Swakkhar Shatabda
Cited by
Cited by
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
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
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
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
iDNAProt-ES: identification of DNA-binding proteins using evolutionary and structural features
SY Chowdhury, S Shatabda, A Dehzangi
Scientific reports 7 (1), 14938, 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
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
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
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
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
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
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
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
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
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
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
HMMBinder: DNA-binding protein prediction using HMM profile based features
R Zaman, SY Chowdhury, MA Rashid, A Sharma, A Dehzangi, ...
BioMed research international 2017, 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
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
Locate-R: subcellular localization of long non-coding RNAs using nucleotide compositions
A Ahmad, H Lin, S Shatabda
Genomics 112 (3), 2583-2589, 2020
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