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M. Michael Gromiha: [Publications] [Author Rank by year] [Co-authors] [Prefers] [Cites] [Cited by]

Publications of Author

  1. Akinori Sarai, Samuel Selvaraj, M. Michael Gromiha, Hidetoshi Kono
    Structure-Function Relationship in DNA Sequence Recognition by Transcription Factors. [Citation Graph (0, 0)][DBLP]
    APBC, 2004, pp:233-238 [Conf]
  2. Shandar Ahmad, M. Michael Gromiha
    NETASA: neural network based prediction of solvent accessibility. [Citation Graph (0, 0)][DBLP]
    Bioinformatics, 2002, v:18, n:6, pp:819-824 [Journal]
  3. Shandar Ahmad, M. Michael Gromiha, Akinori Sarai
    RVP-net: online prediction of real valued accessible surface area of proteins from single sequences. [Citation Graph (0, 0)][DBLP]
    Bioinformatics, 2003, v:19, n:14, pp:1849-1851 [Journal]
  4. Shandar Ahmad, M. Michael Gromiha, Akinori Sarai
    Analysis and prediction of DNA-binding proteins and their binding residues based on composition, sequence and structural information. [Citation Graph (0, 0)][DBLP]
    Bioinformatics, 2004, v:20, n:4, pp:- [Journal]
  5. M. Michael Gromiha, Makiko Suwa
    A simple statistical method for discriminating outer membrane proteins with better accuracy. [Citation Graph (0, 0)][DBLP]
    Bioinformatics, 2005, v:21, n:7, pp:961-968 [Journal]
  6. Keun-Joon Park, M. Michael Gromiha, Paul Horton, Makiko Suwa
    Discrimination of outer membrane proteins using support vector machines. [Citation Graph (0, 0)][DBLP]
    Bioinformatics, 2005, v:21, n:23, pp:4223-4229 [Journal]
  7. Ponraj Prabakaran, Jianghong An, M. Michael Gromiha, Samuel Selvaraj, Hatsuho Uedaira, Hidetoshi Kono, Akinori Sarai
    Thermodynamic database for protein-nucleic acid interactions (ProNIT). [Citation Graph (0, 0)][DBLP]
    Bioinformatics, 2001, v:17, n:11, pp:1027-1034 [Journal]
  8. Shandar Ahmad, M. Michael Gromiha, Hamed Fawareh, Akinori Sarai
    ASAView: Database and tool for solvent accessibility representation in proteins. [Citation Graph (0, 0)][DBLP]
    BMC Bioinformatics, 2004, v:5, n:, pp:51- [Journal]
  9. M. Michael Gromiha, Shandar Ahmad, Makiko Suwa
    Application of residue distribution along the sequence for discriminating outer membrane proteins. [Citation Graph (0, 0)][DBLP]
    Computational Biology and Chemistry, 2005, v:29, n:2, pp:135-142 [Journal]
  10. K. Saraboji, M. Michael Gromiha, Mon Nanjappa Ponnuswamy
    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. [Citation Graph (0, 0)][DBLP]
    Computational Biology and Chemistry, 2005, v:29, n:1, pp:25-35 [Journal]
  11. Liang-Tsung Huang, M. Michael Gromiha, Shiow-Fen Hwang, Shinn-Ying Ho
    Knowledge acquisition and development of accurate rules for predicting protein stability changes. [Citation Graph (0, 0)][DBLP]
    Computational Biology and Chemistry, 2006, v:30, n:6, pp:408-415 [Journal]
  12. Akinori Sarai, Jorg Siebers, Samuel Selvaraj, M. Michael Gromiha, Hidetoshi Kono
    Integration of Bioinformatics and Computational Biology to Understand Protein-dna Recognition Mechanism. [Citation Graph (0, 0)][DBLP]
    J. Bioinformatics and Computational Biology, 2005, v:3, n:1, pp:169-183 [Journal]
  13. Shandar Ahmad, M. Michael Gromiha
    Design and training of a neural network for predicting the solvent accessibility of proteins. [Citation Graph (0, 0)][DBLP]
    Journal of Computational Chemistry, 2003, v:24, n:11, pp:1313-1320 [Journal]
  14. M. Michael Gromiha, Shandar Ahmad, Makiko Suwa
    Neural network-based prediction of transmembrane -strand segments in outer membrane proteins. [Citation Graph (0, 0)][DBLP]
    Journal of Computational Chemistry, 2004, v:25, n:5, pp:762-767 [Journal]
  15. Kenji Sayano, Hidetoshi Kono, M. Michael Gromiha, Akinori Sarai
    Multicanonical Monte Carlo calculation of the free-energy map of the base-amino acid interaction. [Citation Graph (0, 0)][DBLP]
    Journal of Computational Chemistry, 2000, v:21, n:11, pp:954-962 [Journal]
  16. M. Michael Gromiha
    Importance of Native-State Topology for Determining the Folding Rate of Two-State Proteins. [Citation Graph (0, 0)][DBLP]
    Journal of Chemical Information and Computer Sciences, 2003, v:43, n:5, pp:1481-1485 [Journal]
  17. M. Michael Gromiha
    A Statistical Model for Predicting Protein Folding Rates from Amino Acid Sequence with Structural Class Information. [Citation Graph (0, 0)][DBLP]
    Journal of Chemical Information and Modeling, 2005, v:45, n:2, pp:494-501 [Journal]
  18. M. Michael Gromiha, Samuel Selvaraj, A. Mary Thangakani
    A Statistical Method for Predicting Protein Unfolding Rates from Amino Acid Sequence. [Citation Graph (0, 0)][DBLP]
    Journal of Chemical Information and Modeling, 2006, v:46, n:3, pp:1503-1508 [Journal]
  19. K. Abdulla Bava, M. Michael Gromiha, Hatsuho Uedaira, Koji Kitajima, Akinori Sarai
    ProTherm, version 4.0: thermodynamic database for proteins and mutants. [Citation Graph (0, 0)][DBLP]
    Nucleic Acids Research, 2004, v:32, n:Database-Issue, pp:120-121 [Journal]
  20. M. Michael Gromiha, Jianghong An, Hidetoshi Kono, Motohisa Oobatake, Hatsuho Uedaira, Ponraj Prabakaran, Akinori Sarai
    ProTherm, version 2.0: thermodynamic database for proteins and mutants. [Citation Graph (0, 0)][DBLP]
    Nucleic Acids Research, 2000, v:28, n:1, pp:283-285 [Journal]
  21. M. Michael Gromiha, Jianghong An, Hidetoshi Kono, Motohisa Oobatake, Hatsuho Uedaira, Akinori Sarai
    ProTherm: Thermodynamic Database for Proteins and Mutants. [Citation Graph (0, 0)][DBLP]
    Nucleic Acids Research, 1999, v:27, n:1, pp:286-288 [Journal]
  22. M. Michael Gromiha, Hatsuho Uedaira, Jianghong An, Samuel Selvaraj, Ponraj Prabakaran, Akinori Sarai
    ProTherm, Thermodynamic Database for Proteins and Mutants: developments in version 3.0. [Citation Graph (0, 0)][DBLP]
    Nucleic Acids Research, 2002, v:30, n:1, pp:301-302 [Journal]
  23. M. Michael Gromiha, Shandar Ahmad, Makiko Suwa
    TMBETA-NET: discrimination and prediction of membrane spanning ß-strands in outer membrane proteins. [Citation Graph (0, 0)][DBLP]
    Nucleic Acids Research, 2005, v:33, n:Web-Server-Issue, pp:164-167 [Journal]
  24. Csaba Magyar, M. Michael Gromiha, Gerard Pujadas, Gábor E. Tusnády, István Simon
    SRide: a server for identifying stabilizing residues in proteins. [Citation Graph (0, 0)][DBLP]
    Nucleic Acids Research, 2005, v:33, n:Web-Server-Issue, pp:303-305 [Journal]
  25. M. D. Shaji Kumar, M. Michael Gromiha
    PINT: Protein-protein Interactions Thermodynamic Database. [Citation Graph (0, 0)][DBLP]
    Nucleic Acids Research, 2006, v:34, n:Database-Issue, pp:195-198 [Journal]
  26. M. D. Shaji Kumar, K. Abdulla Bava, M. Michael Gromiha, Ponraj Prabakaran, Koji Kitajima, Hatsuho Uedaira, Akinori Sarai
    ProTherm and ProNIT: thermodynamic databases for proteins and protein-nucleic acid interactions. [Citation Graph (0, 0)][DBLP]
    Nucleic Acids Research, 2006, v:34, n:Database-Issue, pp:204-206 [Journal]
  27. Vijaya Parthiban, M. Michael Gromiha, Dietmar Schomburg
    CUPSAT: prediction of protein stability upon point mutations. [Citation Graph (0, 0)][DBLP]
    Nucleic Acids Research, 2006, v:34, n:Web-Server-Issue, pp:239-242 [Journal]
  28. M. Michael Gromiha, A. Mary Thangakani, Samuel Selvaraj
    FOLD-RATE: prediction of protein folding rates from amino acid sequence. [Citation Graph (0, 0)][DBLP]
    Nucleic Acids Research, 2006, v:34, n:Web-Server-Issue, pp:70-74 [Journal]
  29. M. Michael Gromiha, Yukimitsu Yabuki, Srinesh Kundu, Sivasundaram Suharnan, Makiko Suwa
    TMBETA-GENOME: database for annotated ß-barrel membrane proteins in genomic sequences. [Citation Graph (0, 0)][DBLP]
    Nucleic Acids Research, 2007, v:35, n:Database-Issue, pp:314-316 [Journal]
  30. Y.-h. Taguchi, M. Michael Gromiha
    Protein Fold Recognition Based Upon the Amino Acid Occurrence. [Citation Graph (0, 0)][DBLP]
    PRIB, 2007, pp:120-131 [Conf]
  31. M. Michael Gromiha
    Bioinformatics on beta-Barrel Membrane Proteins: Sequence and Structural Analysis, Discrimination and Prediction. [Citation Graph (0, 0)][DBLP]
    PRIB, 2007, pp:148-157 [Conf]
  32. Liang-Tsung Huang, M. Michael Gromiha, Shinn-Ying Ho
    iPTREE-STAB: interpretable decision tree based method for predicting protein stability changes upon mutations. [Citation Graph (0, 0)][DBLP]
    Bioinformatics, 2007, v:23, n:10, pp:1292-1293 [Journal]

  33. Identification and Analysis of Binding Site Residues in Protein Complexes: Energy Based Approach. [Citation Graph (, )][DBLP]


  34. Topology Prediction of alpha-Helical and beta-Barrel Transmembrane Proteins Using RBF Networks. [Citation Graph (, )][DBLP]


  35. Neural network based prediction of protein structure and Function: Comparison with other machine learning methods. [Citation Graph (, )][DBLP]


  36. Gene Ontology term prediction based upon amino acid occurrence. [Citation Graph (, )][DBLP]


  37. Sequence Based Prediction of Protein Mutant Stability and Discrimination of Thermophilic Proteins. [Citation Graph (, )][DBLP]


  38. Reliable prediction of protein thermostability change upon double mutation from amino acid sequence. [Citation Graph (, )][DBLP]


  39. Application of amino acid occurrence for discriminating different folding types of globular proteins. [Citation Graph (, )][DBLP]


  40. Functional discrimination of membrane proteins using machine learning techniques. [Citation Graph (, )][DBLP]


  41. TMBETADISC-RBF: Discrimination of beta-barrel membrane proteins using RBF networks and PSSM profiles. [Citation Graph (, )][DBLP]


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