Browsing by Author "Yao, Tianyi"
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Item Quantifying cognitive resilience in Alzheimer’s Disease: The Alzheimer’s Disease Cognitive Resilience Score(Public Library of Science, 2020) Yao, Tianyi; Sweeney, Elizabeth; Nagorski, John; Shulman, Joshua M.; Allen, Genevera I.Even though there is a clear link between Alzheimer’s Disease (AD) related neuropathology and cognitive decline, numerous studies have observed that healthy cognition can exist in the presence of extensive AD pathology, a phenomenon sometimes called Cognitive Resilience (CR). To better understand and study CR, we develop the Alzheimer’s Disease Cognitive Resilience Score (AD-CR Score), which we define as the difference between the observed and expected cognition given the observed level of AD pathology. Unlike other definitions of CR, our AD-CR Score is a fully non-parametric, stand-alone, individual-level quantification of CR that is derived independently of other factors or proxy variables. Using data from two ongoing, longitudinal cohort studies of aging, the Religious Orders Study (ROS) and the Rush Memory and Aging Project (MAP), we validate our AD-CR Score by showing strong associations with known factors related to CR such as baseline and longitudinal cognition, non AD-related pathology, education, personality, APOE, parkinsonism, depression, and life activities. Even though the proposed AD-CR Score cannot be directly calculated during an individual’s lifetime because it uses postmortem pathology, we also develop a machine learning framework that achieves promising results in terms of predicting whether an individual will have an extremely high or low AD-CR Score using only measures available during the lifetime. Given this, our AD-CR Score can be used for further investigations into mechanisms of CR, and potentially for subject stratification prior to clinical trials of personalized therapies.Item Selective Transparent Headphone(Rice University, 2014-12-18) Wang, Jack; Yao, Tianyi; Xia, StephenDescribes the theory and process of creating a selective transparent headphone, which filters in important sound signals from the environment to the user while suppressing all other signals. The article focuses on a small step of the process: isolating human speech.Item Statistical Machine Learning Methodology for Feature Selection, Structured Data, and Graphical Model Selection(2022-04-07) Yao, Tianyi; Allen, Genevera IWith the rapidly increasing richness and volume of modern data sets, finding important structure, whether informative features, relationships between entities, or group patterns, is crucial for making data-driven discoveries in many domains such as genetics and neuroscience. In this thesis, I develop three methodologies for tackling these problems. The first project considers feature selection. While many feature selection techniques have been proposed, there are typically two key challenges in practice: computational intractability in huge-data settings and deteriorating statistical accuracy of selected features in high-dimensional, high-correlation scenarios. I tackle these issues by developing Stable Minipatch Selection (STAMPS) and AdaSTAMPS. These are meta-algorithms that build ensembles of selection events of base feature selectors trained on many tiny, random or adaptively-chosen subsets of both the observations and features of the data, termed minipatches. Through extensive empirical experiments, I demonstrate that my approaches, especially AdaSTAMPS, achieve superior performance in terms of feature selection accuracy and computational time in challenging high-dimensional, high-correlation settings. The second project considers estimating the structure of Gaussian graphical models, which are powerful statistical approaches for studying conditional dependence relationships between nodes. Despite recent advancements, conducting graphical model selection on data with a huge number of nodes still poses great computational and statistical challenges in practice. I develop a highly scalable computational approach to Gaussian graphical model selection named Minipatch Graph (MPGraph) that ensembles thresholded graph estimators trained on many tiny, random minipatches. I demonstrate the efficacy of MPGraph through extensive empirical studies, showing that it not only yields more accurate graph estimation, but also achieves extensive speed improvement over existing techniques for huge data. The third project considers the problem of uncovering the functional groupings of large neuronal populations from neuronal activity data, which can lead to a better understanding of structures of interconnected neural circuits and thus the operating mechanisms of the brain. The Clustered Gaussian Graphical Model with a novel symmetric convex clustering penalty is developed for finding functionally coherent groups in a data-driven manner. All three methodologies can aid in discoveries of useful structure from large data sets in many applications.Item Supervised convex clustering(Wiley, 2023) Wang, Minjie; Yao, Tianyi; Allen, Genevera I.Clustering has long been a popular unsupervised learning approach to identify groups of similar objects and discover patterns from unlabeled data in many applications. Yet, coming up with meaningful interpretations of the estimated clusters has often been challenging precisely due to their unsupervised nature. Meanwhile, in many real-world scenarios, there are some noisy supervising auxiliary variables, for instance, subjective diagnostic opinions, that are related to the observed heterogeneity of the unlabeled data. By leveraging information from both supervising auxiliary variables and unlabeled data, we seek to uncover more scientifically interpretable group structures that may be hidden by completely unsupervised analyses. In this work, we propose and develop a new statistical pattern discovery method named supervised convex clustering (SCC) that borrows strength from both information sources and guides towards finding more interpretable patterns via a joint convex fusion penalty. We develop several extensions of SCC to integrate different types of supervising auxiliary variables, to adjust for additional covariates, and to find biclusters. We demonstrate the practical advantages of SCC through simulations and a case study on Alzheimer's disease genomics. Specifically, we discover new candidate genes as well as new subtypes of Alzheimer's disease that can potentially lead to better understanding of the underlying genetic mechanisms responsible for the observed heterogeneity of cognitive decline in older adults.