Silvia Liu, PhD

Assistant Professor of Pathology, Division of Experimental Pathology

Dr. Liu is a member of the Division of Experimental Pathology.

Education & Training
BS - Shanghai Jiao Tong University, 2008-2012
PhD - University of Pittsburgh, 2012-2017
Postdoc - The Jackson Laboratory, 2017-2018
Research Interests

My major research interests include:

  • High throughput genomic data analysis by machine learning and statistical methods: Clustering, classification, bi-clustering, meta-analysis, omics data analysis, linear models, regression, probability theory, gene regulation, data preprocessing, feature selection, dimension reduction, biomarker detection, pathway analysis, differential expression analysis, microarray (mRNA, SNP, etc) data analysis, clinical/survival data analysis
  • Next Generation Sequencing (NGS) and Long-read Sequencing data analysis: Transcriptome sequencing (RNA-Seq), whole genome sequencing (WGS), whole exome sequencing (WES), Chip-Seq, Oxford Nanopore sequencing, quality control (QC), trimming, alignment, fusion gene detection, gene expression, structural variation (SV) detection, copy number variation (CNV) analysis, SNP calling, peak calling, long-read sequencing (third-generation) analysis
Representative Publications
  • Yu, Y. P., Ding, Y., Chen, Z., Liu, S., Michalopoulos, A., Chen, R., Gulzar, Z. G., Yang, B., Cieply K. M., Luvison, A., Ren, B., Brooks, J. D., Jarrard, D., Nelson, J. B., Michalopoulos, G. K.*, Tseng, G. C.*, Luo, J.* (2014) Novel fusion transcripts associate with progressive prostate cancer. The American Journal of Pathology. The American journal of pathology 184(10), 2840-2849.
  • Yu, Y. P.^, Liu, S.^, Huo, Z., Martin, A., Nelson, J. B., Tseng, G. C., Luo, J.* (2015) Genomic copy number variations in the genomes of leukocytes predict prostate cancer clinical outcomes. PloS one 10(8), e0135982.
  • Luo, J.*, Liu, S., Zuo Z., Chen, R., Tseng, G. C.*, Yu, Y. P.* (2015) Discovery and classification of fusion transcripts in prostate cancer and normal prostate tissue. The American journal of pathology 185(7), 1834-1845.
  • Huo Z., Ding, Y., Liu, S., Oesterreich, S., Tseng, G. C.* (2016) Meta-analytic framework for sparse K-means to identify disease subtypes in multiple transcriptomic studies. Journal of the American statistical association 111(513), 27-42.
  • Liu. S., Tsai, W., Ding, Y., Chen, R., Fang, Z., Huo, Z., Kim, S., Ma, T., Chang, T., Priedigkeit N. M., Lee, A. V., Luo J., Wang, H.*, Chung, I.*, Tseng, G. C.* (2016) Comprehensive evaluation of fusion transcript detection algorithms and a meta-caller to combine top performing methods in paired-end RNA-seq data. Nucleic acids research 44(5), e47-e47.
  • Wang, L.^, Liu, S.^, Ding, Y., Yuan, S., Ho, Y.*, Tseng, G. C.* (2017) Meta-analytic framework for liquid association. Bioinformatics, btx138.
  • Chen Z., Yu, Y. P., Zuo, Z., Nelson, J. B., Michalopoulos, G. K., Monga, S. P., Liu, S., Tseng, G. C., Luo, J.* (2017) Targeting genomic rearrangements in tumor cells through Cas9-mediated insertion of a suicide gene. Nature biotechnology 35(6), 543-550.
  • Becker, T., Lee, W. P., Leone, J., Zhu, Q., Zhang, C., Liu, S., Sargent, J., Shanker, K., Mil-Homens, A., Cerveira, E., Ryan, M., Cha, J., Navarro, F. C. P., Galeev, T., Gerstein, M., Mills R. E., Shin, D., and Lee, C. (2018). FusorSV: an algorithm for optimally combining data from multiple structural variation detection methods. Genome biology, 19(1), 38.