Skip to main content
Shibiao Wan, PhD

Shibiao Wan, PhD

  • Bioinformatics Research Scientist, Transcriptomics


Postdoc, Bioinformatics and Machine Learning – University of Pennsylvania, Philadelphia, PA, USA
Postdoc, Bioinformatics and Machine Learning – Princeton University, Princeton, NJ, USA
PhD, Bioinformatics and Machine Learning - The Hong Kong Polytechnic University, Hong Kong SAR
BEng, Telecommunication Engineering - Wuhan University, Wuhan, China


Dr. Shibiao Wan is currently a Bioinformatics Research Scientist at St. Jude Children’s Research Hospital. His research interests include bioinformatics, computational biology and machine learning, especially in single cell analysis, transcriptomics, sequence analysis, genomics, epigenetics, metabolism, cardiovascular biology, neurobiology, etc. Dr. Wan developed a series of bioinformatics tools in areas including single-cell data analysis (e.g., SHARP), protein subcellular localization (e.g., FUEL-mLoc, Gram-LocEN, mGOASVM, mLASSO-Hum, HybridGO-Loc, etc), membrane protein function prediction (e.g., Mem-mEN, Mem-ADSVM), and protein subchloroplast localization (e.g., LNP-Chlo, EnTrans-Chlo).

Research Interests

  • Single-cell data analysis and algorithm development
  • Transcriptomics, proteomics, sequence analysis
  • Machine learning, data mining, dimension reduction

Selected Publications

S. Wan, J. Kim, and K. J.  Won, "SHARP: Hyper-Fast and Accurate Processing of Single-Cell RNA-seq Data via Ensemble Random Projection", Genome Research, 2020, vol. 30, pp. 205-213.

T. Sakamoto, T. Matsuura, S. Wan, D. Ryba, J. Kim, K. J. Won, L. Lai, C. Petucci, N. Petrenko, K. Musunuru, R. Vega, D. Kelly, “A Critical Role for Estrogen-Related Receptor Signaling in Cardiac Maturation”, Circulation Research, 2020, vol. 126, pp. 1685-1702.

Ahn, S. Wan, N. Jaiswal, R. Vega, D. E. Ayer, P. M. Titchenell, X. Han, K. J. Won, and D. P. Kelly, "MondoA Drives Muscle Lipid Accumulation and Insulin Resistance", JCI Insight, 2019, vol. 4, pp. 15.

S. Wan and M. W. Mak, "Predicting Subcellular Localization of Multi-Location Proteins by Improving Support Vector Machines with an Adaptive-Decision Scheme", International Journal of Machine Learning and Cybernetics, 2018, vol. 9, pp. 399-411.

J. Q. Wang, C. Zhang, S. Wan, and G. Peng, "Is Congenital Amusia a Disconnection Syndrome? A Study Combining Tract- and Network-Based Analysis", Frontiers in Human Neuroscience, 2017, vol. 11, pp. 473. doi: 10.3389/fnhum.2017.00473. eCollection 2017.

S. Wan, M. W. Mak, and S. Y. Kung, "Gram-LocEN: Interpretable Prediction of Subcellular Multi-Localization of Gram-Positive and Gram-Negative Bacterial Proteins", Chemometrics and Intelligent Laboratory Systems, 2017, vol. 162, pp. 1-9.

S. Wan, M. W. Mak, and S. Y. Kung, "FUEL-mLoc: Feature-Unified Prediction and Explanation of Multi-Localization of Cellular Proteins in Multiple Organisms", Bioinformatics, 2017, vol. 33, pp. 749-750.

S. Wan, M. W. Mak, and S. Y. Kung, "Ensemble Linear Neighborhood Propagation for Predicting Subchloroplast Localization of Multi-Location Proteins", Journal of Proteome Research, 2016, vol. 15, pp. 4755-4762.

S. Wan, M. W. Mak, and S. Y. Kung, "Benchmark Data for Identifying Multi-Functional Types of Membrane Proteins", Data in Brief, 2016, vol. 8, pp. 105-107.

S. Wan, M. W. Mak, and S. Y. Kung, "Mem-ADSVM: A Two-Layer Multi-Label Predictor for Identifying Multi-Functional Types of Membrane Proteins", Journal of Theoretical Biology, 2016, vol. 398, pp. 32-42.

S. Wan, M. W. Mak, and S. Y. Kung, "Transductive Learning for Multi-Label Protein Subchloroplast Localization Prediction", IEEE/ACM Transactions on Computational Biology and Bioinformatics, 2017, vol. 14, pp. 212-224. 

S. Wan, M. W. Mak, and S. Y. Kung, "Sparse Regressions for Predicting and Interpreting Subcellular Localization of Multi-Label Proteins", BMC Bioinformatics, 2016, 17:97.

S. Wan, M. W. Mak, and S. Y. Kung, "Mem-mEN: Predicting Multi-Functional Types of Membrane Proteins by Interpretable Elastic Nets", IEEE/ACM Transactions on Computational Biology and Bioinformatics, 2016, vol. 13, pp. 706-718. 

S. Wan, M. W. Mak, and S. Y. Kung, "mLASSO-Hum: A LASSO-Based Interpretable Human-Protein Subcellular Localization Predictor", Journal of Theoretical Biology, 2015, vol. 382, pp. 223-234. 

S. Wan, M. W. Mak, and S. Y. Kung, " mPLR-Loc: An Adaptive-decision Multi-label Classifier Based on Penalized Logistic Regression for Protein Subcellular Localization Prediction", Analytical Biochemistry, 2015, vol. 473, pp. 14-27.

S. Wan, M. W. Mak, and S. Y. Kung, "R3P-Loc: A Compact Multi-label Predictor Using Ridge Regression and Random Projection for Protein Subcellular Localization", Journal of Theoretical Biology, 2014, vol.360, pp. 34-45. 

S. Wan, M. W. Mak, and S. Y. Kung, "HybridGO-Loc: Mining Hybrid Features on Gene Ontology for Predicting Subcellular Localization of Multi-Location Proteins", PLoS ONE2014,  9(3): e89545.

S. Wan, M. W. Mak, and S. Y. Kung, "GOASVM: A Subcellular Location Predictor by Incorporating Term-Frequency Gene Ontology into the General Form of Chou’s Pseudo-Amino Acid Composition", Journal of Theoretical Biology, 2013, vol. 323, pp. 40–48. 

S. Wan, M. W. Mak, and S. Y. Kung, "mGOASVM: Multi-label Protein Subcellular Localization Based on Gene Ontology and Support Vector Machines", BMC Bioinformatics, 2012, 13:290.

Full list of publications

Last update: June 2020