Следене
Dr. Maqsood Hayat Tenured Professor
Dr. Maqsood Hayat Tenured Professor
Потвърден имейл адрес: awkum.edu.pk
Заглавие
Позовавания
Позовавания
Година
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
1762011
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
1732012
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
1582015
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
1572015
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
1412015
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
1362020
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
1352017
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
1322018
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
1112015
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
1102018
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
1092016
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
1062017
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
992016
MemHyb: predicting membrane protein types by hybridizing SAAC and PSSM
M Hayat, A Khan
Journal of theoretical biology 292, 93-102, 2012
962012
Prediction of membrane proteins using split amino acid and ensemble classification
M Hayat, A Khan, M Yeasin
Amino acids 42, 2447-2460, 2012
932012
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
802014
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
792019
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
762020
Identification of DNA binding proteins using evolutionary profiles position specific scoring matrix
M Waris, K Ahmad, M Kabir, M Hayat
Neurocomputing 199, 154-162, 2016
722016
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
712021
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