Yuanhua Huang

Dr Huang is an assistant professor in the School of Biomedical Sciences and the Department of Statistics and Actuarial Science at the University of Hong Kong (HKU). Prior to joining HKU, he was an EBPOD research fellow in the University of Cambridge and European Bioinformatics Institute (EMBL-EBI). Dr Huang completed his BEng in Automation from Tsinghua University (2009-2013) and PhD in Informatics (Machine learning and computational biology) from the University of Edinburgh (2014-2017).

Education

• Doctor of Philosophy (Ph.D.) The University of Edinburgh (2014 — 2017)
• Bachelor of Engineering (B.E.) Tsinghua University (2009 — 2013)

Companies

• Assistant Professor The University of Hong Kong (2019)
• Postdoctoral Fellow European Bioinformatics Institute | EMBL-EBI (2017 — 2019)
• Visitor Harvard University (2016 — 2016)
• Trainee EMBL-EBI (2013 — 2014)

Research Interests:

• Bioinformatics
• Machine learning
• Single-cell genomics
• Spatial transcriptomics
• Somatic mutations and evolution

Honours and Awards:

• 2017, Best poster award, High Throughput Sequencing algorithms (HiTSeq) workshop, ISMB/ECCB Conference
• 2017, EBPOD postdoctoral fellowship, University of Cambridge and EMBL-European Bioinformatics Institute
• 2018, Chinese Government Award for Outstanding Self-Financed Students Abroad
• 2019, Travel fellowship, Conference on Intelligent Systems for Molecular Biology (ISMB/ECCB), Switzerland

Selected Publications:

• Kwok, A. W. C., Qiao, C., Huang, R., Sham, M. H., Ho, J. W.#, & Huang, Y.# “MQuad enables clonal substructure discovery using single cell mitochondrial variants.” Nature communications, 2022, 13(1): 1-10.
• Hou, R., & Huang, Y.# “Genomic sequences and RNA binding proteins predict RNA splicing efficiency in various single-cell contexts.” Bioinformatics. 2022, btac321.
• Qiao, C., & Huang, Y.# “Representation learning of RNA velocity reveals robust cell transitions.” Proceedings of the National Academy of Sciences, 2021, 118(49).
• Huang, X., & Huang, Y.#. “Cellsnp-lite: an efficient tool for genotyping single cells.” Bioinformatics, 2021, 37(23): 4569-4571.
• Huang, Y.#, & Sanguinetti, G.# “BRIE2: computational identification of splicing phenotypes from single-cell transcriptomic experiments.” Genome biology, 2021, 22(1): 1-15.
• Huang, Y., & Sanguinetti, G. “Uncertainty versus variability: Bayesian methods for analysis of scRNA-seq data.” Current Opinion in Systems Biology, 2021, 28, 100375.
• McCarthy D.†, Rostom R.†, Huang Y.†, Kunz D., Danecek P., Bonder M, Hagai T., Lyu R., Wang W., Gaffney D.J., Simons B.D., Stegle O., Teichmann S.A. “Cardelino: Integrating whole exomes and single-cell transcriptomes to reveal phenotypic impact of somatic variants." Nature Methods ,2020, 17:414-421. †co-first author
• Huang Y., McCarthy D., Stegle O. “Vireo: Bayesian demultiplexing of pooled single-cell RNA-seq data without genotype reference." Genome Biology, 2019, 20(1): 273.
• Huang Y., and Sanguinetti G. “BRIE: transcriptome-wide splicing quantification in single cells." Genome Biology, 2017, 18(1): 123.
• Huang Y., and Sanguinetti G. “Statistical modeling of isoform dynamics from RNA-seq time series data." Bioinformatics, 2016, 32(19): 2965-2972.
• Huang Y., Xu B., Zhou X., Li Y., Lu M., Jiang R., and Li T. “Systematic characterization and prediction of post-translational modification cross-talk." Molecular & Cellular Proteomics, 2015, 14(3): 761-770.