World's Best Scientists 2026 revealed!

D-Index & Metrics

Computer Science

D-Index
38
Citations
4380
World Ranking
10411
National Ranking
15

Maqsood Hayat publication distribution in Computer Science in 2026

The chart shows the distribution of publications by all Research.com ranked scientists in the field of Computer Science in 2026. The highlighted bar marks where Maqsood Hayat sits on this spectrum.

32–41 publications: 7 scientists 42–51 publications: 22 scientists 52–61 publications: 82 scientists 62–71 publications: 134 scientists 72–81 publications: 249 scientists 82–91 publications: 324 scientists 92–101 publications: 421 scientists 102–111 publications: 420 scientists 112–121 publications: 497 scientists 122–131 publications: 544 scientists 132–141 publications: 555 scientists 142–151 publications: 609 scientists 152–161 publications: 559 scientists 162–171 publications: 534 scientists 172–181 publications: 556 scientists 182–191 publications: 583 scientists 192–201 publications: 519 scientists 202–211 publications: 508 scientists 212–221 publications: 490 scientists 222–231 publications: 437 scientists 232–241 publications: 423 scientists 242–251 publications: 408 scientists 252–261 publications: 377 scientists 262–271 publications: 301 scientists 272–281 publications: 335 scientists 282–291 publications: 320 scientists 292–301 publications: 293 scientists 302–311 publications: 250 scientists 312–321 publications: 238 scientists 322–331 publications: 206 scientists 332–341 publications: 209 scientists 342–351 publications: 208 scientists 352–361 publications: 162 scientists 362–371 publications: 176 scientists 372–381 publications: 127 scientists 382–391 publications: 158 scientists 392–401 publications: 128 scientists 402–411 publications: 104 scientists 412–421 publications: 94 scientists 422–431 publications: 99 scientists 432–441 publications: 83 scientists 442–451 publications: 108 scientists 452–461 publications: 73 scientists 462–471 publications: 77 scientists 472–481 publications: 69 scientists 482–491 publications: 84 scientists 492–501 publications: 62 scientists 502–511 publications: 54 scientists 512–521 publications: 57 scientists 522–531 publications: 51 scientists 532–541 publications: 51 scientists 542–551 publications: 32 scientists 552–561 publications: 38 scientists 562–571 publications: 28 scientists 572–581 publications: 43 scientists 582–591 publications: 33 scientists 592–601 publications: 41 scientists 602–611 publications: 32 scientists 612–621 publications: 28 scientists 622–631 publications: 25 scientists 632–641 publications: 27 scientists 642–651 publications: 17 scientists 652–661 publications: 20 scientists 662–671 publications: 17 scientists 672–681 publications: 15 scientists 682–691 publications: 14 scientists 692–701 publications: 21 scientists 702–711 publications: 13 scientists 712–721 publications: 12 scientists 722–731 publications: 19 scientists 732–741 publications: 14 scientists 742–751 publications: 12 scientists 752–761 publications: 10 scientists 762–771 publications: 10 scientists 772–781 publications: 11 scientists 782–791 publications: 10 scientists 792–801 publications: 11 scientists 802–811 publications: 8 scientists 812–821 publications: 8 scientists 822–831 publications: 7 scientists 832–841 publications: 11 scientists 842–851 publications: 10 scientists 852–861 publications: 5 scientists 862–871 publications: 9 scientists 872–881 publications: 4 scientists 882–891 publications: 6 scientists 892–901 publications: 3 scientists 902–911 publications: 6 scientists 912–921 publications: 3 scientists 922–931 publications: 2 scientists 932–941 publications: 2 scientists 942–951 publications: 2 scientists 952–961 publications: 3 scientists 962–971 publications: 3 scientists 972–981 publications: 3 scientists 982–990 publications: 5 scientists 991+ publications: 100 scientists
32 publications 991+

This scientist: 78 publications — 3rd percentile

3% of scientists in this discipline score the same or lower.

The last bar groups every scientist with 991 publications or more.

Maqsood Hayat D-index placement in Computer Science in 2026

The chart shows the D-index (discipline H-index) distribution of Computer Science scientists ranked by Research.com in 2026. The highlighted bar marks where Maqsood Hayat sits on this spectrum.

30–31 D-Index: 879 scientists 32–33 D-Index: 983 scientists 34–35 D-Index: 918 scientists 36–37 D-Index: 990 scientists 38–39 D-Index: 968 scientists 40–41 D-Index: 907 scientists 42–43 D-Index: 821 scientists 44–45 D-Index: 763 scientists 46–47 D-Index: 689 scientists 48–49 D-Index: 543 scientists 50–51 D-Index: 543 scientists 52–53 D-Index: 518 scientists 54–55 D-Index: 500 scientists 56–57 D-Index: 458 scientists 58–59 D-Index: 400 scientists 60–61 D-Index: 337 scientists 62–63 D-Index: 308 scientists 64–65 D-Index: 292 scientists 66–67 D-Index: 249 scientists 68–69 D-Index: 213 scientists 70–71 D-Index: 192 scientists 72–73 D-Index: 189 scientists 74–75 D-Index: 165 scientists 76–77 D-Index: 139 scientists 78–79 D-Index: 119 scientists 80–81 D-Index: 121 scientists 82–83 D-Index: 113 scientists 84–85 D-Index: 88 scientists 86–87 D-Index: 87 scientists 88–89 D-Index: 75 scientists 90–91 D-Index: 69 scientists 92–93 D-Index: 57 scientists 94–95 D-Index: 46 scientists 96–97 D-Index: 38 scientists 98–99 D-Index: 34 scientists 100–101 D-Index: 36 scientists 102–103 D-Index: 27 scientists 104–105 D-Index: 37 scientists 106–107 D-Index: 18 scientists 108–109 D-Index: 31 scientists 110–111 D-Index: 19 scientists 112–113 D-Index: 16 scientists 114–115 D-Index: 12 scientists 116–117 D-Index: 20 scientists 118–119 D-Index: 15 scientists 120–121 D-Index: 5 scientists 122–123 D-Index: 20 scientists 124–125 D-Index: 8 scientists 126–127 D-Index: 5 scientists 128–129 D-Index: 7 scientists 130 D-Index: 3 scientists 131+ D-Index: 98 scientists
30 D-Index 131+

This scientist: 38 D-Index — 30th percentile

30% of scientists in this discipline score the same or lower.

The last bar groups every scientist with 131 D-Index or more.

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
Mian Ahmad Jan
Mian Ahmad Jan University of Sharjah

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