Scholars

Qian, Xiaoning

Associate Professor

Single Page Bio

xqian@tamu.edu

1 979 845 7441

Co-author Network

Map of Science

Websites

Personal Webpage

ORCID

0000-0002-4347-2476

overview

Xiaoning Qian's research interests include machine learning and Bayesian experimental design as well as their applications in computational network biology, genomic signal processing, and biomedical signal and image analysis. He is affiliated with the Center for Bioinformatics and Genomic Systems Engineering and the Center for Translational Environmental Health Research at Texas A&M.

education and training

Ph.D. in , Yale University - (New Haven, Connecticut, United States)

selected publications

Academic Articles81

Tsai, C., Hsu, K., Lin, Y., Qian, X., & Chuang, Y. (2020). Deep Co-Saliency Detection via Stacked Autoencoder-Enabled Fusion and Self-Trained CNNs. IEEE Transactions on Multimedia. 22(4), 1016-1031.
Nagashima, Y., Ma, Z., Liu, X., Qian, X., Zhang, X., von Schaewen, A., & Koiwa, H. (2020). Multiple Quality Control Mechanisms in the ER and TGN Determine Subcellular Dynamics and Salt-Stress Tolerance Function of KORRIGAN1.. The Plant Cell. 32(2), 470-485.
Choobdar, S., Ahsen, M. E., Crawford, J., Tomasoni, M., Fang, T., Lamparter, D., ... Ch, D. (2019). Assessment of network module identification across complex diseases. Nature Methods. 16(9), 843-+.
Boluki, S., Esfahani, M. S., Qian, X., & Dougherty, E. R. (2019). Constructing Pathway-Based Priors within a Gaussian Mixture Model for Bayesian Regression and Classification. IEEE/ACM Transactions on Computational Biology and Bioinformatics. 16(2), 524-537.
Fong, T., Qian, X., Li, K., & Huang, S. (2019). Dynamic Inspection of Latent Variables in State-Space Systems. IEEE Transactions on Automation Science and Engineering. 16(3), 1232-1243.

Chapters1

Lu, M., He, K., Huang, J. Z., & Qian, X. (2017). Principal component analysis for exponential family data. Advances in Principal Component Analysis: Research and Development. (pp. 193-223). Springer Singapore.

Conference Papers62

Dehghannasiri, R., Qian, X., & Dougherty, E. R. (2019). Robust Smoothing for State-Space Models with Unknown Noise Statistics. 2016 50TH ASILOMAR CONFERENCE ON SIGNALS, SYSTEMS AND COMPUTERS. 2018-October, 1024-1028.
Hajiramezanali, E., Dadaneh, S. Z., Zhou, M., Karbalayghareh, A., & Qian, X. (2018). Bayesian multi-domain learning for cancer subtype discovery from next-generation sequencing count data. ADVANCES IN NEURAL INFORMATION PROCESSING SYSTEMS 31 (NIPS 2018). 2018-December, 9115-9124.
Zamani Dadaneh, S., Zhou, M., & Qian, X. (2018). Covariate-dependent negative binomial factor analysis of RNA sequencing data.. BIOINFORMATICS. 34(13), i61-i69.
Li, W., Qian, X., & Ji, J. (2018). Noise-tolerant deep learning for histopathological image segmentation. 2003 INTERNATIONAL CONFERENCE ON IMAGE PROCESSING, VOL 1, PROCEEDINGS. 2017-September, 3075-3079.
Karbalayghareh, A., Qian, X., & Dougherty, E. R. (2018). Optimal Bayesian Transfer Learning for Classifying Multivariate Gaussian Observations.