Dr. Krishnakumar Kandaswamy

Photo of Dr. Krishnakumar  Kandaswamy

Postdoc

Max-Planck-Institut für Biologie des Alterns
Joseph-Stelzmann-Straße 9b
50931 Köln

Email:
Phone:
+49 221 37970-0
Fax:
+49 221 37970-800

Publikationen

2013

  • Kandaswamy, K. K., Pugalenthi, G., Kalies, K., Hartmann, E., and Martinetz, T.: EcmPred: Prediction of extracellular matrix proteins based on random forest with maximum relevance minimum redundancy feature selection: Journal of Theoretical Biology, vol. 317, pp. 377-383, 2013
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2011

  • Pugalenthi, G., Kandaswamy, K. K., and Kolatkar, P.: RSARF: Prediction of residue solvent accessibility from protein sequence using random forest method: Protein & Peptide Letters, vol. 18, 2011, (to appear)
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  • Kandaswamy, K. K., Chou, K., Martinetz, T., Möller, S., Suganthan, P. N., Sridharan, S., and Pugalenthi, G.: AFP-Pred: A random forest approach for predicting antifreeze proteins from sequence-derived properties: Journal of Theoretical Biology, vol. 270, no. 1, pp. 56-62, 2011
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  • Shameer, K., Pugalenthi, G., Kandaswamy, K. K., and Sowdhamini, R.: 3dswap-pred: Prediction of 3D Domain Swapping from Protein Sequence Using Random Forest Approach: Protein & Peptide Letters, vol. 18, 2011
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  • Kandaswamy, K. K., Pugalenthi, G., Hazrati, M. K., Kalies, K., and Martinetz, T.: BLProt: Prediction of bioluminescent proteins based on Support Vector Machine and Relief feature selection: BMC Bioinformatics, 2011
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2010

  • Pugalenthi, G., Kumar, K. K., Suganthan, P., .Archunan, ., and .Sowdhamini, .: Identification of functionally diverse lipocalin proteins from sequence information using support vector machine: Amino Acids, vol. 39, pp. 777-783, 2010
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  • Kandaswamy, K. K., Pugalenthi, G., Hartmann, E., Kalies, K., Möller, S., Suganthan, P., and Martinetz, T.: SPRED: A machine learning approach for the identification of classical and non-classical secretory proteins in mammalian genomes: Biochemical and biophysical research communications, vol. 391, no. 3, pp. 1306-1311, 2010
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  • Kandaswamy, K. K., Pugalenthi, G., Möller, S., Hartmann, E., Kalies, K., .N.Suganthan, ., and Martinetz, T.: Prediction of apoptosis protein locations with Genetic Algorithms and Support Vector Machines through a new mode of pseudo amino acid composition: Protein Peptide Letters, vol. 17, pp. 1473-1479, 2010
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  • Shameer, K., Pugalenthi, G., Kandaswamy, K. K., Suganthan, P. N., Archunan, G., and Sowdhamini, R.: Insights in to protein sequence and structure derived features mediating 3D domain swapping mechanism using Support Vector Machine based approach: Bioinformatics and Biology Insights, vol. 4, pp. 33-42, 2010
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  • Kandaswamy, K. K., Pugalenthi, G., Suganthan, P., and Gangal, R.: SVMCRYS: An SVM approach for the prediction of protein crystallization propensity from protein: Protein Peptide Letters, vol. 26, pp. 423-430, 2010
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  • Pugalenthi, G., Kandaswamy, K. K., Suganthan, P. N., .Sowdhamini, ., Martinetz, T., and Kolatkar, P.: SMpred: A Support Vector Machine Approach to Identify Structural Motifs in Protein Structure Without Using Evolutionary Information: Journal of Biomolecular Structure and Dynamics, vol. 28, no. 3, 2010
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2008

  • Kumar, K. K. and Shelokar, P. S.: An SVM method using evolutionary information for the identification of allergenic proteins: Bioinformation, vol. 2, pp. 253-256, 2008
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