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. ^

OCCUPATIONAL CAUSES OF LUNG CANCER IN NEW MEXICO'S ANGLOS AND HISPANICS

Although, elevated risk for lung cancer has been associated with certain industries and occupations in previous studies, the lack of cigarette smoking information in many of these investigations resulted in estimates that could not be adjusted for the effects of smoking. To determine lung cancer risk due to occupation and smoking, for New Mexico's Anglos and Hispanics, a population-based case-control study was conducted. Incident cases diagnosed 1980-1982, and controls from the general population, were interviewed for lifetime occupational and smoking histories. Specific high risk industries and occupations were identified in advance and linked with industrial and occupational codes for hypotheses-testings. Significantly elevated risks were found for welders (RR = 3.5) and underground miners (RR = 2.0) with adjustment for smoking. Because shipbuilding was the industry of employment for only five of the 18 cases who were welders, exposures other than asbestos could be causal agents. Among the underground for only five of the 18 cases who were welders, exposures other than asbestos could be causal agents. Among the underground miners, uranium, copper, lead and zinc, coal, and potash mining industries were represented. Low prevalence of employment in some of the industries and occupations of interest resulted in inconclusive results. ^