World's Best Scientists 2026 revealed!

D-Index & Metrics

Genetics

D-Index
65
Citations
17106
World Ranking
2688
National Ranking
1180

Fengzhu Sun publication distribution in Genetics in 2026

The chart shows the distribution of publications by all Research.com ranked scientists in the field of Genetics in 2026. The highlighted bar marks where Fengzhu Sun sits on this spectrum.

45–54 publications: 6 scientists 55–64 publications: 10 scientists 65–74 publications: 35 scientists 75–84 publications: 84 scientists 85–94 publications: 102 scientists 95–104 publications: 151 scientists 105–114 publications: 175 scientists 115–124 publications: 203 scientists 125–134 publications: 217 scientists 135–144 publications: 205 scientists 145–154 publications: 193 scientists 155–164 publications: 188 scientists 165–174 publications: 170 scientists 175–184 publications: 178 scientists 185–194 publications: 164 scientists 195–204 publications: 173 scientists 205–214 publications: 159 scientists 215–224 publications: 134 scientists 225–234 publications: 143 scientists 235–244 publications: 105 scientists 245–254 publications: 114 scientists 255–264 publications: 92 scientists 265–274 publications: 88 scientists 275–284 publications: 87 scientists 285–294 publications: 80 scientists 295–304 publications: 62 scientists 305–314 publications: 75 scientists 315–324 publications: 67 scientists 325–334 publications: 60 scientists 335–344 publications: 52 scientists 345–354 publications: 40 scientists 355–364 publications: 48 scientists 365–374 publications: 47 scientists 375–384 publications: 46 scientists 385–394 publications: 31 scientists 395–404 publications: 27 scientists 405–414 publications: 40 scientists 415–424 publications: 30 scientists 425–434 publications: 43 scientists 435–444 publications: 29 scientists 445–454 publications: 14 scientists 455–464 publications: 28 scientists 465–474 publications: 21 scientists 475–484 publications: 21 scientists 485–494 publications: 22 scientists 495–504 publications: 17 scientists 505–514 publications: 12 scientists 515–524 publications: 11 scientists 525–534 publications: 8 scientists 535–544 publications: 8 scientists 545–554 publications: 14 scientists 555–564 publications: 4 scientists 565–574 publications: 11 scientists 575–584 publications: 5 scientists 585–594 publications: 11 scientists 595–604 publications: 12 scientists 605–614 publications: 7 scientists 615–624 publications: 6 scientists 625–634 publications: 10 scientists 635–644 publications: 9 scientists 645–654 publications: 10 scientists 655–664 publications: 6 scientists 665–674 publications: 6 scientists 675–684 publications: 6 scientists 685–694 publications: 4 scientists 695–702 publications: 6 scientists 703+ publications: 100 scientists
45 publications 703+

This scientist: 190 publications — 46th percentile

46% of scientists in this discipline score the same or lower.

The last bar groups every scientist with 703 publications or more.

Fengzhu Sun D-index placement in Genetics in 2026

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

40–41 D-Index: 24 scientists 42–43 D-Index: 52 scientists 44–45 D-Index: 84 scientists 46–47 D-Index: 112 scientists 48–49 D-Index: 118 scientists 50–51 D-Index: 141 scientists 52–53 D-Index: 143 scientists 54–55 D-Index: 145 scientists 56–57 D-Index: 179 scientists 58–59 D-Index: 162 scientists 60–61 D-Index: 175 scientists 62–63 D-Index: 191 scientists 64–65 D-Index: 172 scientists 66–67 D-Index: 184 scientists 68–69 D-Index: 164 scientists 70–71 D-Index: 158 scientists 72–73 D-Index: 150 scientists 74–75 D-Index: 136 scientists 76–77 D-Index: 127 scientists 78–79 D-Index: 127 scientists 80–81 D-Index: 111 scientists 82–83 D-Index: 110 scientists 84–85 D-Index: 110 scientists 86–87 D-Index: 84 scientists 88–89 D-Index: 102 scientists 90–91 D-Index: 66 scientists 92–93 D-Index: 72 scientists 94–95 D-Index: 70 scientists 96–97 D-Index: 54 scientists 98–99 D-Index: 60 scientists 100–101 D-Index: 49 scientists 102–103 D-Index: 55 scientists 104–105 D-Index: 45 scientists 106–107 D-Index: 42 scientists 108–109 D-Index: 28 scientists 110–111 D-Index: 39 scientists 112–113 D-Index: 25 scientists 114–115 D-Index: 31 scientists 116–117 D-Index: 29 scientists 118–119 D-Index: 34 scientists 120–121 D-Index: 29 scientists 122–123 D-Index: 29 scientists 124–125 D-Index: 18 scientists 126–127 D-Index: 27 scientists 128–129 D-Index: 22 scientists 130–131 D-Index: 16 scientists 132–133 D-Index: 11 scientists 134–135 D-Index: 17 scientists 136–137 D-Index: 12 scientists 138–139 D-Index: 21 scientists 140–141 D-Index: 4 scientists 142–143 D-Index: 9 scientists 144–145 D-Index: 14 scientists 146–147 D-Index: 6 scientists 148–149 D-Index: 10 scientists 150–151 D-Index: 7 scientists 152–153 D-Index: 9 scientists 154–155 D-Index: 8 scientists 156–157 D-Index: 8 scientists 158–159 D-Index: 9 scientists 160+ D-Index: 96 scientists
40 D-Index 160+

This scientist: 65 D-Index — 39th percentile

39% of scientists in this discipline score the same or lower.

The last bar groups every scientist with 160 D-Index or more.

Research.com Recognitions

  • 2015 - Fellow of the American Statistical Association (ASA)
  • 2012 - Fellow of the American Association for the Advancement of Science (AAAS)

Overview

Fengzhu Sun is affiliated with the University of Southern California in the United States. Their research spans various aspects of biochemistry, genetics, and molecular biology, with a particular focus on molecular biology as the main subfield. Other notable subfields include ecology, genetics, infectious diseases, and cancer research.

The scientist has contributed significantly to topics such as genomics and phylogenetic studies, gene expression and cancer classification, bacteriophages and microbial interactions, gut microbiota and health, bioinformatics and genomic networks, microbial community ecology and physiology, and metabolomics and mass spectrometry studies.

Frequent coauthors associated with Fengzhu Sun include Yuxuan Du, Jed A. Fuhrman, Yihui Luan, Sonia Michail, and Beibei Wang.

Common publication venues for Sun's work include bioRxiv (Cold Spring Harbor Laboratory), Nature Communications, Journal of Computational Biology, Scientific Reports, and PLoS Computational Biology.

Recent papers associated with Fengzhu Sun cover various areas of biology and computational methods:

  • Identifying viruses from metagenomic data using deep learning, 2020, Quantitative Biology
  • Association of relative brain age with tobacco smoking, alcohol consumption, and genetic variants, 2020, Scientific Reports
  • A network-based integrated framework for predicting virus-prokaryote interactions, 2020, NAR Genomics and Bioinformatics
  • Cost-effective methylome sequencing of cell-free DNA for accurately detecting and locating cancer, 2022, Nature Communications
  • 16S rRNA and metagenomic shotgun sequencing data revealed consistent patterns of gut microbiome signature in pediatric ulcerative colitis, 2022, Scientific Reports

Sun's work has been recognized through fellowships in professional scientific organizations:

  • Fellow of the American Statistical Association (ASA), 2015
  • Fellow of the American Association for the Advancement of Science (AAAS), 2012

Best Publications

  • Inferring domain-domain interactions from protein-protein interactions

    Minghua Deng;Shipra Mehta;Fengzhu Sun;Ting Chen

  • Prediction of protein function using protein-protein interaction data.

    Minghua Deng;Kui Zhang;Shipra Mehta;Ting Chen

  • Correlation detection strategies in microbial data sets vary widely in sensitivity and precision

    Sophie Weiss;Will Van Treuren;Catherine Lozupone;Karoline Faust;Karoline Faust

  • Marine bacterial, archaeal and protistan association networks reveal ecological linkages.

    Joshua A. Steele;Peter D. Countway;Li Xia;Patrick D. Vigil

  • Identifying viruses from metagenomic data using deep learning.

    Jie Ren;Kai Song;Chao Deng;Nathan A. Ahlgren

  • VirFinder: a novel k -mer based tool for identifying viral sequences from assembled metagenomic data

    Jie Ren;Nathan A. Ahlgren;Nathan A. Ahlgren;Yang Young Lu;Jed A. Fuhrman

  • A dynamic programming algorithm for haplotype block partitioning

    Kui Zhang;Minghua Deng;Ting Chen;Michael S. Waterman

  • Taq DNA polymerase slippage mutation rates measured by PCR and quasi‐likelihood analysis: (CA/GT)n and (A/T)n microsatellites

    Deepali Shinde;Yinglei Lai;Fengzhu Sun;Norman Arnheim

  • Haplotype Block Structure and Its Applications to Association Studies: Power and Study Designs

    Kui Zhang;Peter Calabrese;Magnus Nordborg;Fengzhu Sun

  • Local similarity analysis reveals unique associations among marine bacterioplankton species and environmental factors

    Quansong Ruan;Debojyoti Dutta;Michael S. Schwalbach;Joshua A. Steele

  • The Relationship Between Microsatellite Slippage Mutation Rate and the Number of Repeat Units

    Yinglei Lai;Fengzhu Sun

  • A critical assessment of Mus musculus gene function prediction using integrated genomic evidence.

    Lourdes Pena-Castillo;Murat Tasan;Chad L Myers;Hyunju Lee

  • Clustering of Caucasian Leber hereditary optic neuropathy patients containing the 11778 or 14484 mutations on an mtDNA lineage.

    Brown;F. Sun;D.C. Wallace

  • Alignment-free $d_2^*$ oligonucleotide frequency dissimilarity measure improves prediction of hosts from metagenomically-derived viral sequences

    Nathan A. Ahlgren;Jie Ren;Yang Young Lu;Jed A. Fuhrman

  • CancerLocator: non-invasive cancer diagnosis and tissue-of-origin prediction using methylation profiles of cell-free DNA.

    Shuli Kang;Qingjiao Li;Quan Chen;Yonggang Zhou

  • Assessment of the reliability of protein-protein interactions and protein function prediction.

    Minghua Deng;Fengzhu Sun;Ting Chen

  • An integrated probabilistic model for functional prediction of proteins.

    Minghua Deng;Ting Chen;Fengzhu Sun

  • Extended local similarity analysis (eLSA) of microbial community and other time series data with replicates

    Li C Xia;Joshua A Steele;Joshua A Steele;Jacob A Cram;Zoe G Cardon

  • Haplotype block partitioning and tag SNP selection using genotype data and their applications to association studies

    Kui Zhang;Zhaohui S. Qin;Jun S. Liu;Ting Chen

  • Alignment-free sequence comparison (I): statistics and power.

    Gesine Reinert;David Chew;Fengzhu Sun;Michael S. Waterman

Frequent Co-Authors

Michael S. Waterman
Michael S. Waterman University of Southern California
Jed A. Fuhrman
Jed A. Fuhrman University of Southern California
Gesine Reinert
Gesine Reinert University of Oxford
Shanfeng Zhu
Shanfeng Zhu Fudan University
Rui Jiang
Rui Jiang Beijing Jiaotong University
Xuegong Zhang
Xuegong Zhang Tsinghua University
Hiroshi Mamitsuka
Hiroshi Mamitsuka Kyoto University
Arthur W. Toga
Arthur W. Toga University of Southern California
Jianfeng Feng
Jianfeng Feng Fudan University
Hongyu Zhao
Hongyu Zhao Yale University

If you think any of the details on this page are incorrect, let us know.

Report an issue

We appreciate your kind effort to assist us to improve this page, it would be helpful providing us with as much detail as possible in the text box below:

Related Online Degrees & Career Pathways

Exploring Genetics can open doors to a range of healthcare degrees and dynamic career opportunities in the USA. Many professionals interested in genetics first gain foundational experience in healthcare fields and then pursue specialized education online.

For those with a nursing background, an rn to bsn program without clinicals can be an appealing route, offering flexibility for working adults. If you are aiming higher, a fastest dnp program online might accelerate your path to leadership in nursing and healthcare research, relevant for those interested in genetic applications in clinical practice.

Short on time? Many students choose medical assistant programs accelerated to quickly enter the healthcare workforce and build hands-on skills that complement a future in genetics.

Additionally, students seeking a terminal degree with genetics applications in advanced nursing practice may explore dnp programs that simplify admission and completion. Together, these online pathways provide accessible entry points and ongoing advancement for anyone interested in genetics and healthcare careers.

Best Scientists Citing Fengzhu Sun

Trending Scientists