New algorithms for automated phylogenetic reconstruction using artificial intelligence and data mining techniques

Academic and industrial research in the late 90s have brought about an exponential explosion of DNA sequence data. Automated expert systems are being created to help biologists to extract patterns, trends and links from this ever-deepening ocean of information. Two such systems aimed on retrieving and subsequently utilizing phylogenetically relevant information have been developed in this dissertation, the major objective of which was to automate the often difficult and confusing phylogenetic reconstruction process. ^ Popular phylogenetic reconstruction methods, such as distance-based methods, attempt to find an optimal tree topology (that reflects the relationships among related sequences and their evolutionary history) by searching through the topology space. Various compromises between the fast (but incomplete) and exhaustive (but computationally prohibitive) search heuristics have been suggested. An intelligent compromise algorithm that relies on a flexible “beam” search principle from the Artificial Intelligence domain and uses the pre-computed local topology reliability information to adjust the beam search space continuously is described in the second chapter of this dissertation. ^ However, sometimes even a (virtually) complete distance-based method is inferior to the significantly more elaborate (and computationally expensive) maximum likelihood (ML) method. In fact, depending on the nature of the sequence data in question either method might prove to be superior. Therefore, it is difficult (even for an expert) to tell a priori which phylogenetic reconstruction method—distance-based, ML or maybe maximum parsimony (MP)—should be chosen for any particular data set. ^ A number of factors, often hidden, influence the performance of a method. For example, it is generally understood that for a phylogenetically “difficult” data set more sophisticated methods (e.g., ML) tend to be more effective and thus should be chosen. However, it is the interplay of many factors that one needs to consider in order to avoid choosing an inferior method (potentially a costly mistake, both in terms of computational expenses and in terms of reconstruction accuracy.) ^ Chapter III of this dissertation details a phylogenetic reconstruction expert system that selects a superior proper method automatically. It uses a classifier (a Decision Tree-inducing algorithm) to map a new data set to the proper phylogenetic reconstruction method. ^

Functional data analysis approaches for genotype-phenotype association studies from next-generation sequencing

Next-generation DNA sequencing platforms can effectively detect the entire spectrum of genomic variation and is emerging to be a major tool for systematic exploration of the universe of variants and interactions in the entire genome. However, the data produced by next-generation sequencing technologies will suffer from three basic problems: sequence errors, assembly errors, and missing data. Current statistical methods for genetic analysis are well suited for detecting the association of common variants, but are less suitable to rare variants. This raises great challenge for sequence-based genetic studies of complex diseases.^ This research dissertation utilized genome continuum model as a general principle, and stochastic calculus and functional data analysis as tools for developing novel and powerful statistical methods for next generation of association studies of both qualitative and quantitative traits in the context of sequencing data, which finally lead to shifting the paradigm of association analysis from the current locus-by-locus analysis to collectively analyzing genome regions.^ In this project, the functional principal component (FPC) methods coupled with high-dimensional data reduction techniques will be used to develop novel and powerful methods for testing the associations of the entire spectrum of genetic variation within a segment of genome or a gene regardless of whether the variants are common or rare.^ The classical quantitative genetics suffer from high type I error rates and low power for rare variants. To overcome these limitations for resequencing data, this project used functional linear models with scalar response to develop statistics for identifying quantitative trait loci (QTLs) for both common and rare variants. To illustrate their applications, the functional linear models were applied to five quantitative traits in Framingham heart studies. ^ This project proposed a novel concept of gene-gene co-association in which a gene or a genomic region is taken as a unit of association analysis and used stochastic calculus to develop a unified framework for testing the association of multiple genes or genomic regions for both common and rare alleles. The proposed methods were applied to gene-gene co-association analysis of psoriasis in two independent GWAS datasets which led to discovery of networks significantly associated with psoriasis.^

A comparative analysis of CHIP Perinatal policy in twelve states

The purpose of this comparative analysis of CHIP Perinatal policy (42 CFR § 457) was to provide a basis for understanding the variation in policy outputs across the twelve states that, as of June 2007, implemented the Unborn Child rule. This Department of Health and Human Services regulation expanded in 2002 the definition of “child” to include the period from conception to birth, allowing states to consider an unborn child a “targeted low-income child” and therefore eligible for SCHIP coverage. ^ Specific study aims were to (1) describe typologically the structural and contextual features of the twelve states that adopted a CHIP Perinatal policy; (2) describe and differentiate among the various designs of CHIP Perinatal policy implemented in the states; and (3) develop a conceptual model that links the structural and contextual features of the adopting states to differences in the forms the policy assumed, once it was implemented. ^ Secondary data were collected from publicly available information sources to describe characteristics of states’ political system, health system, economic system, sociodemographic context and implemented policy attributes. I posited that socio-demographic differences, political system differences and health system differences would directly account for the observed differences in policy output among the states. ^ Exploratory data analysis techniques, which included median polishing and multidimensional scaling, were employed to identify compelling patterns in the data. Scaled results across model components showed that economic system was most closely related to policy output, followed by health system. Political system and socio-demographic characteristics were shown to be weakly associated with policy output. Goodness-of-fit measures for MDS solutions implemented across states and model components, in one- and two-dimensions, were very good. ^ This comparative policy analysis of twelve states that adopted and implemented HHS Regulation 42 C.F.R. § 457 contributes to existing knowledge in three areas: CHIP Perinatal policy, public health policy and policy sciences. First, the framework allows for the identification of CHIP Perinatal program design possibilities and provides a basis for future studies that evaluate policy impact or performance. Second, studies of policy determinants are not well represented in the health policy literature. Thus, this study contributes to the development of the literature in public health policy. Finally, the conceptual framework for policy determinants developed in this study suggests new ways for policy makers and practitioners to frame policy arguments, encouraging policy change or reform. ^

HEALTH MAINTENANCE ORGANIZATION ENVIRONMENT: AN EMPIRICAL STUDY OF THE RELATIONSHIP OF EXTERNAL CONTEXT AND PERFORMANCE

The research project is an extension of a series of administrative science and health care research projects evaluating the influence of external context, organizational strategy, and organizational structure upon organizational success or performance. The research will rely on the assumption that there is not one single best approach to the management of organizations (the contingency theory). As organizational effectiveness is dependent on an appropriate mix of factors, organizations may be equally effective based on differing combinations of factors. The external context of the organization is expected to influence internal organizational strategy and structure and in turn the internal measures affect performance (discriminant theory). The research considers the relationship of external context and organization performance.^ The unit of study for the research will be the health maintenance organization (HMO); an organization the accepts in exchange for a fixed, advance capitation payment, contractual responsibility to assure the delivery of a stated range of health sevices to a voluntary enrolled population. With the current Federal resurgence of interest in the Health Maintenance Organization (HMO) as a major component in the health care system, attention must be directed at maximizing development of HMOs from the limited resources available. Increased skills are needed in both Federal and private evaluation of HMO feasibility in order to prevent resource investment and in projects that will fail while concurrently identifying potentially successful projects that will not be considered using current standards.^ The research considers 192 factors measuring contextual milieu (social, educational, economic, legal, demographic, health and technological factors). Through intercorrelation and principle components data reduction techniques this was reduced to 12 variables. Two measures of HMO performance were identified, they are (1) HMO status (operational or defunct), and (2) a principle components factor score considering eight measures of performance. The relationship between HMO context and performance was analysed using correlation and stepwise multiple regression methods. In each case it has been concluded that the external contextual variables are not predictive of success or failure of study Health Maintenance Organizations. This suggests that performance of an HMO may rely on internal organizational factors. These findings have policy implications as contextual measures are used as a major determinant in HMO feasibility analysis, and as a factor in the allocation of limited Federal funds. ^

Comparison of machine learning approaches and statistical methods for estimation of airborne chemical exposures

Accurate quantitative estimation of exposure using retrospective data has been one of the most challenging tasks in the exposure assessment field. To improve these estimates, some models have been developed using published exposure databases with their corresponding exposure determinants. These models are designed to be applied to reported exposure determinants obtained from study subjects or exposure levels assigned by an industrial hygienist, so quantitative exposure estimates can be obtained. ^ In an effort to improve the prediction accuracy and generalizability of these models, and taking into account that the limitations encountered in previous studies might be due to limitations in the applicability of traditional statistical methods and concepts, the use of computer science- derived data analysis methods, predominantly machine learning approaches, were proposed and explored in this study. ^ The goal of this study was to develop a set of models using decision trees/ensemble and neural networks methods to predict occupational outcomes based on literature-derived databases, and compare, using cross-validation and data splitting techniques, the resulting prediction capacity to that of traditional regression models. Two cases were addressed: the categorical case, where the exposure level was measured as an exposure rating following the American Industrial Hygiene Association guidelines and the continuous case, where the result of the exposure is expressed as a concentration value. Previously developed literature-based exposure databases for 1,1,1 trichloroethane, methylene dichloride and, trichloroethylene were used. ^ When compared to regression estimations, results showed better accuracy of decision trees/ensemble techniques for the categorical case while neural networks were better for estimation of continuous exposure values. Overrepresentation of classes and overfitting were the main causes for poor neural network performance and accuracy. Estimations based on literature-based databases using machine learning techniques might provide an advantage when they are applied to other methodologies that combine `expert inputs' with current exposure measurements, like the Bayesian Decision Analysis tool. The use of machine learning techniques to more accurately estimate exposures from literature-based exposure databases might represent the starting point for the independence from the expert judgment.^