World's Best Scientists 2026 revealed!

D-Index & Metrics

Genetics

D-Index
60
Citations
16335
World Ranking
3133
National Ranking
1369

Overview

Haixu Tang is affiliated with Indiana University in the United States and engages in research that spans Biochemistry, Genetics and Molecular Biology as well as Computer Science. Their work includes a strong focus on Molecular Biology, Spectroscopy, Artificial Intelligence, Genetics, and Information Systems, reflecting an interdisciplinary approach to biological and computational sciences.

The scientist's research covers key topics such as:

  • Genomics and Phylogenetic Studies
  • Advanced Proteomics Techniques and Applications
  • Machine Learning in Bioinformatics
  • Metabolomics and Mass Spectrometry Studies
  • Bioinformatics and Genomic Networks
  • Gene expression and cancer classification
  • Analytical Chemistry and Chromatography

Haixu Tang has a notable record of recent publications, including:

  • "MOGONET integrates multi-omics data using graph convolutional networks allowing patient classification and biomarker identification" (2021, Nature Communications)
  • "Integration of time-series meta-omics data reveals how microbial ecosystems respond to disturbance" (2020, Nature Communications)
  • "Full-Spectrum Prediction of Peptides Tandem Mass Spectra using Deep Neural Network" (2020, Analytical Chemistry)
  • "Roles of bacteriophages, plasmids and CRISPR immunity in microbial community dynamics revealed using time-series integrated meta-omics" (2020, Nature Microbiology)
  • "Accurate de novo peptide sequencing using fully convolutional neural networks" (2023, Nature Communications)

Their publication record also indicates frequent co-authorship with several researchers, including Yuzhen Ye, Sujun Li, Yuhui Hong, Kaiyuan Liu, and Xiaofeng Wang.

Haixu Tang has contributed to multiple publishing venues, with the highest number of publications appearing in arXiv (Cornell University) and bioRxiv (Cold Spring Harbor Laboratory), followed by Bioinformatics, Nature Communications, and Analytical Chemistry. This distribution highlights a presence in both preprint and peer-reviewed journals focused on computational biology and analytical methods.

In addition to articles, Tang has authored books published by respected scientific publishers. These include "Neural Networks for Chemists" released by the American Chemical Society in 2024 and "Research in Computational Molecular Biology" published by Springer Science+Business Media in 2023.

Best Publications

  • An Eulerian path approach to DNA fragment assembly

    Pavel A. Pevzner;Haixu Tang;Michael S. Waterman

  • The ecoresponsive genome of Daphnia pulex

    John K. Colbourne;Michael E. Pfrender;Michael E. Pfrender;Donald Gilbert;W. Kelley Thomas

  • FragGeneScan: predicting genes in short and error-prone reads

    Mina Rho;Haixu Tang;Yuzhen Ye

  • Rate and molecular spectrum of spontaneous mutations in the bacterium Escherichia coli as determined by whole-genome sequencing

    Heewook Lee;Ellen Popodi;Haixu Tang;Patricia L. Foster

  • RAPSearch2: a fast and memory-efficient protein similarity search tool for next-generation sequencing data

    Yongan Zhao;Haixu Tang;Yuzhen Ye

  • Leaky Cauldron on the Dark Land: Understanding Memory Side-Channel Hazards in SGX

    Wenhao Wang;Guoxing Chen;Xiaorui Pan;Yinqian Zhang

  • De Novo Repeat Classification and Fragment Assembly

    Pavel A Pevzner;Haixu Tang;Glenn Tesler

  • Learning your identity and disease from research papers: information leaks in genome wide association study

    Rui Wang;Yong Fuga Li;XiaoFeng Wang;Haixu Tang

  • Fragment assembly with short reads

    Mark Chaisson;Pavel Pevzner;Haixu Tang

  • The transcriptional diversity of 25 Drosophila cell lines

    Lucy Cherbas;Aarron Willingham;Aarron Willingham;Dayu Zhang;Li Yang

  • Splicing graphs and EST assembly problem.

    Steffen Heber;Max A. Alekseyev;Sing-Hoi Sze;Haixu Tang

  • ISEScan: automated identification of insertion sequence elements in prokaryotic genomes.

    Zhiqun Xie;Haixu Tang

  • Identification of Pol IV and RDR2-dependent precursors of 24 nt siRNAs guiding de novo DNA methylation in Arabidopsis

    Todd Blevins;Todd Blevins;Ram Podicheti;Vibhor Mishra;Michelle Marasco

  • Ancestral reconstruction of segmental duplications reveals punctuated cores of human genome evolution.

    Zhaoshi Jiang;Haixu Tang;Mario Ventura;Maria Francesca Cardone

  • Comparing bacterial communities inferred from 16S rRNA gene sequencing and shotgun metagenomics.

    Neethu Shah;Haixu Tang;Thomas G. Doak;Yuzhen Ye

  • A computational approach toward label-free protein quantification using predicted peptide detectability

    Haixu Tang;Randy J. Arnold;Pedro Alves;Zhiyin Xun

  • Understanding Membership Inferences on Well-Generalized Learning Models

    Yunhui Long;Vincent Bindschaedler;Lei Wang;Diyue Bu

  • Spatial and functional relationships among Pol V-associated loci, Pol IV-dependent siRNAs, and cytosine methylation in the Arabidopsis epigenome

    Andrzej T. Wierzbicki;Ross Cocklin;Anoop Mayampurath;Ryan Lister

  • Diverse CRISPRs evolving in human microbiomes

    Mina Rho;Yu Wei Wu;Haixu Tang;Thomas G. Doak

  • A novel method for multiple alignment of sequences with repeated and shuffled elements

    Benjamin Raphael;Degui Zhi;Haixu Tang;Pavel Pevzner

Frequent Co-Authors

Yehia Mechref
Yehia Mechref Texas Tech University
Predrag Radivojac
Predrag Radivojac Northeastern University
Patricia L. Foster
Patricia L. Foster Indiana University
Pavel A. Pevzner
Pavel A. Pevzner University of California, San Diego
Xiaofeng Wang
Xiaofeng Wang Free University of Bozen-Bolzano
Lucila Ohno-Machado
Lucila Ohno-Machado University of California, San Diego
Xiaoqian Jiang
Xiaoqian Jiang The University of Texas Health Science Center at Houston
Michael Lynch
Michael Lynch Arizona State University
Milos V. Novotny
Milos V. Novotny Indiana University
Craig S. Pikaard
Craig S. Pikaard Indiana University

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