Tsung-Hung Yao

Machine learning-based multimodal biomarkers enable accurate diagnosis and early detection of pancreatic ductal adenocarcinoma

Tsung-Hung Yao, Warapen Treekitkarnmongkol, Nagireddy Putluri, Deivendran Sankaran et autres

While there has been some progress on discovering clinically validated biomarkers for early detection in pancreatic ductal adenocarcinoma (PDAC), several challenges remain. Most approaches rely on single-modality biomarkers with limited sensitivity and/or specificity. Using data from a multicenter study with an age-matched …

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Estrogen prescriptions for women with young-onset rectal cancer after pelvic radiation: a retrospective cohort study

Angelica Arzola, Abigail Kohut-Jackson, Tsung-Hung Yao, Kelsey L. Corrigan et autres

Aim The incidence of young-onset rectal cancer (YORC) is increasing. Treatment often includes pelvic radiation, which can affect bone and vaginal health and induce menopause in women. Estrogen can treat vasomotor symptoms of menopause, as well as support bone and vaginal health. …

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Robust Bayesian graphical regression models for assessing tumor heterogeneity in proteomic networks

Tsung-Hung Yao, Yang Ni, Anindya Bhadra, Jian Kang et autres

Graphical models are powerful tools to investigate complex dependency structures in high-throughput datasets. However, most existing graphical models make one of two canonical assumptions: (i) a homogeneous graph with a common network for all subjects or (ii) an assumption of normality, especially …

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Flexible Bayesian Nonparametric Product Mixtures for Multi-scale Functional Clustering

Tsung-Hung Yao, Suprateek Kundu

There is a rich literature on clustering functional data with applications to time-series modeling, trajectory data, and even spatio-temporal applications. However, existing methods routinely perform global clustering that enforces identical atom values within the same cluster. Such grouping may be inadequate for …

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Geometry-driven Bayesian Inference for Ultrametric Covariance Matrices

Tsung-Hung Yao, Zhenke Wu, Karthik Bharath, Veerabhadran Baladandayuthapani

Ultrametric matrices are a class of covariance matrices that arise in latent tree models. As a parameter space in a statistical model, the set of ultrametric matrices is neither convex nor a smooth manifold. Focus in the literature has hitherto been restricted …

Robust Bayesian Graphical Regression Models for Assessing Tumor Heterogeneity in Proteomic Networks

Tsung-Hung Yao, Yang Ni, Anindya Bhadra, Jian Kang et autres

Graphical models are powerful tools to investigate complex dependency structures in high-throughput datasets. However, most existing graphical models make one of the two canonical assumptions: (i) a homogeneous graph with a common network for all subjects; or (ii) an assumption of normality …

Probabilistic learning of treatment trees in cancer

Tsung-Hung Yao, Zhenke Wu, Karthik Bharath, Jinju Li et autres

Accurate identification of synergistic treatment combinations and their underlying biological mechanisms is critical across many disease domains, especially cancer. In translational oncology research, preclinical systems, such as patient-derived xenografts (PDX), have emerged as a unique study design evaluating multiple treatments administered to …

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Bayesian Learning of Structured Covariances, with Applications to Cancer Data

Tsung-Hung Yao

The identification of scientifically-driven dependence structures is of interest across many biomedical domains. Examples include tree- and graph-based structures manifesting themselves in precision medicine and genomic contexts. Such dependence structures can be compactly represented as covariance or precision matrices, which are useful …

Probabilistic Learning of Treatment Trees in Cancer

Tsung-Hung Yao, Zhenke Wu, Karthik Bharath, Jinju Li et autres

A bstract Accurate identification of synergistic treatment combinations and their underlying biological mechnisms is critical across many disease domains, especially cancer. In translational oncology research, preclinical systems such as patient-derived xenografts (PDX) have emerged as a unique study design evaluating multiple treatments …

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Probabilistic Learning of Treatment Trees in Cancer

Tsung-Hung Yao, Zhenke Wu, Karthik Bharath, Jinju Li et autres

Accurate identification of synergistic treatment combinations and their underlying biological mechanisms is critical across many disease domains, especially cancer. In translational oncology research, preclinical systems such as patient-derived xenografts (PDX) have emerged as a unique study design evaluating multiple treatments administered to …