Ganesan Pugalenthi

AFP-Pred: A random forest approach for predicting antifreeze proteins from sequence-derived properties

Krishna Kumar Kandaswamy, Kuo‐Chen Chou, Thomas Martinetz et al. · Journal of Theoretical Biology · 2010 · 240 citations

Sequence-derived Properties, Structural Bioinformatics, Natural Sciences +14

An approach for classification of highly imbalanced data using weighting and undersampling

Ashish Anand, Ganesan Pugalenthi, Gary B. Fogel et al. · Amino Acids · 2010 · 171 citations

Data Classification, Classification Method, Engineering +9

DNA-Prot: Identification of DNA Binding Proteins from Protein Sequence Information using Random Forest

K. Krishna Kumar, Ganesan Pugalenthi, Ponnuthurai Nagaratnam Suganthan · Journal of Biomolecular Structure and Dynamics · 2009 · 132 citations

Prediction of Apoptosis Protein Locations with Genetic Algorithms and Support Vector Machines Through a New Mode of Pseudo Amino Acid Composition

Krishna Kumar Kandaswamy, Ganesan Pugalenthi, Steffen Möller et al. · Protein and Peptide Letters · 2010 · 74 citations

Predicting protein structural class by SVM with class-wise optimized features and decision probabilities

Ashish Anand, Ganesan Pugalenthi, Ponnuthurai Nagaratnam Suganthan · Journal of Theoretical Biology · 2008 · 66 citations

Support Vector Machine, Engineering, Pattern Recognition +15