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Your Environment. Your Health.

YuanYuan Li, Ph.D.

Biostatistics & Computational Biology Branch

YuanYuan Li
Yuanyuan Li, Ph.D.
Staff Scientist
Tel 984-287-3838
yuanyuan.li@nih.gov
111 T W Alexander Dr
Rall Building
Research Triangle Park, NC 27709

Research Summary

Yuanyuan Li, Ph.D. is a staff scientist in the Biostatistics and Computational Biology Branch. Her primary research interest is in bioinformatics, a rapidly growing area where computational, mathematical, and statistical methods are developed and applied to solve complex biological problems. With an exponentially growing amount of genomic, epigenomic and clinical data, it is critical to develop suitable and realistic models as well as efficient algorithms to extract useful information. Machine learning, as a subdiscipline of bioinformatics, has demonstrated its effectiveness in many biological applications. Among various machine-learning methods, tree-based methods offer many advantages, including high efficiency, interpretability, and performance. They can also handle non-linear relationships, as well as heterogenous data types.

Li’s research has centered on the development and application of machine-learning methods, especially tree-based methods, for mining and interpreting complex biological data sets. She has applied these methods, for example, to locate constitutive protein bonding sites or genomic hotspots across a set of samples, to classify samples, to identify gene signatures that can distinguish different classes of samples, and to predict continuous outcomes such as tumor purity. The overall goals of her research are to develop and apply computational/statistical methods to questions related to biological and environmental health sciences research.

Software

  • T-KDE: T-KDE will identify the locations of constitutive binding sites. T-KDE, which combines a binary range tree with a kernel density estimator, is applied to ChIP-seq data from multiple cell lines.

Selected Publications

  1. Y. Li, M. Li, I. Shats, J. M. Krahn, G. P. Flake, D. M. Umbach, X. Li, and L. Li, Glypican 6 Is a Putative Biomarker for Metastatic Progression of Cutaneous Melanoma, PLoS One, to appear, 2019.
  2. Nguyen TT, Grimm SA, Bushel PR, Li J, Li Y, Bennett BD, Lavender CA, Ward JM, Fargo DC, Anderson CW, Li L, Resnick MA, Menendez D. Revealing a Human p53 Universe. Nucleic Acids Research. 46 (16), 8153-8167, 2018. [Abstract Nguyen TT, Grimm SA, Bushel PR, Li J, Li Y, Bennett BD, Lavender CA, Ward JM, Fargo DC, Anderson CW, Li L, Resnick MA, Menendez D. Revealing a Human p53 Universe. Nucleic Acids Research. 46 (16), 8153-8167, 2018.]
  3. Y. Li, D. M. Umbach, and L. Li, Putative Genomic Characteristics of Braf V600k Versus V600e Cutaneous Melanoma, Melanoma Research, 27(6), 527-535, 2017. [Abstract Y. Li, D. M. Umbach, and L. Li, Putative Genomic Characteristics of Braf V600k Versus V600e Cutaneous Melanoma, Melanoma Research, 27(6), 527-535, 2017.]
  4. Y. Li, J. M. Krahn, N. Croutwater, K. Lee, D. M. Umbach, and L. Li, A Comprehensive Genomic Pan-cancer Analysis Using the Cancer Genome Atlas Gene Expression Data, BMC Genomics, 18(1), 508, 2017. [Abstract Y. Li, J. M. Krahn, N. Croutwater, K. Lee, D. M. Umbach, and L. Li, A Comprehensive Genomic Pan-cancer Analysis Using the Cancer Genome Atlas Gene Expression Data, BMC Genomics, 18(1), 508, 2017.]
  5. Y. Li, J. M. Krahn, G. P. Flake, D. M. Umbach and L. Li, Toward Predicting Metastatic Progression of Melanoma Based on Gene Expression Data, Pigment Cell & Melanoma Research, 28(4), 453-463, 2015. [Abstract Y. Li, J. M. Krahn, G. P. Flake, D. M. Umbach and L. Li, Toward Predicting Metastatic Progression of Melanoma Based on Gene Expression Data, Pigment Cell & Melanoma Research, 28(4), 453-463, 2015.]
  6. Y. Li, D.M. Umbach, L. Li. T-KDE: A Method for Genome-wide Identification of Constitutive Protein Binding Sites from Multiple ChIP-seq Data Sets. BMC Genomics, 15 (1), 27, 2014. [Abstract Y. Li, D.M. Umbach, L. Li. T-KDE: A Method for Genome-wide Identification of Constitutive Protein Binding Sites from Multiple ChIP-seq Data Sets. BMC Genomics, 15 (1), 27, 2014.]
  7. Y. Li, M. Thomason, and L. E. Parker, Sequential Anomaly Detection Using Wireless Sensor Networks in Unknown Environments, Human Behavior Understanding in Networked Sensing - Theory and Applications of Networks of Sensors, 99-123, 2014. 
  8. Y. Li and L. E. Parker, Nearest Neighbor Imputation Using Spatial-Temporal Correlations in Wireless Sensor Networks, Information Fusion, 15, 64-79, 2014. [Abstract Y. Li and L. E. Parker, Nearest Neighbor Imputation Using Spatial-Temporal Correlations in Wireless Sensor Networks, Information Fusion, 15, 64-79, 2014.]
  9. Li Y., W. Huang, L. Niu, S. Covo, D.M. Umbach, D.M., and Li, L. Characterization of Constitutive CTCF/Cohesin Loci: A Possible Role in Establishing Topological Domains in Mammalian Genomes. BMC Genomics, 14(1), 553, 2013. [Abstract Li Y., W. Huang, L. Niu, S. Covo, D.M. Umbach, D.M., and Li, L. Characterization of Constitutive CTCF/Cohesin Loci: A Possible Role in Establishing Topological Domains in Mammalian Genomes. BMC Genomics, 14(1), 553, 2013.]
  10. S. Lenaghan, Y. Li (co-first author), H. Zhang, J. Burris, C. Stewart, L. E. Parker, and M. Zhang, Monitoring the Environmental Impact of TiO2 Nanoparticles Using a Plant-based Sensor-network, IEEE Transactions on Nanotechnology, 2(2), 182-189, 2013. [Abstract S. Lenaghan, Y. Li (co-first author), H. Zhang, J. Burris, C. Stewart, L. E. Parker, and M. Zhang, Monitoring the Environmental Impact of TiO2 Nanoparticles Using a Plant-based Sensor-network, IEEE Transactions on Nanotechnology, 2(2), 182-189, 2013.]
  11. Y. Li, S. Lenaghan, and M. Zhang. A Data-driven Predictive Approach for Drug Delivery Using Machine Learning Techniques, PLoS ONE, 7(2): e31724, 2012. [Abstract Y. Li, S. Lenaghan, and M. Zhang. A Data-driven Predictive Approach for Drug Delivery Using Machine Learning Techniques, PLoS ONE, 7(2): e31724, 2012.]
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