About Me

Who Am I?

Hi I'm Jingxiao Chen I am a Ph.D. student in biostatistics at UTHealth in Houston and working as a graduate research assistant at the Department of Biostatistics and Data Science, University of Texas Health Science Center at Houston, TX, USA. I have been trained in theoretical Statistics, with strong programming, statistics and communication skills developed from extensive research experience and ability independently or as part of a team.

I grew up in Hangzhou located in Zhejiang Province, China, which is a very beautiful slow-paced city in the east coast of China with lots of historical and cultural relics mingling with lush sceneries, a scenic spot barely seen anywhere else in the country. Bathing in a monsoon subtropical climate, the city borders the Yangtze River to the south and is bounded by hills to its west.

Data Analytics

Statistical Modeling

Machine Learning

Data Science

"To be a scientist is to be naive. We are so focused on our search for truth, we fail to consider how few actually it or not, whether we choose to or not. The truth doesn’t care about our needs or wants. It doesn’t care about our governments, our ideologies, our religions. It will lie in wait, for all time. And this, at last, is the gift of Chernobyl. Where I once would fear the cost of truth, now I only ask:
What is the cost of lies?"

My Specialty

Programming

R

95%

SAS

60%

UNIX

75%

MySQL

70%

Python

80%

Languages

Chinese (Native)

English (Proficient)

Education

Education

THE UNIVERSITY OF TEXAS HEALTH SCIENCE CENTER AT HOUSTON, HOUSTON, TX, USA

  • Minor: Epidemiology
  • Breadth: Data Science

    • Anticipated in 2023

    CASE WESTERN RESERVE UNIVERSITY, CLEVELAND, OH, USA

    January 2018

    PURDUE UNIVERSITY, WEST LAFAYETTE, IN, USA

    May 2016

    Discovery Park Undergraduate Research Internship Scholarship, Purdue University 2015-2016

    Experience

    Professional Experience

    Graduate Research Assistant August 2020 - Present
    The University of Texas Health Science Center at Houston, Houston, TX, USA

    Leveraging FITBIR Data to Improve Clinical Practice of Severe Traumatic Brain Injury

  • Harmonize and curate data from various multi-center clinical trial studies
  • Develop and validate statistical and machine learning methods (e.g., regularized regressions, discriminant analysis, random forests, support vector machines, etc.) that assess the association of multimodal longitudinal physiological variables with long-term neurological outcomes
  • Cluster multivariate clinical patient trajectories with many missing values for patient stratification and optimize the personalized medicine

    • SARS-CoV-2 Surveillance Testing Program for Harris County

  • Adopt time-series models (e.g., Auto-Regressive Integrated Moving Average and neural network autoregression models) to predict COVID-19-related ICU and general bed usage for multiple Trauma Service Areas (TSAs) and Harris County in Texas for allocation of medical resources
  • Perform geospatial analysis with the data monitoring the trend of daily and weekly SARS-CoV-2 infections and vaccinations to inform public health policy

    • Graduate Research Assistant August 2018 - July 2020
    MD Anderson Cancer Center, Houston, TX, USA

    Statistical Methods for Genomic Analysis of Heterogeneous Tumors

  • Researched cell-type classification with high dimensional scRNA-seq data using dimensionality reduction techniques and unsupervised learning algorithms data
  • Simulated multimodal transcriptomic data and evaluated transcriptome decomposition performance with survival and time-dependent ROC analysis
  • Established tumor transcriptome deconvolution analysis pipeline to understand the tumor microenvironment (TME) using available gene expression cancer consortium, i.e., TCGA
  • Benchmarked deconvolution methods utilize cell-type-specific gene expression from scRNA-seq data to characterize cell-type compositions from bulk RNA-seq data in complex tissues

    • Risk Prediction for Li-Fraumeni Syndrome: A Practical Tool for Clinical Health Care Providers

  • Built LFSPRO, an R package for TP53 germline mutation carrier and cancer risk predictions which outperformed typical clinical diagnostic criteria
  • Validated the prediction performance of the penetrance estimates from our competing risk-based statistical model trained with data of 186 pediatric-sarcoma families collected at MD Anderson Cancer Center via two independent cohorts combined
  • Provided the first set of penetrance estimates using a recurrent events survival modeling approach for single primary cancers (SPC) and multiple primary cancers (MPC) for TP53 mutation carriers and demonstrated its accuracy for cancer risk assessment via MD Anderson cohort of TP53 tested individuals

    • Biostatistician July 2017 - July 2018
    Cleveland Clinic, Cleveland, OH, USA

  • Performed feature selection, multiple regression, tree-based methods and regularization regression to examine the risk factors of patients undergoing elective posterior lumbar decompression
  • Provided statistical consulting for other researchers and clinicians at the institute
  • Worked as a teaching assistant for biostatistics curriculum at Lerner College of Medicine

    • Publication

    Publication

    Cao, S., Wang, J. R., Ji, S., Yang, P., Chen, J., Montierth, M. D., ... & Livingstone, J. (2020). Differing total mRNA expression shapes the molecular and clinical phenotype of cancer. bioRxiv.

    Shin, S. J., Dodd-Eaton, E. B., Peng, G., Bojadzieva, J., Chen, J., Amos, C. I., ... & Ballinger, M. L. (2020). Penetrance of Different Cancer Types in Families with Li-Fraumeni Syndrome: A Validation Study Using Multicenter Cohorts. Cancer research, 80(2), 354-360.

    Shin, S. J., Dodd-Eaton, E. B., Gao, F., Bojadzieva, J., Chen, J., Kong, X., ... & Wang, W. (2020). Penetrance estimates over time to first and second primary cancer diagnosis in families with Li-Fraumeni syndrome: a single institution perspective. Cancer research, 80(2), 347-353.

    Ilyas, H., Golubovsky, J. L., Chen, J., Winkelman, R. D., Mroz, T. E., & Steinmetz, M. P. (2019). Risk factors for 90-day reoperation and readmission after lumbar surgery for lumbar spinal stenosis. Journal of Neurosurgery: Spine, 31(1), 20-26.

    Cao, S., Wang, Z., Gao, F., Chen, J., Zhang, F., Frigo, D. E., ... & Wang, W. (2019). An R Implementation of Tumor-Stroma-Immune Transcriptome Deconvolution Pipeline using DeMixT. bioRxiv, 566075.

    Golubovsky, J. L., Ilyas, H., Chen, J., Tanenbaum, J. E., Mroz, T. E., & Steinmetz, M. P. (2018). Risk factors and associated complications for postoperative urinary retention after lumbar surgery for lumbar spinal stenosis. The Spine Journal, 18(9), 1533-1539.

    Presentations & Posters

    Presentations & Posters

    iBright 2019, “Deconvolution reveals cell-type-specific transcriptional effects across cancer types”

    ASHG 2019, “LFSPRO: A risk prediction R package for probabilities of age-of-onset of multiple primary cancers and specific cancer types in families with Li-Fraumeni Syndrome”

    Q-bio 2019, “Deconvolution analysis to understand the tumor-stroma-immune environment in prostate cancer”

    Get in Touch

    Contact

    jingxiao.chen@uth.tmc.edu

    @j_chen0322

    @Jingxiao Chen