AWKUM Facultly/Staff Portal

PROFILE

DR. MAQSOOD HAYAT

Professor

Computer Science , Garden Campus

Contact Information:

Academics

Year	Qualification	Institute
2012	Ph.D	Pakistan Institute of Engineering & Applied Sciences, Islamabad
2009	MS / M.Phil	Muhammad Ali Jinnah University Islamabad
2004	BS Hons / Master	Gomal University D I Khan
2002	B.A / B.Sc	University of Peshawar
2000	HSSC	BISE Peshawar
1997	SSC	BISE Peshawar

Academic Experience

From	To	Post Held	Organization
2023-02-08		Professor	Abdul Wali Khan University Mardan
2018-08-15	2023-02-07	Associate Professor	Abdul Wali Khan University Mardan
2012-08-15	2018-08-14	Assistant Professor	Abdul Wali Khan University Mardan

Publications (Total No. of Publications 49)

Title	Journal	DOI
kDeepBind: Prediction of RNA-Proteins binding sites using convolution neural network and k-gram features	Chemometrics and Intelligent Laboratory Systems	https://doi.org/10.1016/j.chemolab.2020.104217
Deep-AntiFP: Prediction of antifungal peptides using distanct multi-informative features incorporating with deep neural networks	Chemometrics and Intelligent Laboratory Systems	https://doi.org/10.1016/j.chemolab.2020.104214
A deep learning-based computational approach for discrimination of DNA N6-methyladenosine sites by fusing heterogeneous features	Chemometrics and Intelligent Laboratory Systems	https://doi.org/10.1016/j.chemolab.2020.104151
Early and accurate detection and diagnosis of heart disease using intelligent computational model	Scientific Reports	https://doi.org/10.1038/s41598-020-76635-9
iHBP-DeepPSSM: Identifying hormone binding proteins using PsePSSM based evolutionary features and deep learning approach	Chemometrics and Intelligent Laboratory Systems	https://doi.org/10.1016/j.chemolab.2020.104103
cACP-2LFS: Classification of Anticancer Peptides Using Sequential Discriminative Model of KSAAP and Two-Level Feature Selection Approach	IEEE Access	10.1109/ACCESS.2020.3009125
Prediction of N6-methyladenosine sites using convolution neural network model based on distributed feature representations	Neural Networks	https://doi.org/10.1016/j.neunet.2020.05.027
ML-RBF: Predict protein subcellular locations in a multi-label system using evolutionary features	Chemometrics and Intelligent Laboratory Systems	https://doi.org/10.1016/j.chemolab.2020.104055
An intelligent computational model for prediction of promoters and their strength via natural language processing	Chemometrics and Intelligent Laboratory Systems	https://doi.org/10.1016/j.chemolab.2020.104034
iRNA-PseTNC: identification of RNA 5-methylcytosine sites using hybrid vector space of pseudo nucleotide composition	Frontiers of Computer Science	https://doi.org/10.1007/s11704-018-8094-9
Pred-BVP-Unb: Fast prediction of bacteriophage Virion proteins using un-biased multi-perspective properties with recursive feature elimination	Genomics	https://doi.org/10.1016/j.ygeno.2019.09.006
cACP: Classifying anticancer peptides using discriminative intelligent model via Chou�s 5-step rules and general pseudo components	Chemometric and Intelligent Laboratory Systems	https://doi.org/10.1016/j.chemolab.2019.103912
iNR-2L: A two-level sequence-based predictor developed via Chou's 5-steps rule and general PseAAC for identifying nuclear receptors and their families	Genomics	https://doi.org/10.1016/j.ygeno.2019.02.006
iPredCNC: Computational prediction model for cancerlectins and non-cancerlectins using novel cascade features subset selection	Chemometrics and Intelligent Laboratory Systems	https://doi.org/10.1016/j.chemolab.2019.103876
MFSC: Multi-voting based feature selection for classification of Golgi proteins by adopting the general form of Chou's PseAAC components	Journal of Theoretical Biology	https://doi.org/10.1016/j.jtbi.2018.12.017
iAFP-gap-SMOTE: an efficient feature extraction scheme gapped dipeptide composition is coupled with an oversampling technique for identification of antifreeze proteins	Letters in Organic Chemistry	https://doi.org/10.2174/1570178615666180816101653
Predicting subcellular localization of multi-label proteins by incorporating the sequence features into Chou's PseAAC	Genomics	https://doi.org/10.1016/j.ygeno.2018.09.004
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	Journal of Theoretical Biology	https://doi.org/10.1016/j.jtbi.2018.01.008
iNuc-ext-PseTNC: an efficient ensemble model for identification of nucleosome positioning by extending the concept of Chou�s PseAAC to pseudo-tri-nucleotide composition	Molecular Genetics and Genomics	https://doi.org/10.1007/s00438-018-1498-2
Efficient computational model for classification of protein localization images using Extended Threshold Adjacency Statistics and Support Vector Machines	Computer methods and programs in biomedicine	https://doi.org/10.1016/j.cmpb.2018.01.021
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	Journal of Theoretical Biology	https://doi.org/10.1016/j.jtbi.2018.07.018
Unb-DPC: Identify mycobacterial membrane protein types by incorporating un-biased dipeptide composition into Chou's general PseAAC	Journal of Theoretical Biology	https://doi.org/10.1016/j.jtbi.2016.12.004
Bi-PSSM: Position specific scoring matrix based intelligent computational model for identification of mycobacterial membrane proteins	Journal of Theoretical Biology	https://doi.org/10.1016/j.jtbi.2017.09.013
Sequence based predictor for discrimination of enhancer and their types by applying general form of Chou�s trinucleotide composition	Computer methods and programs in biomedicine	https://doi.org/10.1016/j.cmpb.2017.05.008
A Two-Layer Computational Model for Discrimination of Enhancer and Their Types Using Hybrid Features Pace of Pseudo K-Tuple Nucleotide Composition	Arabian Journal for Science and Engineering	10.1007/s13369-017-2818-2
Machine learning based identification of protein�protein interactions using derived features of physiochemical properties and evolutionary profiles	Artificial Intelligence in Medicine	https://doi.org/10.1016/j.artmed.2017.06.006
iACP-GAEnsC: Evolutionary genetic algorithm based ensemble classification of anticancer peptides by utilizing hybrid feature space	Artificial Intelligence in Medicine	https://doi.org/10.1016/j.artmed.2017.06.008
Intelligent computational model for classification of sub-Golgi protein using oversampling and fisher feature selection methods	Artificial Intelligence in Medicine	https://doi.org/10.1016/j.artmed.2017.05.001
Machine learning approaches for discrimination of Extracellular Matrix proteins using hybrid feature space	Journal of Theoretical Biology	https://doi.org/10.1016/j.jtbi.2016.05.011
Identification of DNA binding proteins using evolutionary profiles position specific scoring matrix	Neurocomputing	https://doi.org/10.1016/j.neucom.2016.03.025
iNuc-STNC: a sequence-based predictor for identification of nucleosome positioning in genomes by extending the concept of SAAC and Chou�s PseAAC	Molecular BioSystems	10.1039/c6mb00221h
iRSpot-GAEnsC: identifing recombination spots via ensemble classifier and extending the concept of Chou�s PseAAC to formulate DNA samples	Molecular Genetics and Genomics	10.1007/s00438-015-1108-5
Prediction of Protein Submitochondrial Locations by Incorporating Dipeptide Composition into Chou�s General Pseudo Amino Acid Composition	The Journal of Membrane Biology	10.1007/s00232-015-9868-8
�iSS-Hyb-mRMR�: Identification of splicing sites using hybrid space of pseudo trinucleotide and pseudo tetranucleotide composition	Computer methods and programs in biomedicine	https://doi.org/10.1016/j.cmpb.2016.02.006
Classification of membrane protein types using Voting Feature Interval in combination with Chou's Pseudo Amino Acid Composition	Journal of Theoretical Biology	https://doi.org/10.1016/j.jtbi.2015.07.034
PSOFuzzySVM-TMH: identification of transmembrane helix segments using ensemble feature space by incorporated fuzzy support vector machine	Journal of Molecular Biosystem	10.1039/c5mb00196j
Identification of Heat Shock Protein families and J-protein types by incorporating Dipeptide Composition into Chou�s general PseAAC	Computer methods and programs in biomedicine	https://doi.org/10.1016/j.cmpb.2016.02.006
iTIS-PseKNC: Identification of Translation Initiation Site in human genes using pseudo k-tuple nucleotides composition	Computers in Biology and Medicine	https://doi.org/10.1016/j.compbiomed.2015.09.010
Discrimination of acidic and alkaline enzyme using Chou�s pseudo amino acid composition in conjunction with probabilistic neural network model	Journal of Theoretical Biology	https://doi.org/10.1016/j.jtbi.2014.10.014
Prediction of protein structure classes using hybrid space of multi-profile Bayes and bi-gram probability feature spaces	Journal of Theoretical Biology	https://doi.org/10.1016/j.jtbi.2013.12.015
Discriminating protein structure classes by incorporating Pseudo Average Chemical Shift to Chou�s general PseAAC and Support Vector Machine	Computer methods and programs in biomedicine	https://doi.org/10.1016/j.cmpb.2014.06.007
WRF-TMH: predicting transmembrane helix by fusing composition index and physicochemical properties of amino acids	Amino Acids	10.1007/s00726-013-1466-4
Discriminating Outer Membrane Proteins with Fuzzy K-Nearest Neighbor Algorithms Based on the General Form of Chou�s PseAAC	Protein & Peptide Letters	https://doi.org/10.2174/092986612799789387
Mem-PHybrid: Hybrid features-based prediction system for classifying membrane protein types	Analytical Biochemistry	10.1016/j.ab.2012.02.007
CE-PLoc: An ensemble classifier for predicting protein subcellular locations by fusing different modes of pseudo amino acid composition	Computational Biology and Chemistry	https://doi.org/10.1016/j.compbiolchem.2011.05.003
Prediction of membrane protein types by using dipeptide and pseudo amino acid composition-based composite features	IET Communications	10.1049/iet-com.2011.0170
Prediction of membrane proteins using split amino acid and ensemble classification	Amino Acids	10.1007/s00726-011-1053-5
MemHyb: Predicting membrane protein types by hybridizing SAAC and PSSM	Journal of Theoretical Biology	10.1016/j.jtbi.2011.09.026
Predicting membrane protein types by fusing composite protein sequence features into pseudo amino acid composition	Journal of Theoretical Biology	10.1016/j.jtbi.2010.11.017

Conferences

Year	Level	Country	City	Conference
2010	National	Pakistan	Islamabad	6th IEEE International Conference on Emerging Technologies, (ICET 2010)
2014	International	Turky	Rize	2nd International Conference on Computational and Social Sciences (ICCSS-14)

Awards

Year	Awarding Organization	Description
2017	Pakistan Council for Science and Technology	Research Productivity Award for the year 2017 "Category -A"
2016	Pakistan Council for Science and Technology	Research Productivity Award for the year 2016 "Category -D"
2015	Abdul Wali Khan University Mardan	Best Researcher Award
2014	Higher Education Commission	Best Paper HEC outstanding Award for the year 2013
2014	Pakistan Council for Science and Technology	Research Productivity Award for the year 2013"Category -C"
2013	Pakistan Council for Science and Technology	Research Productivity Award for the year 2012 "Category -G"
2011	IEEE	6th IEEE International Conference on Emerging Technology (ICET, 2010) Fast, Islamabad
2011	Higher Education Commission	Six months IRSIP scholarship for University of Illinois, Urbana Champaign, Illinois, USA
2007	Higher Education Commission	HEC indigenous PhD scholarship batch-II

Memberships

From	To	Title	Organization

Courses Taught

Course Code	Type	Title	University	Semester

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Fundings

From	To	Type	Title	Source	Amount

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