| Predicting membrane protein types by fusing composite protein sequence features into pseudo amino acid composition M Hayat, A Khan Journal of theoretical biology 271 (1), 10-17, 2011 | 177 | 2011 |
| Discriminating outer membrane proteins with fuzzy K-nearest neighbor algorithms based on the general form of Chou's PseAAC M Hayat, A Khan Protein and peptide letters 19 (4), 411-421, 2012 | 173 | 2012 |
| Classification of membrane protein types using Voting Feature Interval in combination with Chou׳ s Pseudo Amino Acid Composition F Ali, M Hayat Journal of theoretical biology 384, 78-83, 2015 | 160 | 2015 |
| Discrimination of acidic and alkaline enzyme using Chou’s pseudo amino acid composition in conjunction with probabilistic neural network model ZU Khan, M Hayat, MA Khan Journal of theoretical biology 365, 197-203, 2015 | 160 | 2015 |
| Early and accurate detection and diagnosis of heart disease using intelligent computational model Y Muhammad, M Tahir, M Hayat, KT Chong Scientific reports 10 (1), 19747, 2020 | 147 | 2020 |
| iRSpot‑GAEnsC: identifing recombination spots via ensemble classifier and extending the concept of Chou’s PseAAC to formulate DNA samples M Kabir, M Hayat Mol Genet Genomics, 2015 | 142 | 2015 |
| iACP-GAEnsC: Evolutionary genetic algorithm based ensemble classification of anticancer peptides by utilizing hybrid feature space S Akbar, M Hayat, M Iqbal, MA Jan Artificial intelligence in medicine 79, 62-70, 2017 | 140 | 2017 |
| iMethyl-STTNC: Identification of N6-methyladenosine sites by extending the idea of SAAC into Chou's PseAAC to formulate RNA sequences S Akbar, M Hayat Journal of theoretical biology 455, 205-211, 2018 | 135 | 2018 |
| iMem-2LSAAC: a two-level model for discrimination of membrane proteins and their types by extending the notion of SAAC into Chou's pseudo amino acid composition M Arif, M Hayat, Z Jan Journal of Theoretical Biology 442, 11-21, 2018 | 114 | 2018 |
| Identification of Heat Shock Protein families and J-protein types by incorporating Dipeptide Composition into Chou's general PseAAC S Ahmad, M Kabir, M Hayat Computer methods and programs in biomedicine 122 (2), 165-174, 2015 | 112 | 2015 |
| iNuc-STNC: a sequence-based predictor for identification of nucleosome positioning in genomes by extending the concept of SAAC and Chou's PseAAC M Tahir, M Hayat Molecular BioSystems 12 (8), 2587-2593, 2016 | 110 | 2016 |
| Unb-DPC: Identify mycobacterial membrane protein types by incorporating un-biased dipeptide composition into Chou's general PseAAC M Khan, M Hayat, SA Khan, N Iqbal Journal of theoretical biology 415, 13-19, 2017 | 106 | 2017 |
| Prediction of protein submitochondrial locations by incorporating dipeptide composition into Chou’s general pseudo amino acid composition K Ahmad, M Waris, M Hayat The Journal of membrane biology 249, 293-304, 2016 | 101 | 2016 |
| MemHyb: predicting membrane protein types by hybridizing SAAC and PSSM M Hayat, A Khan Journal of theoretical biology 292, 93-102, 2012 | 97 | 2012 |
| Prediction of membrane proteins using split amino acid and ensemble classification M Hayat, A Khan, M Yeasin Amino acids 42, 2447-2460, 2012 | 93 | 2012 |
| iHBP-DeepPSSM: Identifying hormone binding proteins using PsePSSM based evolutionary features and deep learning approach S Akbar, S Khan, F Ali, M Hayat, M Qasim, S Gul Chemometrics and Intelligent Laboratory Systems 204, 104103, 2020 | 84 | 2020 |
| Discriminating protein structure classes by incorporating pseudo average chemical shift to Chou's general PseAAC and support vector machine M Hayat, N Iqbal Computer methods and programs in biomedicine 116 (3), 184-192, 2014 | 80 | 2014 |
| Predicting subcellular localization of multi-label proteins by incorporating the sequence features into Chou's PseAAC F Javed, M Hayat Genomics 111 (6), 1325-1332, 2019 | 79 | 2019 |
| cACP-DeepGram: Classification of anticancer peptides via deep neural network and skip-gram-based word embedding model FKA S. Akbar, M. Hayat, M. Tahir, S. Khan Artificial Intelligence In Medicine 131, 102349, 2022 | 78 | 2022 |
| Deep-AntiFP: Prediction of antifungal peptides using distanct multi-informative features incorporating with deep neural networks A Ahmad, S Akbar, S Khan, M Hayat, F Ali, A Ahmed, M Tahir Chemometrics and Intelligent Laboratory Systems 208, 104214, 2021 | 78 | 2021 |
