Gao's research lies at the intersection between computer science and biology. His work has two main focuses: 1) developing theory and methodology in the fields of machine learning and algorithms; and 2) solving key open problems in biological and medical fields through building computational models, developing machine-learning techniques, and designing effective and efficient algorithms. In particular, he aims to solve problems that occur along the path from protein amino acid sequences to their three-dimensional structures and functions that ultimately lead to their undesirable expression in complex biological networks.
A. Abbas, X. Guo, B. Jing, and X. Gao. An automated framework for NMR resonance assignment through simultaneous slice picking and spin system forming. Journal of Biomolecular NMR. (2014). 59(2): 75-86.
H. Kuwahara, M. Fan, S. Wang, and X. Gao. A framework for scalable parameter estimation of gene circuit models using structural information. Bioinformatics. (2013). 29(13): i98-i107.
B. Xie, B. Jankovic, V. Bajic, L. Song, and X. Gao. Poly(A) motif prediction using spectral latent features from human DNA sequences. Bioinformatics. (2013). 29(13): i316-i325.
M. Maadooliat, X. Gao, and J. Huang. Assessing protein conformational sampling methods based on bivariate lag-distributions of backbone angles. Briefings in Bioinformatics. (2013). 14(6): 724-736.
Z. Liu, A. Abbas, B. Jing, and X. Gao. WaVPeak: picking NMR peaks through wavelet transform and volume-based filtering. Bioinformatics (2012), 28(7): 914-920.
B. Alipanahi, X. Gao, E. Karakoc, L. Donaldson, A. Gutmanas, C. Arrowsmith, and M. Li. PICKY: a novel SVD-based NMR spectra peak picking method. Bioinformatics. (2009). 25(12): i268-i275.
X. Gao, D. Bu, J. Xu, and M. Li. Improving consensus contact prediction via server correlation reduction. BMC Structural Biology, 2009, 9:28.