Publications

(2022). GenomicKB: a knowledge graph for the human genome. Nucleic Acids Res.

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(2021). 3′HS1 CTCF binding site in human β-globin locus regulates fetal hemoglobin expression. eLife.

DOI

(2021). scHiCTools: a computational toolbox for analyzing single-cell Hi-C data. PLoS Comput. Biol.

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(2021). Mapping the regulatory landscape of auditory hair cells from single-cell multi-omics data. Genome Res..

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(2020). Capturing cell type-specific chromatin structural patterns by applying topic modeling to single-cell Hi-C data. PLoS Comput. Biol..

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(2020). p53 Integrates Temporal WDR5 Inputs during Neuroectoderm and Mesoderm Differentiation of Mouse Embryonic Stem Cells. Cell Rep..

DOI

(2019). Jointly embedding multiple single-cell omics measurements. WABI.

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(2019). Systematic proteomics of endogenous human cohesin reveals an interaction with diverse splicing factors and RNA binding proteins required for mitotic progression. In Journal of Biological Chemistry.

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(2018). Unsupervised embedding of single-cell Hi-C data. In ISMB.

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(2017). Comprehensive statistical inference of the clonal structure of cancer from multiple biopsies. Sci. Rep..

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(2016). Multiple Testing under Dependence via Graphical Models. Ann. Appl. Stat.

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(2016). Structure-leveraged Methods in Breast Cancer Risk Prediction. JMLR.

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(2016). Discriminatory power of common genetic variants in personalized breast cancer diagnosis. In SPIE Medical Imaging conference.

Preprint

(2016). Comparing Mammography Abnormality Features to Genetic Variants in the Prediction of Breast Cancer in Women Recommended for Breast Biopsy. Acad. Radiol..

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(2015). Machine Learning for Treatment Assignment: Improving Individualized Risk Attribution. AMIA Symposium.

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(2015). Developing a Utility Decision Framework to Evaluate Predictive Models in Breast Cancer Risk Estimation. J. Med. Imaging.

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(2015). Machine Learning for Treatment Assignment: Improving Individualized Risk Attribution. AMIA Joint Summits.

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(2015). Developing a Clinical Utility Framework to Evaluate Prediction Models in Radiogenomics. In SPIE Medical Imaging conference.

Preprint

(2014). Comparing the Value of Mammographic Features and Genetic Variants in Breast Cancer Risk Prediction. AMIA Symposium.

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(2014). New Genetic Variants Improve Personalized Breast Cancer Diagnosis. AMIA-TBI.

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(2014). Learning Heterogeneous Hidden Markov Random Fields. AISTATS.

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(2013). Genetic Variants Improve Breast Cancer Risk Prediction on Mammograms. AMIA Symposium.

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(2012). High-Dimensional Structured Feature Screening Using Binary Markov Random Fields. AISTAT.

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(2012). A Collective Ranking Method for Genome-wide Association Studies. ACM-BCB.

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(2009). Cross-Sectional Stock Return Analysis Using Support Vector Regression. Appl. Econ. Lett..

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(2008). Meta-prediction of Phosphorylation Sites with Weighted Voting and Restricted Grid Search Parameter Selection. Nucleic Acids Res..

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(2007). Meta-prediction of Protein Subcellular Localization with Reduced Voting. Nucleic Acids Res..

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