Startseite
Kandaswamy
erstellt von Dirk Laggin
—
zuletzt verändert:
11.03.2010 16:05
Publikationen
Krishnakumar Kandaswamy
| [1] | Krishna Kumar Kandaswamy, Ganesan Pugalenthi, Mehrnaz Khodam Hazrati, Kai-Uwe Kalies, and Thomas Martinetz. BLProt: Prediction of bioluminescent proteins based on Support Vector Machine and Relief feature selection. BMC Bioinformatics, 2011. [ bib | .pdf ] |
| [2] | Ganesan Pugalenthi, Krishna Kumar Kandaswamy, and Prasanna Kolatkar. RSARF: Prediction of residue solvent accessibility from protein sequence using random forest method. Protein & Peptide Letters, 18, 2011. (to appear). [ bib ] |
| [3] | Khader Shameer, Ganesan Pugalenthi, Krishna Kumar Kandaswamy, and Ramanathan Sowdhamini. 3dswap-pred: Prediction of 3D Domain Swapping from Protein Sequence Using Random Forest Approach. Protein & Peptide Letters, 18, 2011. [ bib ] |
| [4] | Krishna Kumar Kandaswamy, Kuo-Chen Chou, Thomas Martinetz, Steffen Möller, Ponnuthurai Nagaratnam Suganthan, S. Sridharan, and Pugalenthi Ganesan. AFP-Pred: A random forest approach for predicting antifreeze proteins from sequence-derived properties. Journal of Theoretical Biology, 270:56-62, 2011. [ bib ] |
| [5] | Krishna Kumar Kandaswamy, Ganesan Pugalenthi, Steffen Möller, Enno Hartmann, Kai-Uwe Kalies, P.N.Suganthan, and Thomas Martinetz. Prediction of apoptosis protein locations with Genetic Algorithms and Support Vector Machines through a new mode of pseudo amino acid composition. Protein Peptide Letters, 17:1473-1479, 2010. [ bib ] |
| [6] | Ganesan Pugalenthi, Krishna Kumar Kandaswamy, P. N. Suganthan, R.Sowdhamini, Thomas Martinetz, and Prasanna Kolatkar. A support vector machine approach to identify structural motifs in protein structure without using evolutionary information. Journal of Biomolecular Structure and Dynamics, 28, 2010. [ bib | .pdf ] |
| [7] | Khader Shameer, Ganesan Pugalenthi, Krishna Kumar Kandaswamy, Ponnuthurai N. Suganthan, Govindaraju Archunan, and Ramanathan Sowdhamini. Insights in to protein sequence and structure derived features mediating 3D domain swapping mechanism using Support Vector Machine based approach. Bioinformatics and Biology Insights, 4:33-42, 2010. [ bib ] |
| [8] | Ganesan Pugalenthi, Kandaswamy Krishna Kumar, P.N. Suganthan, G.Archunan, and R.Sowdhamini. Identification of functionally diverse lipocalin proteins from sequence information using support vector machine. Amino Acids, 39:777-783, 2010. [ bib ] |
| [9] | Krishna Kumar Kandaswamy, Ganesan Pugalenthi, P.N Suganthan, and Rajeev Gangal. SVMCRYS: An SVM approach for the prediction of protein crystallization propensity from protein. Protein Peptide Letters, 26:423-430, 2010. [ bib ] |
| [10] | Krishna Kumar Kandaswamy, Ganesan Pugalenthi, Enno Hartmann, Kai-Uwe Kalies, Steffen Möller, P.N. Suganthan, and Thomas Martinetz. SPRED: A machine learning approach for the identification of classical and non-classical secretory proteins in mammalian genomes. Biochemical and Biophysical Research Communications, 391:1306-1311, 2010. [ bib | .pdf ] |
| [11] | K. Krishna Kumar and ndP.N. Suganthan Ganesan Pugalenthi. Identification of DNA binding proteins from protein sequence information using random forest method. Journal of Biomolecular Structure and Dynamics, 26:663-895, 2009. [ bib ] |
| [12] | Ganesan Pugalenthi, K. Krishna Kumar, P.N. Suganthan, and Rajeev Gangal. Identification of catalytic residues from protein structure using Support Vector Machine with sequence and structural features. Biochemical and Biophysical Research Communications, 367:630-634, 2008. [ bib ] |
| [13] | Kandaswamy Krishna Kumar and Prakash Shrikrishna Shelokar. An SVM method using evolutionary information for the identification of allergenic proteins. Bioinformation, 2:253-256, 2008. [ bib ] |
| [14] | Rajeev Gangal and K. Krishna Kumar. Reduced Alphabet Motif Methodology for GPCR annotation. Journal of Biomolecular Structure and Dynamics, 25:299-310, 2007. [ bib ] |
| [15] | Piyushkumar Mundra, Madhan Kumar, K. Krishna Kumar, Valadi K. Jayaraman, and Bhaskar D. Kulkarni. Using Pseudo Amino Acid Composition to Predict Protein Sub nuclear Localization: Approached with PSSM. Pattern Recognition Letters, 28:1610-1615, 2007. [ bib ] |
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