Publication

Google Scholar

Signal Separation (ICA / DL):

  • Wang, Y., Li, W., Choi, J. Understanding word embeddings based on statistical methods.
  • Ge, L., Wang, Y., Guo, Y. A novel independent component modeling framework for multi-center brain imaging data decomposition. (wk)
  • Wang, Y., Guo, Y. Bayesian sparse low-rank dictionary learning with application to large-scale brain network data. (wk)
  • Wang, Y. Highly efficiently autoencoding algorithm via ensemble learning for mixed data modalities.
  • Wang, Y., Guo, Y. Longitudinal independent component analysis with application to fMRI data. Accepted by Neuroimage.
  • Lukemire, J., Wang, Y., Verma A., Guo, Y. HINT: A toolbox for hierarchical independent component modeling of neuroimaging data. Submitted to Journal of Statistical Software.
  • Mejia,. AF, Wang, Y., Guo, Y., Caffo, B. Template-Based Independent Component Analysis of fMRI: Reliable and Efficient Estimation of Subject-Level Resting-State Networks. Accepted by JASA, 2019.

Network Science:

  • Kundu, S., Lukemire, J., Wang, Y., Guo, Y. (2019). A novel multiscale brain network analysis using longitudinal Alzheimer's disease data. Submitted to Nature Scientific Reports.
  • Kemmer, P. B., Wang, Y., Bowman, F. D., Mayberg, H., & Guo, Y. (2017). Quantifying the strength of structural connectivity underlying functional brain networks. arXiv preprint arXiv:1703.04056. Accepted by Brain Connectivity.
  • Yang X., Wang, Y., Guo, Y. A novel path-embedded functional connectivity measure for brain multi-modality analysis with application to fMRI and DTI. (wk)
  • Wang, Y., Wu, H., & Yu, T. (2017). Differential gene network analysis from single cell RNA-seq. Journal of Genetics and Genomics.
  • Wang, Y., Kang, J., Kemmer, P. B., & Guo, Y. (2016). An efficient and reliable statistical method for estimating functional connectivity in large scale brain networks using partial correlation. Frontiers in neuroscience, 10.
  • Kemmer, P. B., Guo, Y., Wang, Y., & Pagnoni, G. (2015). Network-based characterization of brain functional connectivity in Zen practitioners. Frontiers in psychology, 6, 603.
  • Wang, Y., Zhao, Y., Zhang, L., Liang, J., Zeng, M., & Liu, X. (2013). Graph construction based on re-weighted sparse representation for semi-supervised learning. JOURNAL OF INFORMATION &COMPUTATIONAL SCIENCE, 10(2), 375-383.

Time Series Analysis:

  • Guo, Y., Wang, Y., Marin, T., Kirk, E., Patel, R., Josephson, C. Statistical methods for characterizing transfusion-related changes in regional oxygenation using Near-infrared spectroscopy in preterm infants. Accepted by Statistical Methods in Medical Research (currently available at arXiv:1801.08153 [stat.AP] ).
  • Wang, Y., Hu, X., Chang, H., Waller, L., Belle, J., Liu., Y. A Bayesian downscaler model to estimate PM2.5 levels in the Continental US. Accepted by IJERPH.
  • Wang, Y., Gao, X. A novel multivariate reconciliation approach for hierarchical time series forecast via Bayesian restricted state space model (wk).

Collaboration:

  • Treatment Strategies of Hospitalized Patients with Novel Coronavirus-Infected Pneumonia. Submitted to Emerging Infectious Diseases, 2020.
  • Characteristics and Prognosis of 239 hospitalized patients with Novel Coronavirus-Infected Pneumonia in Changsha, submitted to The Lancet Respiratory Medicine, 2020
  • Liu, J., Li, J., Wang, H., Wang, Y., et al. Clinical and genetic risk factors for Fulvestrant treatment in post-menopause ER-positive advanced breast cancer patients. Journal of Translational Medicine, 2019.
  • Zhou D, Ouyang Q, Liu L, Liu J, Tang Y, Xiao M, Wang Y, et al. Chemotherapy modulates endocrine therapy-related resistance mutations in metastatic breast cancer, Translational oncology. 2019 May 1;12(5):764-74.
  • Friend or foe: multiple roles of adipose tissue in cancer formation and progression, accepted by Journal of Cellular Physiology, 2019
  • Prognostic Factors and Treatment Strategies for Intrahepatic Cholangiocarcinoma from 2004 to 2013: population-based SEER analysis , accepted by Translational oncology. 2019.
  • Tang, Y., Wang, H., Wang, Y., et al. FGFR1 mutation increases the risk of brain metastases and implies drug resistance in brain-metastatic breast cancer patients by using Circulating tumor DNA surveillance. submitted to Cancers, 2019
  • A real-world observational study to explore the clinical treatment and prognoses and the genetic aberrations of the patients with breast cancer complicated with brain metastases, submitted to Breast Cancer Research, 2019
  • Hu, C., Wang, Y., et al. Local management for primary tumors in stage IV breast cancer patients: A retrospective study based on SEER database in 2010-2012, submitted to Journal of Translational Medicine in 2019.
  • Wnt5a/Ror2 pathway contributes to the regulation of cholesterol homeostasis and inflammatory response in atherosclerosis, submitted in 2019
  • Breast cancer complicated with brain metastases: a clinical plus genetic investigation, submitted to Mayo Clinic Proceedings, 2019
  • Germline PALB2 mutations in cancers and its distinction from somatic PALB2 mutations in breast cancers, submitted to Frontiers in Genetics, 2019
  • The transcriptome profile of lincRNAs in predicting advanced stage and poor prognosis in breast cancer, submitted to Genomics in 2020.

wk: working paper