This dissertation studies prediction and classification models for disease risk through semiparametric modeling and statistical learning. It consists of three parts. In the first part, we propose several survival models to analyze the Cooperative Huntington's Observational Research Trial (COHORT) study data accounting for the missing mutation status in relative participants (Kieburtz and Huntington Study Group, 1996a). Huntington's disease (HD) is a progressive neurodegenerative disorder caused by an expansion of cytosine-adenine-guanine (CAG) repeats at the IT15 gene. A CAG repeat number greater than or equal to 36 is defined as carrying the mutation and carriers will eventually show symptoms if not censored by other events. There is an inverse relationship between the age-at-onset of HD and the CAG repeat length; the greater the CAG expansion, the earlier the age-at-onset. Accurate estimation of age-at-onset based on CAG repeat length is important for genetic counseling and the design of clinical trials for HD. Participants in COHORT (denoted as probands) undergo a genetic test and their CAG repeat number is determined. Family members of the probands do not undergo the genetic test and their HD onset information is provided by probands. Several methods are proposed in the literature to model the age specific cumulative distribution function (CDF) of HD onset as a function of the CAG repeat length. However, none of the existing methods can be directly used to analyze COHORT proband and family data because family members' mutation status is not always known. In this work, we treat the presence or absence of an expanded CAG repeat in first-degree family members as missing data and use the expectation-maximization (EM) algorithm to carry out the maximum likelihood estimation of the COHORT proband and family data jointly. We perform simulation studies to examine finite sample performance of the proposed methods and apply these methods to estimate the CDF of HD age-at-onset from the COHORT proband and family combined data. Our results show a slightly lower estimated cumulative risk of HD with the combined data compared to using proband data alone. We then extend the approach to predict the cumulative risk of disease accommodating predictors with time-varying effects and outcomes subject to censoring. We model the time-specific effect through a nonparametric varying-coefficient function and handle censoring through self-consistency equations that redistribute the probability mass of censored outcomes to the right. The computational procedure is extremely convenient and can be implemented by standard software. We prove large sample properties of the proposed estimator and evaluate its finite sample performance through simulation studies. We apply the method to estimate the cumulative risk of developing HD from the mutation carriers in COHORT data and illustrate an inverse relationship between the cumulative risk of HD and the length of CAG repeats at the IT15 gene. In the second part of the dissertation, we develop methods to accurately predict whether pre-symptomatic individuals are at risk of a disease based on their various marker profiles, which offers an opportunity for early intervention well before definitive clinical diagnosis. For many diseases, existing clinical literature may suggest the risk of disease varies with some markers of biological and etiological importance, for example age. To identify effective prediction rules using nonparametric decision functions, standard statistical learning approaches treat markers with clear biological importance (e.g., age) and other markers without prior knowledge on disease etiology interchangeably as input variables. Therefore, these approaches may be inadequate in singling out and preserving the effects from the biologically important variables, especially in the presence of potential noise markers. Using age as an example of a salient marker to receive special care in the analysis, we propose a local smoothing large margin classifier implemented with support vector machine to construct effective age-dependent classification rules. The method adaptively adjusts age effect and separately tunes age and other markers to achieve optimal performance. We derive the asymptotic risk bound of the local smoothing support vector machine, and perform extensive simulation studies to compare with standard approaches. We apply the proposed method to two studies of premanifest HD subjects and controls to construct age-sensitive predictive scores for the risk of HD and risk of receiving HD diagnosis during the study period. In the third part of the dissertation, we develop a novel statistical learning method for longitudinal data. Predicting disease risk and progression is one of the main goals in many clinical studies. Cohort studies on the natural history and etiology of chronic diseases span years and data are collected at multiple visits. Although kernel-based statistical learning methods are proven to be powerful for a wide range of disease prediction problems, these methods are only well studied for independent data but not for longitudinal data. It is thus important to develop time-sensitive prediction rules that make use of the longitudinal nature of the data. We develop a statistical learning method for longitudinal data by introducing subject-specific long-term and short-term latent effects through designed kernels to account for within-subject correlation of longitudinal measurements. Since the presence of multiple sources of data is increasingly common, we embed our method in a multiple kernel learning framework and propose a regularized multiple kernel statistical learning with random effects to construct effective nonparametric prediction rules. Our method allows easy integration of various heterogeneous data sources and takes advantage of correlation among longitudinal measures to increase prediction power. We use different kernels for each data source taking advantage of distinctive feature of data modality, and then optimally combine data across modalities. We apply the developed methods to two large epidemiological studies, one on Huntington's disease and the other on Alzhemeier's Disease (Alzhemeier's Disease Neuroimaging Initiative, ADNI) where we explore a unique opportunity to combine imaging and genetic data to predict the conversion from mild cognitive impairment to dementia, and show a substantial gain in performance while accounting for the longitudinal feature of data.

Hung Shieh-Jung Fanny. / Thesis submitted in: August 2002. / Thesis (M.Phil.)--Chinese University of Hong Kong, 2004. / Includes bibliographical references (leaves 97-109). / Abstracts in English and Chinese. / Acknowledgements --- p.i / Abstract --- p.ii / 摘要 --- p.iv / Content --- p.vi / List of Abbreviations --- p.xiii / List of Figure --- p.xv / List of Tables --- p.xix / Chapter Chapter 1 --- Introduction --- p.1 / Chapter 1.1 --- Alzheimer's Disease (AD) --- p.1 / Chapter 1.1.1 --- Clinical features --- p.2 / Chapter 1.2 --- Histopathological studies of AD --- p.2 / Chapter 1.2.1 --- Neuritic plaques --- p.2 / Chapter 1.2.2 --- Neurofibrillary tangles (NFT) --- p.4 / Chapter 1.2.3 --- Tau --- p.5 / Chapter 1.3 --- Kinases and Alzheimer's Disease --- p.7 / Chapter 1.4 --- Free radical damage --- p.8 / Chapter 1.5 --- Available treatment for AD --- p.7 / Chapter 1.6 --- A Chinese medicinal material 一 Danshen （（Salviae miltiorrhizcie) --- p.11 / Chapter 1.6.1 --- Chemical constituents --- p.11 / Chapter 1.6.1.1 --- Lipophilic Compounds of Danshen --- p.12 / Chapter 1.6.1.2 --- Water-soluble Compounds of Danshen --- p.17 / Chapter 1.6.2 --- Pharmacological usage --- p.20 / Chapter 1.6.2.1 --- Action on Coronary system --- p.20 / Chapter 1.6.2.2 --- Bacteriostatic action --- p.21 / Chapter 1.6.2.3 --- Actions on the immune system --- p.21 / Chapter 1.6.3 --- Biological activity on brain --- p.22 / Chapter 1.7 --- Objectives and scope of the project --- p.23 / Chapter Chapter 2 --- General Materials and Method --- p.24 / Chapter 2.1 --- Recombinant DNA techniques --- p.24 / Chapter 2.1.1 --- Preparation of E. coli strain DH-5a competent cells --- p.24 / Chapter 2.1.2 --- Transformation of plasmid DNA into competent cells --- p.25 / Chapter 2.1.3 --- Preparation of plasmid DNA using QIAGEN Plasmid Maxipreps kit --- p.25 / Chapter 2.1.4 --- Phenol/ choroform extraction of DNA --- p.26 / Chapter 2.1.5 --- Spectrophotometric quantitation of the amount and purity of DNA --- p.27 / Chapter 2.2 --- Drugs preparation --- p.27 / Chapter 2.2.1 --- Preparation of aqueous extracts of Traditional Chinese Medicine (TCM) --- p.27 / Chapter 2.2.2 --- Preparation of ethanol extracts of Traditional Chinese Medicine (TCM) --- p.27 / Chapter 2.3 --- "3-(4,5-dimethylthiazol-2-yl)-2, 5-diphenyl-tetrazolium (MTT) assay " --- p.28 / Chapter 2.4 --- Analysis of proteins from culture cells --- p.28 / Chapter 2.4.1 --- Extraction of total proteins from culture cells --- p.28 / Chapter 2.4.2 --- Quantitation of protein by Bradford method --- p.29 / Chapter 2.4.3 --- Protein separation by sodium dodecylsulphate polyacrylamide gel electrophoresis (SDS-PAGE) --- p.29 / Chapter 2.4.4 --- Western blot analysis --- p.31 / Chapter 2.5 --- Reagents and buffers --- p.32 / Chapter 2.5.1 --- Reagents for competent cell preparation --- p.32 / Chapter 2.5.2 --- Reagents provided by QIAGEN Plasmid Maxipreps kit --- p.33 / Chapter 2.5.3 --- Reagents for SDS-PAGE --- p.34 / Chapter 2.5.4 --- Reagents and buffers for Western Blotting --- p.35 / Chapter 2.5.5 --- Cell lines --- p.36 / Chapter 2.5.6 --- Antibodies --- p.37 / Chapter 2.5.7 --- Plasmids --- p.37 / Chapter 2.5.8 --- Other Chemicals --- p.38 / Chapter Chapter 3 --- The effect of Danshen on GSK-3 induced hyperposphorylation of tau in Cos7 cells / Chapter 3.1 --- Introduction --- p.39 / Chapter 3.1.1 --- Glycogen synthase kinase-3 (GSK-3) --- p.39 / Chapter 3.1.2 --- Structure of GSK-3 --- p.39 / Chapter 3.1.3 --- The importance of GSK-3 in AD --- p.39 / Chapter 3.2 --- Materials and Methods --- p.41 / Chapter 3.2.1 --- Transfection of Gsk-3 and tau into Cos7 monkey kidney cells --- p.43 / Chapter 3.2.2 --- Extraction of total proteins from culture cells --- p.44 / Chapter 3.2.3 --- Quantitation of protein by Bradford method --- p.44 / Chapter 3.2.4 --- Protein separation by sodium dodecylsulphate polyacrylamide gel electrophoresis (SDS-PAGE) --- p.44 / Chapter 3.2.5 --- Western blot analysis --- p.44 / Chapter 3.3 --- Results --- p.45 / Chapter 3.3.1 --- Toxicity test on Cos7 cells --- p.45 / Chapter 3.3.2 --- The effect of ethanol extract of Danshen on GSK-3 β induced tau phosphorylation --- p.45 / Chapter 3.3.3 --- The effect of aqueous extract of Danshen on GSK-3 β induced tau phosphorylation --- p.48 / Chapter 3.3.4 --- The effect of Protocatechualdehyde on GSK-3β induced tau phosphorylation --- p.48 / Chapter 3.3.5 --- The effect of Salvianolic acid B on GSK-3β induced tau phosphorylation --- p.49 / Chapter 3.4 --- Discussion --- p.60 / Chapter Chapter 4 --- Cdk5 induced hyperposphorylation of tau in CHO cells / Chapter 4.1 --- Introduction --- p.63 / Chapter 4.1.1 --- Cyclin dependent kinase 5 (Cdk5) --- p.63 / Chapter 4.1.2 --- Structure of Cdk5 --- p.63 / Chapter 4.1.3 --- Neurological functions of Cdk5 --- p.64 / Chapter 4.2 --- Materials and Methods --- p.66 / Chapter 4.2.1 --- Transfection of p35 and tau into CHO cells --- p.66 / Chapter 4.2.2 --- Extraction of total proteins from culture cells --- p.67 / Chapter 4.2.3 --- Quantitation of protein by Bradford method --- p.67 / Chapter 4.2.4 --- Protein separation by sodium dodecylsulphate polyacrylamide gel electrophoresis (SDS-PAGE) --- p.67 / Chapter 4.2.5 --- Western blot analysis --- p.67 / Chapter 4.3 --- Results --- p.68 / Chapter 4.3.1 --- Toxicity test on CHO cells --- p.68 / Chapter 4.3.2 --- Tau transfection in Cdk5/p35 and TauON3R transiently transfected in CHO cells --- p.68 / Chapter 4.3.3 --- Effect of roscovitine treatment on the transiently tau and p35 transfection in CHO cells --- p.74 / Chapter 4.3.4 --- "Effects of aqueous active components of Danshen, PCAH and SAB on the transiently tau and p35 transfection in CHO cells " --- p.74 / Chapter 4.4 --- Discussion --- p.79 / Chapter Chapter 5 --- Antioxidant effect of Danshen and its active components on lipid peroxidation / Chapter 5.1 --- Introduction --- p.81 / Chapter 5.1.1 --- Red-blood-cell hemolysis model --- p.82 / Chapter 5.2 --- Materials and methods --- p.84 / Chapter 5.2.1 --- Red-blood-cell hemolysis model --- p.84 / Chapter 5.2.2 --- Materials --- p.85 / Chapter 5.2.2.1 --- Animals --- p.85 / Chapter 5.2.2.2 --- Chemicals --- p.85 / Chapter 5.3 --- Results --- p.86 / Chapter 5.3.1 --- Aqueous and ethanol extracts of Danshen --- p.86 / Chapter 5.3.2 --- Active components ´ؤ Protocatechualdehyde and Salvianolic acid B --- p.87 / Chapter 5.4 --- Discussion --- p.91 / Chapter Chapter 6 --- General discussion and Outlook / Chapter 6.1 --- General discussion --- p.93 / Chapter 6.2 --- Proposed study in the future --- p.95 / Chapter 6.2.1 --- In vitro kinase assay using gamma32 P ATP and substrate with or without TCM --- p.95 / Chapter 6.2.2 --- Use of neuroblastoma cells (SHSY-5Y) to study the effect of Danshen and its active components on tau phosphorylation --- p.95 / Chapter 6.2.3 --- Thiobarbituric acid-reacting substances (TBARS) assay --- p.96 / Chapter 6.2.4 --- In vitro phosphatase kinase assay --- p.96

by Tsz-Wan Ho. / Thesis submitted in: July 2003. / Thesis (M.Phil.)--Chinese University of Hong Kong, 2004. / Includes bibliographical references (leaves 107-122). / Abstracts in English and Chinese. / Acknowledgements --- p.i / Abstract --- p.ii / 摘要 --- p.iv / Content --- p.vi / Abbreviations --- p.x / List of Figures --- p.xi / List of tables --- p.xiv / Chapter CHAPTER 1 --- GENERAL INTRODUCTION --- p.1 / Chapter 1.1 --- Alzheimer'sDisease --- p.1 / Chapter 1.2 --- Hallmarks of AD --- p.3 / Chapter 1.2.1 --- The amyloid cascade hypothesis --- p.3 / Chapter 1.2.2 --- The tauopathy hypothesis --- p.4 / Chapter 1.3 --- The Cholinergic Hypothesis --- p.6 / Chapter 1.3.1 --- Cholinergic drug therapy --- p.7 / Chapter 1.3.2 --- Acetylcholinesterase inhibitors --- p.8 / Chapter 1.3.2.1 --- Tacrine --- p.10 / Chapter 1.3.2.2 --- Donepezil --- p.10 / Chapter 1.3.2.3 --- Rivastigimine - ENA-713 --- p.11 / Chapter 1.4 --- AChE inhibitors from plants --- p.12 / Chapter 1.4.1 --- Galanthamine --- p.12 / Chapter 1.4.2 --- Huperzine --- p.14 / Chapter 1.4.3 --- α-onocerin --- p.15 / Chapter 1.4.4 --- (+)-alpha-viniferin --- p.16 / Chapter 1.5 --- My project --- p.17 / Chapter CHAPTER 2 --- MATERIALS AND METHODS --- p.18 / Chapter 2.1 --- Preparation of CMM --- p.18 / Chapter 2.2.1 --- Selecting criteria and sources --- p.18 / Chapter 2.2.2 --- Preparation of aqueous extract --- p.18 / Chapter 2.2.3 --- Preparation of ethanol extract --- p.18 / Chapter 2.3 --- Routine maintenance of cell lines --- p.19 / Chapter 2.4 --- Toxicity test --- p.19 / Chapter 2.5 --- Ellman assay --- p.20 / Chapter 2.6 --- Ellman assay over BuChE --- p.21 / Chapter 2.7 --- Drugs --- p.21 / Chapter CHAPTER 3 --- SCREENING OF ACETYLCHOLINESTERASE INHIBITORS FROM CHINESE MEDICINAL MATERIALS --- p.23 / Chapter 3.1 --- Introduction --- p.23 / Chapter 3.2 --- Materials and Methods --- p.23 / Chapter 3.3 --- Results and discussion --- p.24 / Chapter 3.3.1 --- Preliminary screening of 45 selected TCMs for AChE inhibition --- p.24 / Chapter 3.3.2 --- Rescreening of drugs that show AChE inhibition in both aqueous and organic extracts --- p.25 / Chapter 3.4 --- Discussion --- p.28 / Chapter CHAPTER 4 --- CHARACTERIZATION OF ANTI-ACETYLCHOLINESTERASE ACTIVITY FROM SALVIA MBLTIORRHIZA BGE.(丹參) --- p.33 / Chapter 4.1 --- Introduction --- p.33 / Chapter 4.1.1 --- Clinical application of Danshen --- p.34 / Chapter 4.1.2 --- Pharmacological properties of Danshen and Salvia species --- p.34 / Chapter 4.1.2.1. --- Antiinflammatory and antibacterial responses --- p.35 / Chapter 4.1.2.2 --- Diabetes --- p.35 / Chapter 4.1.2.3 --- Alcoholism --- p.35 / Chapter 4.1.2.4 --- Apoptosis --- p.36 / Chapter 4.1.2.5 --- The effect of Salvia extracts on neuro-receptors --- p.36 / Chapter 4.1.3 --- Anti-cholinesterase activity by the Salvia species --- p.37 / Chapter 4.1.4 --- Active components from Salvia miltiorrhiza Bge --- p.38 / Chapter 4.2 --- Effects of tanshinone derivatives on AChE --- p.39 / Chapter 4.2.1 --- Materials and Methods --- p.39 / Chapter 4.2.2. --- Results --- p.39 / Chapter 4.3 --- Discussion --- p.50 / Chapter CHAPTER 5 --- EXTRACTION OF CRYPTOTANSHINONE FROM SALVIA MILTIORRHIZA --- p.54 / Chapter 5.1 --- Introduction --- p.54 / Chapter 5.1.1 --- Reverse phase high performance liquid chromatography (RP-HPLC) --- p.55 / Chapter 5.2 --- Materials and Methods --- p.56 / Chapter 5.2.1 --- Extracts of Danshen from different sources for obtaining the chemical profile --- p.55 / Chapter 5.2.2 --- Reverse phase high performance liquid chromatography (RP-HPLC) --- p.57 / Chapter 5.2.2.1 --- Analytical RP-HPLC --- p.57 / Chapter 5.2.2.2 --- Preparative RP-HPLC --- p.58 / Chapter 5.3 --- Results --- p.60 / Chapter 5.3.1 --- Identification of Peaks that contain the proposed active components --- p.60 / Chapter 5.3.2 --- Different samples of Danshen contain different amount of active components that can exert inhibitory effect on hAChE --- p.66 / Chapter 5.4 --- Discussion --- p.75 / Chapter CHAPTER 6 --- EFFECT OF CRYPTOTANSHINONE ON CALCIUM MOVEMENT in SH-SY5Y Cell --- p.80 / Chapter 6.1 --- Introduction --- p.80 / Chapter 6.2 --- Materials and Methods --- p.82 / Chapter 6.2.1 --- Reagents and drugs --- p.82 / Chapter 6.2.2 --- Calcium fluorimetry --- p.82 / Chapter 6.3 --- Results --- p.85 / Chapter 6.4 --- Discussion --- p.96 / Chapter CHAPTER 7 --- GENERAL DISCUSSION --- p.98 / Chapter 7.1 --- Structure-function relationship of crytotanshinone and dihydrotanshinone I --- p.98 / Chapter 7.2 --- Further study on cryptotanshinone and dihydrotanshinone I --- p.100 / Chapter 7.2.1 --- Modulation on nictonic receptor --- p.100 / Chapter 7.2.2 --- Behavioral study on mice --- p.101 / Chapter 7.2.3 --- Large scale production of the desired active components --- p.102 / Chapter 7.3 --- Study on other candidate herbs --- p.102 / References --- p.107

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This doctoral thesis outlines several methodological advances in network science aimed towards uncovering rapid, complex interdependencies of electromagnetic brain activity recorded from the Electroencephalogram (EEG). This entails both new analyses and modelling of EEG brain network topologies and a novel approach to analyse rapid dynamics of connectivity. Importantly, we implement these advances to provide novel insights into pathological brain function in Alzheimer's disease. We introduce the concept of hierarchical complexity of network topology, providing both an index to measure it and a model to simulate it. We then show that the topology of functional connectivity estimated from EEG recordings is hierarchically complex, existing in a scale between random and star-like topologies, this is a paradigm shift from the established understanding that complexity arises between random and regular topologies. We go on to consider the density appropriate for binarisation of EEG functional connectivity, a methodological step recommended to produce compact and unbiased networks, in light of its new-found hierarchical complexity. Through simulations and real EEG data, we show the benefit of going beyond often recommended sparse representations to account for a broader range of hierarchy level interactions. After this, we turn our attention to assessing dynamic changes in connectivity. By constructing a unified framework for multivariate signals and graphs, inspired by network science and graph signal processing, we introduce graph-variate signal analysis which allows us to capture rapid fluctuations in connectivity robust to spurious short-term correlations. We define this for three pertinent brain connectivity estimates - Pearson's correlation coefficient, coherence and phase-lag index - and show its benefit over standard dynamic connectivity measures in a range of simulations and real data. Applying these novel methods to EEG datasets of the performance of visual short-term memory binding tasks by familial and sporadic Alzheimer's disease patients, we uncover disorganisation of the topological hierarchy of EEG brain function and abnormalities of transient phase-based activity which paves the way for new interpretations of the disease's affect on brain function. Hierarchical complexity and graph-variate dynamic connectivity are entirely new methods for analysing EEG brain networks. The former provides new interpretations of complexity in static connectivity patterns while the latter enables robust analysis of transient temporal connectivity patterns, both at the frontiers of analysis. Although designed with EEG functional connectivity in mind, we hope these techniques will be picked up in the broader field, having consequences for research into complex networks in general.