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Abdollah Dehzangi

D-Index & Metrics

Computer Science

D-Index
34
Citations
4403
World Ranking
12250
National Ranking
4963

Abdollah Dehzangi 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 Abdollah Dehzangi 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: 96 publications — 7th percentile

7% of scientists in this discipline score the same or lower.

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

Abdollah Dehzangi 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 Abdollah Dehzangi 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: 34 D-Index — 16th percentile

16% of scientists in this discipline score the same or lower.

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

Overview

Abdollah Dehzangi is affiliated with Rutgers, The State University of New Jersey in the United States. Their research primarily spans the fields of Biochemistry, Genetics and Molecular Biology, with a strong emphasis on Molecular Biology. Additional focus areas include Artificial Intelligence, Radiology, Nuclear Medicine and Imaging, Computational Theory and Mathematics, and Microbiology.

The scientist's work covers several key topics, notably Machine Learning in Bioinformatics, RNA and protein synthesis mechanisms, Genomics and Phylogenetic Studies, Protein Structure and Dynamics, Computational Drug Discovery Methods, COVID-19 diagnosis using AI, and Antimicrobial Peptides and Activities.

Recent publications by Abdollah Dehzangi include:

  • Accurate brain tumor detection using deep convolutional neural network (2022) in Computational and Structural Biotechnology Journal
  • A review on deep learning approaches in healthcare systems: Taxonomies, challenges, and open issues (2020) in Journal of Biomedical Informatics
  • Application of explainable artificial intelligence in medical health: A systematic review of interpretability methods (2023) in Informatics in Medicine Unlocked
  • ACP-MHCNN: an accurate multi-headed deep-convolutional neural network to predict anticancer peptides (2021) in Scientific Reports
  • CNN-KCL: Automatic myocarditis diagnosis using convolutional neural network combined with k-means clustering (2022) in Mathematical Biosciences & Engineering

Their frequent collaborators include Alok Sharma, Hamid Alinejad-Rokny, Swakkhar Shatabda, Shahab S. Band, and Amin Beheshti.

Abdollah Dehzangi has published frequently in venues such as bioRxiv (Cold Spring Harbor Laboratory), Preprints.org, Scientific Reports, Genes, and arXiv (Cornell University).

Best Publications

  • Improving prediction of secondary structure, local backbone angles, and solvent accessible surface area of proteins by iterative deep learning.

    Rhys Heffernan;Kuldip Paliwal;James Lyons;Abdollah Dehzangi

  • A review on deep learning approaches in healthcare systems: Taxonomies, challenges, and open issues.

    Shahab Shamshirband;Shahab Shamshirband;Mahdis Fathi;Abdollah Dehzangi;Anthony Theodore Chronopoulos

  • Gram-positive and Gram-negative protein subcellular localization by incorporating evolutionary-based descriptors into Chou׳s general PseAAC

    Abdollah Dehzangi;Abdollah Dehzangi;Rhys Heffernan;Alok Sharma;Alok Sharma;James Lyons

  • SPIDER2: A Package to Predict Secondary Structure, Accessible Surface Area, and Main-Chain Torsional Angles by Deep Neural Networks

    Yuedong Yang;Rhys Heffernan;Kuldip Paliwal;James Lyons

  • Predicting backbone Cα angles and dihedrals from protein sequences by stacked sparse auto-encoder deep neural network.

    James G. Lyons;Abdollah Dehzangi;Abdollah Dehzangi;Rhys Heffernan;Alok Sharma;Alok Sharma

  • A feature extraction technique using bi-gram probabilities of position specific scoring matrix for protein fold recognition.

    Alok Sharma;James Lyons;Abdollah Dehzangi;Abdollah Dehzangi;Kuldip Kumar Paliwal

  • Highly accurate sequence-based prediction of half-sphere exposures of amino acid residues in proteins

    Rhys Heffernan;Abdollah Dehzangi;Abdollah Dehzangi;James G. Lyons;Kuldip K. Paliwal

  • iDTI-ESBoost: Identification of Drug Target Interaction Using Evolutionary and Structural Features with Boosting.

    Farshid Rayhan;Sajid Ahmed;Swakkhar Shatabda;Dewan Md Farid

  • iDNAProt-ES: Identification of DNA-binding Proteins Using Evolutionary and Structural Features

    Shahana Yasmin Chowdhury;Swakkhar Shatabda;Abdollah Dehzangi

  • PyFeat: A Python-based Effective Feature Generation Tool for DNA, RNA, and Protein Sequences.

    Rafsanjani Muhammod;Sajid Ahmed;Dewan M. Farid;Swakkhar Shatabda

  • Predict Gram-Positive and Gram-Negative Subcellular Localization via Incorporating Evolutionary Information and Physicochemical Features Into Chou's General PseAAC

    Ronesh Sharma;Abdollah Dehzangi;James Lyons;Kuldip Paliwal

  • Computer-aided decision-making for predicting liver disease using PSO-based optimized SVM with feature selection

    Javad Hassannataj Joloudari;Hamid Saadatfar;Abdollah Dehzangi;Shahaboddin Shamshirband

  • SPIN2: Predicting sequence profiles from protein structures using deep neural networks.

    James O'Connell;Zhixiu Li;Jack Hanson;Rhys Heffernan

  • PSSM-Suc: Accurately predicting succinylation using position specific scoring matrix into bigram for feature extraction

    Abdollah Dehzangi;Yosvany López;Sunil Pranit Lal;Ghazaleh Taherzadeh

  • A Tri-Gram Based Feature Extraction Technique Using Linear Probabilities of Position Specific Scoring Matrix for Protein Fold Recognition

    Kuldip K. Paliwal;Alok Sharma;James Lyons;Abdollah Dehzangi

  • Proposing a highly accurate protein structural class predictor using segmentation-based features.

    Abdollah Dehzangi;Abdollah Dehzangi;Kuldip K. Paliwal;James G. Lyons;Alok Sharma;Alok Sharma

  • A Combination of Feature Extraction Methods with an Ensemble of Different Classifiers for Protein Structural Class Prediction Problem

    Abdollah Dehzangi;Kuldip Paliwal;Alok Sharma;Omid Dehzangi

  • Using random forest for protein fold prediction problem: An empirical study

    Abdollah Dehzangi;Somnuk Phon-Amnuaisuk;Omid Dehzangi

  • SPRINT-Gly: predicting N- and O-linked glycosylation sites of human and mouse proteins by using sequence and predicted structural properties.

    Ghazaleh Taherzadeh;Abdollah Dehzangi;Maryam Golchin;Yaoqi Zhou

  • A strategy to select suitable physicochemical attributes of amino acids for protein fold recognition.

    Alok Sharma;Alok Sharma;Kuldip Kumar Paliwal;Abdollah Dehzangi;James Lyons

  • Improving succinylation prediction accuracy by incorporating the secondary structure via helix, strand and coil, and evolutionary information from profile bigrams.

    Abdollah Dehzangi;Yosvany López;Sunil Pranit Lal;Ghazaleh Taherzadeh

  • Success: evolutionary and structural properties of amino acids prove effective for succinylation site prediction.

    Yosvany López;Alok Sharma;Alok Sharma;Abdollah Dehzangi;Sunil Pranit Lal

Frequent Co-Authors

Alok Sharma
Alok Sharma Griffith University
Abdul Sattar
Abdul Sattar Griffith University
Kuldip K. Paliwal
Kuldip K. Paliwal Griffith University
Tatsuhiko Tsunoda
Tatsuhiko Tsunoda University of Tokyo
Yaoqi Zhou
Yaoqi Zhou Griffith University
Yuedong Yang
Yuedong Yang Sun Yat-sen University
Satoru Miyano
Satoru Miyano Tokyo Medical and Dental University
Shoba Ranganathan
Shoba Ranganathan Macquarie University
Bhyravabhotla Jayaram
Bhyravabhotla Jayaram Indian Institute of Technology Delhi
Kenta Nakai
Kenta Nakai University of Tokyo

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Related Online Degrees & Career Pathways

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