations

M. Michael Gromiha, Dietmar Schomburg, Vijaya Parthiban, 2006, Nucleic Acids Res..

Knowledge acquisition and development of accurate rules for predicting protein stability changes

Shiow-Fen Hwang, Liang-Tsung Huang, M. Michael Gromiha, 2006, Comput. Biol. Chem..

Neural network‐based prediction of transmembrane β‐strand segments in outer membrane proteins

Shandar Ahmad, M. Michael Gromiha, Makiko Suwa, 2004, J. Comput. Chem..

Relationship between amino acid properties and functional parameters in olfactory receptors and discrimination of mutants with enhanced specificity

M. Michael Gromiha, Kazuhiko Fukui, K. Harini, 2012, BMC Bioinformatics.

A simple statistical method for discriminating outer membrane proteins with better accuracy

M. Michael Gromiha, Makiko Suwa, M. Gromiha, 2005, Bioinform..

FOLD-RATE: prediction of protein folding rates from amino acid sequence

M. Michael Gromiha, A. Mary Thangakani, Samuel Selvaraj, 2006, Nucleic Acids Res..

Structure-Function Relationship in Olfactory Receptors

M. Michael Gromiha, Kazuhiko Fukui, R. Sowdhamini, 2011, ICIC.

Prediction of membrane spanning segments and topology in β‐barrel membrane proteins at better accuracy

Yu-Yen Ou, M. Michael Gromiha, Shu-An Chen, 2010, J. Comput. Chem..

ProCaff: protein-carbohydrate complex binding affinity database

M. Michael Gromiha, N. R. Siva Shanmugam, J. Jino Blessy, 2020, Bioinform..

Scoring Function Based Approach for Locating Binding Sites and Understanding Recognition Mechanism of Protein-DNA Complexes

M. Michael Gromiha, Kazuhiko Fukui, K. Fukui, 2011, J. Chem. Inf. Model..

Distinct position-specific sequence features of hexa-peptides that form amyloid-fibrils: application to discriminate between amyloid fibril and amorphous β-aggregate forming peptide sequences

Sandeep Kumar, M. Michael Gromiha, A. Mary Thangakani, 2013, BMC Bioinformatics.

Response to the comment on 'protein-protein binding affinity prediction from amino acid sequence'

M. Michael Gromiha, K. Yugandhar, 2015, Bioinform..

Prediction of transporter targets using efficient RBF networks with PSSM profiles and biochemical properties

Yu-Yen Ou, M. Michael Gromiha, Tzong-Yi Lee, 2011, Bioinform..

Data mining application in biomedical informatics for probing into protein stability upon double mutation

M. Michael Gromiha, Chao-Chin Wu, Lien-Fu Lai, 2014 .

Neural network based prediction of protein structure and Function: Comparison with other machine learning methods

Shandar Ahmad, M. Michael Gromiha, Makiko Suwa, 2008, 2008 IEEE International Joint Conference on Neural Networks (IEEE World Congress on Computational Intelligence).

Real value prediction of protein folding rate change upon point mutation

M. Michael Gromiha, Liang-Tsung Huang, 2012, Journal of Computer-Aided Molecular Design.

Design and training of a neural network for predicting the solvent accessibility of proteins

Shandar Ahmad, M. Michael Gromiha, M. Gromiha, 2003, J. Comput. Chem..

Identification of DNA-binding proteins using support vector machines and evolutionary profiles

Gajendra P. S. Raghava, Manish Kumar, M. Michael Gromiha, 2007, BMC Bioinformatics.

Folding RaCe: a robust method for predicting changes in protein folding rates upon point mutations

M. Michael Gromiha, Athi N. Naganathan, Priyashree Chaudhary, 2015, Bioinform..

Development of a Machine Learning Method to Predict Membrane Protein-Ligand Binding Residues Using Basic Sequence Information

M. Michael Gromiha, M. Xavier Suresh, Makiko Suwa, 2015, Adv. Bioinformatics.

Sequence and structural features of binding site residues in protein-protein complexes

M. Michael Gromiha, Kazuhiko Fukui, B. Jayaram, 2010, 2010 IEEE International Conference on Bioinformatics and Biomedicine (BIBM).

Reliable prediction of protein thermostability change upon double mutation from amino acid sequence

Liang-Tsung Huang, M. Michael Gromiha, M. Gromiha, 2009, Bioinform..

ccPDB: compilation and creation of data sets from Protein Data Bank

Gajendra P. S. Raghava, M. Michael Gromiha, Jagat Singh Chauhan, 2012, Nucleic Acids Res..

Development of knowledge-based system for predicting the stability of proteins upon point mutations

Lien Fu Lai, Liang-Tsung Huang, M. Michael Gromiha, 2010, Neurocomputing.

Protein Fold Recognition Based Upon the Amino Acid Occurrence

M. Michael Gromiha, Yoshihiro Taguchi, 2007, PRIB.

Human-Readable Rule Generator for Integrating Amino Acid Sequence Information and Stability of Mutant Proteins

Lien Fu Lai, Liang-Tsung Huang, M. Michael Gromiha, 2010, IEEE/ACM Transactions on Computational Biology and Bioinformatics.

Identification and Analysis of Key Residues in Protein–RNA Complexes

M. Michael Gromiha, Pradeep Kumar, R. Nagarajan, 2018, IEEE/ACM Transactions on Computational Biology and Bioinformatics.

Discrimination of outer membrane proteins using support vector machines

Paul Horton, M. Michael Gromiha, Makiko Suwa, 2005, Bioinform..

Protein-protein binding affinity prediction from amino acid sequence

M. Michael Gromiha, K. Yugandhar, M. Gromiha, 2014, Bioinform..

Guest Editorial for Special Section on the 11th International Conference on Intelligent Computing ICIC

De-Shuang Huang, Vitoantonio Bevilacqua, M. Michael Gromiha, 2017, TCBB.

Role of Protein Aggregation and Interactions between α-Synuclein and Calbindin in Parkinson's Disease

Sandeep Kumar, M. Michael Gromiha, A. Mary Thangakani, 2013, ICIC.

Multi-omics-based identification of SARS-CoV-2 infection biology and candidate drugs against COVID-19

M. Michael Gromiha, Debmalya Barh, Preetam Ghosh, 2020, Computers in Biology and Medicine.

Development of a fragment library for solvent accessibility prediction and comparison with neural network approach

Shandar Ahmad, Akinori Sarai, M. Michael Gromiha, 2003 .

COMPUTATIONAL APPROACHES FOR UNDERSTANDING THE RECOGNITION MECHANISM OF PROTEIN COMPLEXES

M. Michael Gromiha, 2013 .

TMBETA-GENOME: database for annotated β-barrel membrane proteins in genomic sequences

M. Michael Gromiha, Yukimitsu Yabuki, Makiko Suwa, 2006, Nucleic Acids Res..

Guest Editorial for Special Section on the 12th International Conference on Intelligent Computing ICIC

De-Shuang Huang, Vitoantonio Bevilacqua, M. Michael Gromiha, 2018, TCBB.

Multiple Contact Network Is a Key Determinant to Protein Folding Rates

M. Michael Gromiha, M. Gromiha, 2009, J. Chem. Inf. Model..

First insight into the prediction of protein folding rate change upon point mutation

Liang-Tsung Huang, M. Michael Gromiha, M. Gromiha, 2010, Bioinform..

Structure-Function Relationship in DNA Sequence Recognition by Transcription Factors

Akinori Sarai, M. Michael Gromiha, Samuel Selvaraj, 2004, APBC.

PDBparam: Online Resource for Computing Structural Parameters of Proteins

M. Michael Gromiha, A. Mary Thangakani, D. Velmurugan, 2016, Bioinformatics and biology insights.

Protein Bioinformatics: From Sequence to Function

M. Michael Gromiha, M. Gromiha, 2010 .

ASAView: Database and tool for solvent accessibility representation in proteins

Shandar Ahmad, Akinori Sarai, M. Michael Gromiha, 2003, BMC Bioinformatics.

Application of amino acid occurrence for discriminating different folding types of globular proteins

Y-h. Taguchi, M. Michael Gromiha, M. Gromiha, 2007, BMC Bioinformatics.

Analysis and prediction of DNA-binding proteins and their binding residues based on composition, sequence and structural information

Shandar Ahmad, Akinori Sarai, M. Michael Gromiha, 2004, Bioinform..

In silico identification of essential proteins in Corynebacterium pseudotuberculosis based on protein-protein interaction networks

Preetam Ghosh, M. Michael Gromiha, Debmalya Barh, 2016, BMC Systems Biology.

ProTherm: Thermodynamic Database for Proteins and Mutants

Akinori Sarai, M. Michael Gromiha, Jianghong An, 1999, Nucleic Acids Res..

Topology Prediction of alpha-Helical and beta-Barrel Transmembrane Proteins Using RBF Networks

Yu-Yen Ou, M. Michael Gromiha, Shu-An Chen, 2010, ICIC.

ProTherm, version 2.0: thermodynamic database for proteins and mutants

Akinori Sarai, M. Michael Gromiha, Jianghong An, 2000, Nucleic Acids Res..

Advanced Intelligent Computing Theories and Applications: 7th International Conference

Yong Gan, De-Shuang Huang, Phalguni Gupta, 2012 .

Guest Editorial for Special Section on the 13th International Conference on Intelligent Computing ICIC

De-Shuang Huang, Vitoantonio Bevilacqua, M. Michael Gromiha, 2019, TCBB.

Sequence Analysis and Discrimination of Amyloid and Non-amyloid Peptides

Sandeep Kumar, M. Michael Gromiha, A. Mary Thangakani, 2012, ICIC.

Thermodynamic database for protein-nucleic acid interactions (ProNIT)

Akinori Sarai, M. Michael Gromiha, Jianghong An, 2001, Bioinform..

Bioinformatics approaches for functional annotation of membrane proteins

Yu-Yen Ou, M. Michael Gromiha, M. Gromiha, 2014, Briefings Bioinform..

Integration of Bioinformatics and Computational Biology to Understand Protein-dna Recognition Mechanism

Akinori Sarai, M. Michael Gromiha, Samuel Selvaraj, 2005, J. Bioinform. Comput. Biol..

Editorial [Hot topic: Protein Folding, Stability and Interactions (Guest Editor: M. Michael Gromiha)]

M. Michael Gromiha, M. Gromiha, 2010 .

Functional discrimination of membrane proteins using machine learning techniques

M. Michael Gromiha, Yukimitsu Yabuki, M. Gromiha, 2008, BMC Bioinformatics.

Editorial [Hot Topic: Bioinformatics on Proteins and Complexes (Guest Editor: M. Michael Gromiha)]

M. Michael Gromiha, 2012 .

High throughput computing to improve efficiency of predicting protein stability change upon mutation

Lien Fu Lai, Liang-Tsung Huang, M. Michael Gromiha, 2014, Int. J. Data Min. Bioinform..

Sequence Based Prediction of Protein Mutant Stability and Discrimination of Thermophilic Proteins

Lien Fu Lai, Liang-Tsung Huang, M. Michael Gromiha, 2008, PRIB.

Computer Algorithms for Discriminating Protein Folds and Predicting Protein Folding Rates Based on Contact Information

M. Michael Gromiha, M. Gromiha, 2009, 2009 International Association of Computer Science and Information Technology - Spring Conference.

SRide: a server for identifying stabilizing residues in proteins

M. Michael Gromiha, Csaba Magyar, István Simon, 2005, Nucleic Acids Res..

Relative importance of secondary structure and solvent accessibility to the stability of protein mutants.: A case study with amino acid properties and energetics on T4 and human lysozymes

M. Michael Gromiha, K. Saraboji, Mon Nanjappa Ponnuswamy, 2005, Comput. Biol. Chem..

Development of Residue Look-Up Tables and Graphical Representation of Solvent Accessibility in Proteins

Shandar Ahmad, Akinori Sarai, M. Michael Gromiha, 2003 .

iPTREE-STAB: interpretable decision tree based method for predicting protein stability changes upon mutations

Liang-Tsung Huang, M. Michael Gromiha, Shinn-Ying Ho, 2007, Bioinform..

Identification and Analysis of Binding Site Residues in Protein Complexes: Energy Based Approach

M. Michael Gromiha, Kazuhiko Fukui, B. Jayaram, 2010, ICIC.

TMBETADISC-RBF: Discrimination of beta-barrel membrane proteins using RBF networks and PSSM profiles

Yu-Yen Ou, M. Michael Gromiha, Makiko Suwa, 2008, Comput. Biol. Chem..

Highlights from the 11th ISCB Student Council Symposium 2015

Giovanni Felici, Farzana Rahman, M. Michael Gromiha, 2016, BMC Bioinformatics.

Computational approaches for identifying potential inhibitors on targeting protein interactions in drug discovery.

M. Michael Gromiha, R. Sakthivel, Vishnupriya Kanakaveti, 2020, Advances in protein chemistry and structural biology.

MutHTP: mutations in human transmembrane proteins

Dmitrij Frishman, M. Michael Gromiha, R. Sakthivel, 2018, Bioinform..

Protein Structural Bioinformatics: An Overview

M. Michael Gromiha, Samuel Selvaraj, R. Nagarajan, 2019, Encyclopedia of Bioinformatics and Computational Biology.

Proceedings of the 8th international conference on Intelligent Computing Theories and Applications

Jianhua Ma, Kang-Hyun Jo, De-Shuang Huang, 2012 .

Prediction of protein-carbohydrate complex binding affinity using structural features

M. Michael Gromiha, J. Jino Blessy, K. Veluraja, 2020, Briefings Bioinform..

ProThermDB: thermodynamic database for proteins and mutants revisited after 15 years

M. Michael Gromiha, A. Kulandaisamy, Rahul Nikam, 2020, Nucleic Acids Res..

Mutations in transmembrane proteins: diseases, evolutionary insights, prediction and comparison with globular proteins

Dmitrij Frishman, M. Michael Gromiha, Petr Popov, 2020, Briefings Bioinform..

Guest Editorial for Special Section on the 15th International Conference on Intelligent Computing (ICIC)

De-Shuang Huang, Vitoantonio Bevilacqua, M. Michael Gromiha, 2021, IEEE ACM Trans. Comput. Biol. Bioinform..

Prediction of RNA Binding Residues: An Extensive Analysis Based on Structure and Function to Select the Best Predictor

M. Michael Gromiha, R. Nagarajan, M. Gromiha, 2014, PloS one.

Quantitative structure–activity relationship in ligand-based drug design: Concepts and applications

M. Michael Gromiha, Vishnupriya Kanakaveti, P. Anoosha, 2020 .

3P068 Latest developments in ProTherm : Thermodynamic Database for Proteins and Mutants

M. Michael Gromiha, Abdulla Bava, Uedaira, 2004 .

Chapter 1 – Proteins

M. Michael Gromiha, M. Gromiha, 2010 .

Prediction of Bendability and Curvature in Genomic DNA

M. Michael Gromiha, Andrei Gabrielian, Sándor Pongor, 1998 .

Drug repositioning for dengue haemorrhagic fever by integrating multiple omics analyses

M. Michael Gromiha, Katsuhisa Horimoto, Takayuki Amemiya, 2019, Scientific Reports.

Structural and Functional Discrimination of Membrane Proteins

Yu-Yen Ou, M. Michael Gromiha, Shu-An Chen, 2011 .

Sequence analysis and rule development of predicting protein stability change upon mutation using decision tree model

Liang-Tsung Huang, M. Michael Gromiha, Shinn-Ying Ho, 2007, Journal of molecular modeling.

On the conformational stability of folded proteins

M. Michael Gromiha, P. K. Ponnuswamy, 1994 .

A Statistical Method for Predicting Protein Unfolding Rates from Amino Acid Sequence.

M. Michael Gromiha, A. Mary Thangakani, Samuel Selvaraj, 2006 .

Target Prediction of Transcription Factors: Refinement of Structure-Based Method

M. Michael Gromiha, Samuel Selvaraj, Ponraj Prabakaran, 2001 .

ProTherm: Thermodynamic Database for Proteins and Mutants

M. Michael Gromiha, Jianghong An, Hidetoshi Kono, 1998 .

Temperature‐Dependent Molecular Adaptations, Microbial Proteins

M. Michael Gromiha, Satish K. Singh, Sandeep Kumar, 2010 .

Factors influencing the thermal stability of buried protein mutants

M. Michael Gromiha, M. Gromiha, 2003 .

Thermodynamic Database for Protein-Nucleic Acid Interactions

M. Michael Gromiha, Jianghong An, Samuel Selvaraj, 1999 .

Importance of Native-State Topology for Determining the Folding Rate of Two-State Proteins.

M. Michael Gromiha, 2003 .

Relationship Between Amino Acid Properties and Protein Compressibility

M. Michael Gromiha, P. K. Ponnuswamy, M. Gromiha, 1993 .

Structural basis of flavonoids as dengue polymerase inhibitors: insights from QSAR and docking studies

M. Michael Gromiha, M. Gromiha, R. Sarma, 2019, Journal of biomolecular structure & dynamics.

Protein Structure Prediction

M. Michael Gromiha, M. Gromiha, 2010 .

A simple method for predicting transmembrane α helices with better accuracy

M. Michael Gromiha, M. Gromiha, 1999 .

1P207 Classification of Protein-DNA Complexes Based on Structural Descriptors(7. Nucleic acid binding protein,Poster Session,Abstract,Meeting Program of EABS & BSJ 2006)

M. Michael Gromiha, Ponraj Prabakaran, Akinori Sarai, 2006 .

Mitochondrial β-Barrel Proteins, an Exclusive Club?

Paul Horton, M. Michael Gromiha, Kenichiro Imai, 2008, Cell.

Free-Energy Maps of Base−Amino Acid Interactions for DNA−Protein Recognition

M. Michael Gromiha, Misako Aida, Fabio Pichierri, 1999 .

Development of RNA Stiffness Parameters and Analysis on Protein-RNA Binding Specificity: Comparison with DNA

M. Michael Gromiha, M. Gromiha, 2012 .

Multicanonical Monte Carlo calculation of the free‐energy map of the base–amino acid interaction

M. Michael Gromiha, Hidetoshi Kono, Akinori Sarai, 2000 .

Role of Hydrophobic Clusters and Long-Range Contact Networks in the Folding of (α/β)8 Barrel Proteins

M. Michael Gromiha, Samuel Selvaraj, M. Gromiha, 2003 .

Recognition of protein folds using amino acid occurrence

M. Michael Gromiha, Y-h. Taguchi, 2006 .

Exploring the environmental preference of weak interactions in (α/β)8 barrel proteins

M. Michael Gromiha, M. Madan Babu, Rao Sethumadhavan, 2006 .

An Analysis of the Amino Acid Clustering Pattern in (α/β)8 Barrel Proteins

M. Michael Gromiha, Samuel Selvaraj, 1998 .

Influence of cation–π interactions in protein–DNA complexes

M. Michael Gromiha, Makiko Suwa, C. Santhosh, 2004 .

Computational studies of drug repurposing and synergism of lopinavir, oseltamivir and ritonavir binding with SARS-CoV-2 protease against COVID-19

M. Michael Gromiha, D. Velmurugan, M. Gromiha, 2020, Journal of biomolecular structure & dynamics.

Influence of Medium and Long Range Interactions in Different Structural Classes of Globular Proteins

M. Michael Gromiha, M. Gromiha, S. Selvaraj, 1997, Journal of biological physics.

Recent Developments in ProNIT: Thermodynamic Database for Protein-Nucleic Acid Interactions

M. Michael Gromiha, Jianghong An, Samuel Selvaraj, 2001 .

TMB Finding Pipeline: Novel Approach for Detecting β-Barrel Membrane Proteins in Genomic Sequences.

M. Michael Gromiha, Yukimitsu Yabuki, Makiko Suwa, 2008 .

Importance of Long-Range Interactions for Determining the Folding Rate and Transition State Structures of Two-state Proteins

M. Michael Gromiha, Samuel Selvaraj, 2002 .