DPP-PseAAC: a DNA-binding protein prediction model using Chou’s general PseAAC MS Rahman, S Shatabda, S Saha, M Kaykobad, MS Rahman Journal of theoretical biology 452, 22-34, 2018 | 147 | 2018 |
Cusboost: Cluster-based under-sampling with boosting for imbalanced classification F Rayhan, S Ahmed, A Mahbub, R Jani, S Shatabda, DM Farid 2017 2nd international conference on computational systems and information …, 2017 | 112 | 2017 |
iDTI-ESBoost: identification of drug target interaction using evolutionary and structural features with boosting F Rayhan, S Ahmed, S Shatabda, DM Farid, Z Mousavian, A Dehzangi, ... Scientific reports 7 (1), 17731, 2017 | 109 | 2017 |
PyFeat: a Python-based effective feature generation tool for DNA, RNA and protein sequences R Muhammod, S Ahmed, D Md Farid, S Shatabda, A Sharma, A Dehzangi Bioinformatics 35 (19), 3831-3833, 2019 | 104 | 2019 |
iDNAProt-ES: identification of DNA-binding proteins using evolutionary and structural features SY Chowdhury, S Shatabda, A Dehzangi Scientific reports 7 (1), 14938, 2017 | 99 | 2017 |
An approximation algorithm for sorting by reversals and transpositions A Rahman, S Shatabda, M Hasan Journal of Discrete Algorithms 6 (3), 449-457, 2008 | 78 | 2008 |
Towards development of IoT-ML driven healthcare systems: A survey NS Sworna, AKMM Islam, S Shatabda, S Islam Journal of Network and Computer Applications 196, 103244, 2021 | 61 | 2021 |
An ensemble 1D-CNN-LSTM-GRU model with data augmentation for speech emotion recognition MR Ahmed, S Islam, AKMM Islam, S Shatabda Expert Systems with Applications 218, 119633, 2023 | 60 | 2023 |
YOLO-Fish: A robust fish detection model to detect fish in realistic underwater environment A Al Muksit, F Hasan, MFHB Emon, MR Haque, AR Anwary, S Shatabda Ecological Informatics 72, 101847, 2022 | 59 | 2022 |
iRSpot-SF: Prediction of recombination hotspots by incorporating sequence based features into Chou's Pseudo components MA Al Maruf, S Shatabda Genomics 111 (4), 966-972, 2019 | 57 | 2019 |
FRnet-DTI: Deep convolutional neural network for drug-target interaction prediction F Rayhan, S Ahmed, Z Mousavian, DM Farid, S Shatabda Heliyon 6 (3), 2020 | 54 | 2020 |
ACP-MHCNN: An accurate multi-headed deep-convolutional neural network to predict anticancer peptides S Ahmed, R Muhammod, ZH Khan, S Adilina, A Sharma, S Shatabda, ... Scientific reports 11 (1), 23676, 2021 | 53 | 2021 |
Effective DNA binding protein prediction by using key features via Chou’s general PseAAC S Adilina, DM Farid, S Shatabda Journal of theoretical biology 460, 64-78, 2019 | 53 | 2019 |
Hybrid methods for class imbalance learning employing bagging with sampling techniques S Ahmed, A Mahbub, F Rayhan, R Jani, S Shatabda, DM Farid 2017 2nd International Conference on Computational Systems and Information …, 2017 | 52 | 2017 |
Improving detection accuracy for imbalanced network intrusion classification using cluster-based under-sampling with random forests MO Miah, SS Khan, S Shatabda, DM Farid 2019 1st international conference on advances in science, engineering and …, 2019 | 50 | 2019 |
iPromoter-BnCNN: a novel branched CNN-based predictor for identifying and classifying sigma promoters R Amin, CR Rahman, S Ahmed, MHR Sifat, MNK Liton, MM Rahman, ... Bioinformatics 36 (19), 4869-4875, 2020 | 49 | 2020 |
HMMBinder: DNA‐binding protein prediction using HMM profile based features R Zaman, SY Chowdhury, MA Rashid, A Sharma, A Dehzangi, ... BioMed research international 2017 (1), 4590609, 2017 | 47 | 2017 |
iPro70-FMWin: identifying Sigma70 promoters using multiple windowing and minimal features MS Rahman, U Aktar, MR Jani, S Shatabda Molecular Genetics and Genomics 294 (1), 69-84, 2019 | 42 | 2019 |
iPromoter-FSEn: Identification of bacterial σ70 promoter sequences using feature subspace based ensemble classifier MS Rahman, U Aktar, MR Jani, S Shatabda Genomics 111 (5), 1160-1166, 2019 | 41 | 2019 |
Locate-R: subcellular localization of long non-coding RNAs using nucleotide compositions A Ahmad, H Lin, S Shatabda Genomics 112 (3), 2583-2589, 2020 | 39 | 2020 |