| Unsupervised feature evaluation: A neuro-fuzzy approach SK Pal, RK De, J Basak IEEE Transactions on neural networks 11 (2), 366-376, 2000 | 178 | 2000 |
| Immunoinformatics: an integrated scenario N Tomar, RK De Immunology 131 (2), 153-168, 2010 | 146 | 2010 |
| Feature analysis: Neural network and fuzzy set theoretic approaches K De Rajat, NR Pal, SK Pal Pattern Recognition 30 (10), 1579-1590, 1997 | 135 | 1997 |
| Unsupervised feature selection using a neuro-fuzzy approach J Basak, RK De, SK Pal Pattern Recognition Letters 19 (11), 997-1006, 1998 | 131 | 1998 |
| Knowledge-based fuzzy MLP for classification and rule generation S Mitra, RK De, SK Pal IEEE Transactions on Neural Networks 8 (6), 1338-1350, 1997 | 122 | 1997 |
| Obesity: an immunometabolic perspective I Ray, SK Mahata, RK De Frontiers in endocrinology 7, 157, 2016 | 104 | 2016 |
| Divisive Correlation Clustering Algorithm (DCCA) for grouping of genes: detecting varying patterns in expression profiles A Bhattacharya, RK De bioinformatics 24 (11), 1359-1366, 2008 | 82 | 2008 |
| Immunoinformatics: a brief review N Tomar, RK De Immunoinformatics, 23-55, 2014 | 70 | 2014 |
| Comparing methods for metabolic network analysis and an application to metabolic engineering N Tomar, RK De Gene 521 (1), 1-14, 2013 | 68 | 2013 |
| Segmentation of remotely sensed images using wavelet features and their evaluation in soft computing framework M Acharyya, RK De, MK Kundu IEEE Transactions on Geoscience and Remote Sensing 41 (12), 2900-2905, 2003 | 66 | 2003 |
| A brief outline of the immune system N Tomar, RK De Immunoinformatics, 3-12, 2014 | 65 | 2014 |
| A review on host–pathogen interactions: classification and prediction R Sen, L Nayak, RK De European Journal of Clinical Microbiology & Infectious Diseases 35, 1581-1599, 2016 | 63 | 2016 |
| Bi-correlation clustering algorithm for determining a set of co-regulated genes A Bhattacharya, RK De Bioinformatics 25 (21), 2795-2801, 2009 | 58 | 2009 |
| Extraction of features using M-band wavelet packet frame and their neuro-fuzzy evaluation for multitexture segmentation M Acharyya, RK De, MK Kundu IEEE Transactions on Pattern Analysis and Machine Intelligence 25 (12), 1639 …, 2003 | 52 | 2003 |
| Neuro-fuzzy feature evaluation with theoretical analysis RK De, J Basak, SK Pal Neural Networks 12 (10), 1429-1455, 1999 | 35 | 1999 |
| Estimating gene expression from DNA methylation and copy number variation: a deep learning regression model for multi-omics integration DB Seal, V Das, S Goswami, RK De Genomics 112 (4), 2833-2841, 2020 | 33 | 2020 |
| Wnt signal transduction pathways: modules, development and evolution L Nayak, NP Bhattacharyya, RK De BMC Systems Biology 10 (2), 197-213, 2016 | 26 | 2016 |
| Selection of genes mediating certain cancers, using a neuro-fuzzy approach A Ghosh, BC Dhara, RK De Neurocomputing 133, 122-140, 2014 | 26 | 2014 |
| Average correlation clustering algorithm (ACCA) for grouping of co-regulated genes with similar pattern of variation in their expression values A Bhattacharya, RK De Journal of Biomedical Informatics 43 (4), 560-568, 2010 | 24 | 2010 |
| Catestatin improves insulin sensitivity by attenuating endoplasmic reticulum stress: In vivo and in silico validation A Dasgupta, GK Bandyopadhyay, I Ray, K Bandyopadhyay, N Chowdhury, ... Computational and structural biotechnology journal 18, 464-481, 2020 | 22 | 2020 |
