| Vehicle detection and counting in high-resolution aerial images using convolutional regression neural network H Tayara, KG Soo, KT Chong Ieee Access 6, 2220-2230, 2017 | 183 | 2017 |
| DeePromoter: robust promoter predictor using deep learning M Oubounyt, Z Louadi, H Tayara, KT Chong Frontiers in genetics 10, 286, 2019 | 149 | 2019 |
| Object detection in very high-resolution aerial images using one-stage densely connected feature pyramid network H Tayara, KT Chong Sensors 18 (10), 3341, 2018 | 116 | 2018 |
| iRNA-PseKNC (2methyl): Identify RNA 2'-O-methylation sites by convolution neural network and Chou's pseudo components M Tahir, H Tayara, KT Chong Journal of theoretical biology 465, 1-6, 2019 | 99 | 2019 |
| iDNA6mA (5-step rule): Identification of DNA N6-methyladenine sites in the rice genome by intelligent computational model via Chou's 5-step rule M Tahir, H Tayara, KT Chong Chemometrics and Intelligent Laboratory Systems 189, 96-101, 2019 | 92 | 2019 |
| iN6-Methyl (5-step): Identifying RNA N6-methyladenosine sites using deep learning mode via Chou's 5-step rules and Chou's general PseKNC I Nazari, M Tahir, H Tayara, KT Chong Chemometrics and Intelligent Laboratory Systems 193, 103811, 2019 | 88 | 2019 |
| PUResNet: prediction of protein-ligand binding sites using deep residual neural network J Kandel, H Tayara, KT Chong Journal of cheminformatics 13, 1-14, 2021 | 79 | 2021 |
| iPseU-CNN: identifying RNA pseudouridine sites using convolutional neural networks M Tahir, H Tayara, KT Chong Molecular Therapy-Nucleic Acids 16, 463-470, 2019 | 77 | 2019 |
| Branch point selection in RNA splicing using deep learning I Nazari, H Tayara, KT Chong Ieee Access 7, 1800-1807, 2018 | 63 | 2018 |
| A CNN-based RNA N6-methyladenosine site predictor for multiple species using heterogeneous features representation W Alam, SD Ali, H Tayara, K to Chong IEEE Access 8, 138203-138209, 2020 | 56 | 2020 |
| 4mCCNN: Identification of N4-methylcytosine sites in prokaryotes using convolutional neural network J Khanal, I Nazari, H Tayara, KT Chong Ieee Access 7, 145455-145461, 2019 | 56 | 2019 |
| Artificial intelligence in drug toxicity prediction: recent advances, challenges, and future perspectives TTV Tran, A Surya Wibowo, H Tayara, KT Chong Journal of chemical information and modeling 63 (9), 2628-2643, 2023 | 52 | 2023 |
| Identification of prokaryotic promoters and their strength by integrating heterogeneous features H Tayara, M Tahir, KT Chong Genomics 112 (2), 1396-1403, 2020 | 51 | 2020 |
| Identifying enhancers and their strength by the integration of word embedding and convolution neural network J Khanal, H Tayara, KT Chong Ieee Access 8, 58369-58376, 2020 | 47 | 2020 |
| XG-ac4C: identification of N4-acetylcytidine (ac4C) in mRNA using eXtreme gradient boosting with electron-ion interaction pseudopotentials W Alam, H Tayara, KT Chong Scientific reports 10 (1), 20942, 2020 | 40 | 2020 |
| Spinenet-6ma: A novel deep learning tool for predicting dna n6-methyladenine sites in genomes Z Abbas, H Tayara, K to Chong IEEE Access 8, 201450-201457, 2020 | 40 | 2020 |
| Deep splicing code: Classifying alternative splicing events using deep learning Z Louadi, M Oubounyt, H Tayara, KT Chong Genes 10 (8), 587, 2019 | 40 | 2019 |
| Deep learning models based on distributed feature representations for alternative splicing prediction M Oubounyt, Z Louadi, H Tayara, KT Chong IEEE Access 6, 58826-58834, 2018 | 39 | 2018 |
| DNA sequences performs as natural language processing by exploiting deep learning algorithm for the identification of N4-methylcytosine A Wahab, H Tayara, Z Xuan, KT Chong Scientific reports 11 (1), 212, 2021 | 37 | 2021 |
| pcPromoter-CNN: a CNN-based prediction and classification of promoters M Shujaat, A Wahab, H Tayara, KT Chong Genes 11 (12), 1529, 2020 | 37 | 2020 |
Kil To ChongJeonbuk National University在 jbnu.ac.kr 的电子邮件经过验证
