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Computer Science

D-Index
38
Citations
4380
World Ranking
10413
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Overview

Maqsood Hayat is affiliated with Abdul Wali Khan University Mardan in Pakistan and has an extensive publication record primarily in the field of Biochemistry, Genetics and Molecular Biology, with a focus on Molecular Biology. Their research spans key areas including Machine Learning in Bioinformatics, RNA and protein synthesis mechanisms, vaccines and immunoinformatics approaches, Computational Drug Discovery Methods, Genomics and Phylogenetic Studies, Antimicrobial Peptides and Activities, and Biochemical and Structural Characterization.

They have contributed to a substantial body of work, including recent papers such as:

  • Early and accurate detection and diagnosis of heart disease using intelligent computational model (2020, Scientific Reports)
  • cACP-DeepGram: Classification of anticancer peptides via deep neural network and skip-gram-based word embedding model (2022, Artificial Intelligence in Medicine)
  • iHBP-DeepPSSM: Identifying hormone binding proteins using PsePSSM based evolutionary features and deep learning approach (2020, Chemometrics and Intelligent Laboratory Systems)
  • iAtbP-Hyb-EnC: Prediction of antitubercular peptides via heterogeneous feature representation and genetic algorithm based ensemble learning model (2021, Computers in Biology and Medicine)
  • Deep-AntiFP: Prediction of antifungal peptides using distinct multi-informative features incorporating with deep neural networks (2020, Chemometrics and Intelligent Laboratory Systems)

Maqsood Hayat has frequently published in venues such as Chemometrics and Intelligent Laboratory Systems, Scientific Reports, Computers in Biology and Medicine, IEEE Access, and Artificial Intelligence in Medicine.

They have collaborated notably with several coauthors including Muhammad Tahir, Kil To Chong, Shahid Akbar, Salman Khan, and Farman Ali.

Their work incorporates advanced computational and machine learning methodologies applied to bioinformatics, focusing on peptide classification, disease diagnosis, and drug discovery. The integration of deep learning techniques and evolutionary feature extraction is a recurring theme in their research efforts.

Best Publications

  • Early and accurate detection and diagnosis of heart disease using intelligent computational model.

    Yar Muhammad;Muhammad Tahir;Maqsood Hayat;Kil To Chong

  • Predicting membrane protein types by fusing composite protein sequence features into pseudo amino acid composition.

    Maqsood Hayat;Asifullah Khan

  • iACP-GAEnsC: Evolutionary genetic algorithm based ensemble classification of anticancer peptides by utilizing hybrid feature space.

    Shahid Akbar;Maqsood Hayat;Muhammad Iqbal;Mian Ahmad Jan

  • Discriminating outer membrane proteins with Fuzzy K-nearest Neighbor algorithms based on the general form of Chou's PseAAC.

    Maqsood Hayat;Asifullah Khan

  • Discrimination of acidic and alkaline enzyme using Chou's pseudo amino acid composition in conjunction with probabilistic neural network model.

    Zaheer Ullah Khan;Maqsood Hayat;Muazzam Ali Khan

  • Classification of membrane protein types using Voting Feature Interval in combination with Chou׳s Pseudo Amino Acid Composition

    Farman Ali;Maqsood Hayat

  • iRSpot-GAEnsC: identifing recombination spots via ensemble classifier and extending the concept of Chou's PseAAC to formulate DNA samples.

    Muhammad Kabir;Maqsood Hayat

  • iMethyl-STTNC: Identification of N6-methyladenosine sites by extending the idea of SAAC into Chou's PseAAC to formulate RNA sequences.

    Shahid Akbar;Maqsood Hayat

  • 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.

    Muhammad Arif;Maqsood Hayat;Zahoor Jan

  • iNuc-STNC: a sequence-based predictor for identification of nucleosome positioning in genomes by extending the concept of SAAC and Chou's PseAAC

    Muhammad Tahir;Maqsood Hayat

  • Unb-DPC: Identify mycobacterial membrane protein types by incorporating un-biased dipeptide composition into Chou's general PseAAC.

    Muslim Khan;Maqsood Hayat;Sher Afzal Khan;Nadeem Iqbal

  • Identification of Heat Shock Protein families and J-protein types by incorporating Dipeptide Composition into Chou's general PseAAC

    Saeed Ahmad;Muhammad Kabir;Maqsood Hayat

  • Prediction of Protein Submitochondrial Locations by Incorporating Dipeptide Composition into Chou’s General Pseudo Amino Acid Composition

    Khurshid Ahmad;Muhammad Waris;Maqsood Hayat

  • MemHyb: predicting membrane protein types by hybridizing SAAC and PSSM.

    Maqsood Hayat;Asifullah Khan

  • iHBP-DeepPSSM: Identifying hormone binding proteins using PsePSSM based evolutionary features and deep learning approach

    Shahid Akbar;Salman Khan;Farman Ali;Maqsood Hayat

  • iAtbP-Hyb-EnC: Prediction of antitubercular peptides via heterogeneous feature representation and genetic algorithm based ensemble learning model

    Shahid Akbar;Ashfaq Ahmad;Maqsood Hayat;Ateeq Ur Rehman

  • Deep-AntiFP: Prediction of antifungal peptides using distanct multi-informative features incorporating with deep neural networks

    Ashfaq Ahmad;Shahid Akbar;Salman Khan;Maqsood Hayat

  • Prediction of membrane proteins using split amino acid and ensemble classification.

    Maqsood Hayat;Asifullah Khan;Mohammed Yeasin

  • Discriminating protein structure classes by incorporating Pseudo Average Chemical Shift to Chou's general PseAAC and Support Vector Machine

    Maqsood Hayat;Nadeem Iqbal

  • Predicting subcellular localization of multi-label proteins by incorporating the sequence features into Chou's PseAAC

    Faisal Javed;Maqsood Hayat

  • Identification of DNA binding proteins using evolutionary profiles position specific scoring matrix

    Muhammad Waris;Khurshid Ahmad;Muhammad Kabir;Maqsood Hayat

Frequent Co-Authors

Asifullah Khan
Asifullah Khan Pakistan Institute of Engineering and Applied Sciences
Kil To Chong
Kil To Chong Jeonbuk National University

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