World's Best Scientists 2026 revealed!

D-Index & Metrics

Computer Science

D-Index
38
Citations
3453
World Ranking
10417
National Ranking
2

Md. Mehedi Hasan 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 Md. Mehedi Hasan 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: 60 publications — 1st percentile

1% of scientists in this discipline score the same or lower.

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

Md. Mehedi Hasan 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 Md. Mehedi Hasan 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

Md. Mehedi Hasan is affiliated with the American International University-Bangladesh in Bangladesh. Their research primarily spans the fields of biochemistry, genetics, and molecular biology, with a significant number of publications contributing to molecular biology.

The scientist's work covers a range of main topics including:

  • Machine Learning in Bioinformatics
  • RNA and protein synthesis mechanisms
  • Genomics and Phylogenetic Studies
  • Vaccines and immunoinformatics approaches
  • Antimicrobial Peptides and Activities
  • RNA modifications and cancer
  • Advanced Malware Detection Techniques

Md. Mehedi Hasan has published extensively in several scientific venues. Frequent publication outlets include:

  • Briefings in Bioinformatics
  • SSRN Electronic Journal
  • International Journal of Molecular Sciences
  • Scientific Reports
  • Journal of Computer-Aided Molecular Design

Collaboration is notable in Hasan's career, with frequent co-authors comprising:

  • Watshara Shoombuatong
  • Hiroyuki Kurata
  • Phasit Charoenkwan
  • Balachandran Manavalan
  • Chanin Nantasenamat

Recent papers authored or co-authored by Md. Mehedi Hasan highlight contributions to peptide prediction and machine learning methodologies. Examples include:

  • HLPpred-Fuse: improved and robust prediction of hemolytic peptide and its activity by fusing multiple feature representation, 2020, Bioinformatics
  • Meta-i6mA: an interspecies predictor for identifying DNAN6-methyladenine sites of plant genomes by exploiting informative features in an integrative machine-learning framework, 2020, Briefings in Bioinformatics

Other significant papers co-authored by Hasan's collaborators in related domains include:

  • BERT4Bitter: a bidirectional encoder representations from transformers (BERT)-based model for improving the prediction of bitter peptides, 2021, Bioinformatics
  • iUmami-SCM: A Novel Sequence-Based Predictor for Prediction and Analysis of Umami Peptides Using a Scoring Card Method with Propensity Scores of Dipeptides, 2020, Journal of Chemical Information and Modeling
  • StackIL6: a stacking ensemble model for improving the prediction of IL-6 inducing peptides, 2021, Briefings in Bioinformatics

Best Publications

  • HLPpred-Fuse: improved and robust prediction of hemolytic peptide and its activity by fusing multiple feature representation

    Md. Mehedi Hasan;Md. Mehedi Hasan;Nalini Schaduangrat;Shaherin Basith;Gwang Lee

  • BERT4Bitter: a bidirectional encoder representations from transformers (BERT)-based model for improving the prediction of bitter peptides.

    Phasit Charoenkwan;Chanin Nantasenamat;Md. Mehedi Hasan;Balachandran Manavalan

  • PreAIP: Computational Prediction of Anti-inflammatory Peptides by Integrating Multiple Complementary Features

    Mst. Shamima Khatun;Md. Mehedi Hasan;Hiroyuki Kurata

  • iUmami-SCM: A Novel Sequence-Based Predictor for Prediction and Analysis of Umami Peptides Using a Scoring Card Method with Propensity Scores of Dipeptides.

    Phasit Charoenkwan;Janchai Yana;Chanin Nantasenamat;Md. Mehedi Hasan

  • LSTM-PHV: prediction of human-virus protein-protein interactions by LSTM with word2vec.

    Sho Tsukiyama;Md. Mehedi Hasan;Satoshi Fujii;Hiroyuki Kurata

  • Meta-i6mA: an interspecies predictor for identifying DNA N6-methyladenine sites of plant genomes by exploiting informative features in an integrative machine-learning framework.

    Md. Mehedi Hasan;Shaherin Basith;Mst. Shamima Khatun;Gwang Lee

  • SuccinSite: a computational tool for the prediction of protein succinylation sites by exploiting the amino acid patterns and properties

    Md. Mehedi Hasan;Shiping Yang;Yuan Zhou;Md. Nurul Haque Mollah

  • iBitter-SCM: Identification and characterization of bitter peptides using a scoring card method with propensity scores of dipeptides

    Phasit Charoenkwan;Janchai Yana;Nalini Schaduangrat;Chanin Nantasenamat

  • iDPPIV-SCM: A Sequence-Based Predictor for Identifying and Analyzing Dipeptidyl Peptidase IV (DPP-IV) Inhibitory Peptides Using a Scoring Card Method.

    Phasit Charoenkwan;Sakawrat Kanthawong;Chanin Nantasenamat;Md. Mehedi Hasan

  • Computational Identification of Protein Pupylation Sites by Using Profile-Based Composition of k-Spaced Amino Acid Pairs

    Md. Mehedi Hasan;Yuan Zhou;Xiaotian Lu;Jinyan Li

  • NeuroPred-FRL: an interpretable prediction model for identifying neuropeptide using feature representation learning.

    Mehedi Hasan;Mehedi Hasan;Ashad Alam;Watshara Shoombuatong;Hong-Wen Deng

  • i4mC-ROSE, a bioinformatics tool for the identification of DNA N4-methylcytosine sites in the Rosaceae genome.

    Md. Mehedi Hasan;Balachandran Manavalan;Mst. Shamima Khatun;Hiroyuki Kurata

  • GPSuc: Global Prediction of Generic and Species-specific Succinylation Sites by aggregating multiple sequence features.

    Md. Mehedi Hasan;Hiroyuki Kurata

  • Improved prediction and characterization of anticancer activities of peptides using a novel flexible scoring card method.

    Phasit Charoenkwan;Wararat Chiangjong;Vannajan Sanghiran Lee;Chanin Nantasenamat

  • i6mA-Fuse: improved and robust prediction of DNA 6 mA sites in the Rosaceae genome by fusing multiple feature representation

    Md. Mehedi Hasan;Md. Mehedi Hasan;Balachandran Manavalan;Watshara Shoombuatong;Mst. Shamima Khatun

  • Meta-iPVP: a sequence-based meta-predictor for improving the prediction of phage virion proteins using effective feature representation

    Phasit Charoenkwan;Chanin Nantasenamat;Md. Mehedi Hasan;Watshara Shoombuatong

  • i4mC-Mouse: Improved identification of DNA N4-methylcytosine sites in the mouse genome using multiple encoding schemes.

    Md. Mehedi Hasan;Md. Mehedi Hasan;Balachandran Manavalan;Watshara Shoombuatong;Mst. Shamima Khatun

  • Integrative machine learning framework for the identification of cell-specific enhancers from the human genome.

    Shaherin Basith;Md. Mehedi Hasan;Md. Mehedi Hasan;Gwang Lee;Leyi Wei;Leyi Wei

  • Computational identification of protein S-sulfenylation sites by incorporating the multiple sequence features information

    Md. Mehedi Hasan;Dianjing Guo;Hiroyuki Kurata

  • Prediction of S-nitrosylation sites by integrating support vector machines and random forest.

    Md. Mehedi Hasan;Md. Mehedi Hasan;Balachandran Manavalan;Mst. Shamima Khatun;Hiroyuki Kurata

Frequent Co-Authors

Leyi Wei
Leyi Wei Shandong University
Jiangning Song
Jiangning Song Monash University
Jinyan Li
Jinyan Li University of Technology Sydney

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