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Krishna Kumar Kandaswamy

Showing results (1-10 of 22) with videos related to

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Protein and Peptide Letters|May 20, 2011
3dswap-pred: prediction of 3D domain swapping from protein sequence using Random Forest approachKhader Shameer, Ganesan Pugalenthi, Krishna Kumar Kandaswamy, et al.
Protein and Peptide Letters|January 5, 2010
SVMCRYS: an SVM approach for the prediction of protein crystallization propensity from protein sequenceKrishna Kumar Kandaswamy, Ganesan Pugalenthi, P N Suganthan, et al.
The Lancet. Neurology|September 20, 2024
RAB32 mutation in Parkinson's diseaseChristian Beetz, Mandy Radefeldt, Kornelia Tripolszki, et al.
Protein and Peptide Letters|September 17, 2011
RSARF: prediction of residue solvent accessibility from protein sequence using random forest methodGanesan Pugalenthi, Krishna Kumar Kandaswamy, Kuo-Chen Chou, et al.
Amino Acids|February 27, 2010
Identification of functionally diverse lipocalin proteins from sequence information using support vector machineGanesan Pugalenthi, Krishna Kumar Kandaswamy, P N Suganthan, et al.
Journal of Theoretical Biology|November 6, 2012
EcmPred: prediction of extracellular matrix proteins based on random forest with maximum relevance minimum redundancy feature selectionKrishna Kumar Kandaswamy, Ganesan Pugalenthi, Kai-Uwe Kalies, et al.
BMC Bioinformatics|August 19, 2011
BLProt: prediction of bioluminescent proteins based on support vector machine and relieff feature selectionKrishna Kumar Kandaswamy, Ganesan Pugalenthi, Mehrnaz Khodam Hazrati, et al.
Bioinformatics and Biology Insights|July 17, 2010
Insights into Protein Sequence and Structure-Derived Features Mediating 3D Domain Swapping Mechanism using Support Vector Machine Based ApproachKhader Shameer, Ganesan Pugalenthi, Krishna Kumar Kandaswamy, et al.
Journal of Biomolecular Structure & Dynamics|October 6, 2010
SMpred: a support vector machine approach to identify structural motifs in protein structure without using evolutionary informationGanesan Pugalenthi, Krishna Kumar Kandaswamy, P N Suganthan, et al.
Journal of Theoretical Biology|November 9, 2010
AFP-Pred: A random forest approach for predicting antifreeze proteins from sequence-derived propertiesKrishna Kumar Kandaswamy, Kuo-Chen Chou, Thomas Martinetz, et al.
Pageof 3

Showing results (1-10 of 22) with videos related to

Sort By:
Pageof 3
Protein and Peptide Letters|May 20, 2011
3dswap-pred: prediction of 3D domain swapping from protein sequence using Random Forest approachKhader Shameer, Ganesan Pugalenthi, Krishna Kumar Kandaswamy, et al.
Protein and Peptide Letters|January 5, 2010
SVMCRYS: an SVM approach for the prediction of protein crystallization propensity from protein sequenceKrishna Kumar Kandaswamy, Ganesan Pugalenthi, P N Suganthan, et al.
The Lancet. Neurology|September 20, 2024
RAB32 mutation in Parkinson's diseaseChristian Beetz, Mandy Radefeldt, Kornelia Tripolszki, et al.
Protein and Peptide Letters|September 17, 2011
RSARF: prediction of residue solvent accessibility from protein sequence using random forest methodGanesan Pugalenthi, Krishna Kumar Kandaswamy, Kuo-Chen Chou, et al.
Amino Acids|February 27, 2010
Identification of functionally diverse lipocalin proteins from sequence information using support vector machineGanesan Pugalenthi, Krishna Kumar Kandaswamy, P N Suganthan, et al.
Journal of Theoretical Biology|November 6, 2012
EcmPred: prediction of extracellular matrix proteins based on random forest with maximum relevance minimum redundancy feature selectionKrishna Kumar Kandaswamy, Ganesan Pugalenthi, Kai-Uwe Kalies, et al.
BMC Bioinformatics|August 19, 2011
BLProt: prediction of bioluminescent proteins based on support vector machine and relieff feature selectionKrishna Kumar Kandaswamy, Ganesan Pugalenthi, Mehrnaz Khodam Hazrati, et al.
Bioinformatics and Biology Insights|July 17, 2010
Insights into Protein Sequence and Structure-Derived Features Mediating 3D Domain Swapping Mechanism using Support Vector Machine Based ApproachKhader Shameer, Ganesan Pugalenthi, Krishna Kumar Kandaswamy, et al.
Journal of Biomolecular Structure & Dynamics|October 6, 2010
SMpred: a support vector machine approach to identify structural motifs in protein structure without using evolutionary informationGanesan Pugalenthi, Krishna Kumar Kandaswamy, P N Suganthan, et al.
Journal of Theoretical Biology|November 9, 2010
AFP-Pred: A random forest approach for predicting antifreeze proteins from sequence-derived propertiesKrishna Kumar Kandaswamy, Kuo-Chen Chou, Thomas Martinetz, et al.
Pageof 3