http://hdl.handle.net/1903/7125 Sun, 19 May 2013 13:15:20 GMT 2013-05-19T13:15:20Z http://hdl.handle.net/1903/13549 Title: Analysis of Factors Associated With Tuberculosis Outcomes in District Kullu, India Authors: Stone, Heather Abstract: India is the country with the largest number of tuberculosis (TB) cases, contributing 20% of the global burden of infection (1) and 2 million cases annually (2). However, few if any studies have examined the epidemiology of TB in the Northern state of Himachal Pradesh. This study is a retrospective review of medical records of all tuberculosis patients (N=1086) seen at the two hospitals in Manali, District Kullu, Himachal Pradesh, India between 2008-2011. The analysis determined that being younger, female, living in a town, and/or a patient at Mission Hospital, were factors significantly associated with having extrapulmonary versus pulmonary tuberculosis (EPTB). Being older was associated with an increased likelihood of previous/complex treatment compared to new patients. Being female, from a town, and/or older were associated with receiving a non-standard regimen. Finally, patients who were previously treated/complex were significantly more likely to receive a non-standard regimen than new patients. Sun, 01 Jan 2012 00:00:00 GMT http://hdl.handle.net/1903/13549 2012-01-01T00:00:00Z http://hdl.handle.net/1903/13388 Title: Logic minimization and rule extraction for identification of functional sites in molecular sequences Authors: Cruz-Cano, Raul; Lee, Mei-Ling Ting; Leung, Ming-Ying Abstract: Background Logic minimization is the application of algebraic axioms to a binary dataset with the purpose of reducing the number of digital variables and/or rules needed to express it. Although logic minimization techniques have been applied to bioinformatics datasets before, they have not been used in classification and rule discovery problems. In this paper, we propose a method based on logic minimization to extract predictive rules for two bioinformatics problems involving the identification of functional sites in molecular sequences: transcription factor binding sites (TFBS) in DNA and O-glycosylation sites in proteins. TFBS are important in various developmental processes and glycosylation is a posttranslational modification critical to protein functions. Methods In the present study, we first transformed the original biological dataset into a suitable binary form. Logic minimization was then applied to generate sets of simple rules to describe the transformed dataset. These rules were used to predict TFBS and O-glycosylation sites. The TFBS dataset is obtained from the TRANSFAC database, while the glycosylation dataset was compiled using information from OGLYCBASE and the Swiss-Prot Database. We performed the same predictions using two standard classification techniques, Artificial Neural Networks (ANN) and Support Vector Machines (SVM), and used their sensitivities and positive predictive values as benchmarks for the performance of our proposed algorithm. SVM were also used to reduce the number of variables included in the logic minimization approach. Results For both TFBS and O-glycosylation sites, the prediction performance of the proposed logic minimization method was generally comparable and, in some cases, superior to the standard ANN and SVM classification methods with the advantage of providing intelligible rules to describe the datasets. In TFBS prediction, logic minimization produced a very small set of simple rules. In glycosylation site prediction, the rules produced were also interpretable and the most popular rules generated appeared to correlate well with recently reported hydrophilic/hydrophobic enhancement values of amino acids around possible O-glycosylation sites. Experiments with Self-Organizing Neural Networks corroborate the practical worth of the logic minimization method for these case studies. Conclusions The proposed logic minimization algorithm provides sets of rules that can be used to predict TFBS and O-glycosylation sites with sensitivity and positive predictive value comparable to those from ANN and SVM. Moreover, the logic minimization method has the additional capability of generating interpretable rules that allow biological scientists to correlate the predictions with other experimental results and to form new hypotheses for further investigation. Additional experiments with alternative rule-extraction techniques demonstrate that the logic minimization method is able to produce accurate rules from datasets with large numbers of variables and limited numbers of positive examples. Thu, 16 Aug 2012 00:00:00 GMT http://hdl.handle.net/1903/13388 2012-08-16T00:00:00Z http://hdl.handle.net/1903/13368 Title: Externalizing behavior in early childhood and body mass index from age 2 to 12 years: longitudinal analyses of a prospective cohort study Authors: Anderson, Sarah E; He, Xin; Schoppe-Sullivan, Sarah; Must, Aviva Abstract: Background: Some evidence suggests that obesity and behavior problems are related in children, but studies have been conflicting and have rarely included children under age 4. An association between behavior problems in early childhood and risk for obesity could suggest that a common set of factors contribute to both. Our research objectives were to determine the extent to which externalizing behavior in early childhood is related to body mass index (BMI) in early childhood and through age 12, and to evaluate whether these associations differ by sex and race. Methods: Data from the NICHD Study of Early Child Care and Youth Development were analyzed. Externalizing behaviors at 24 months were assessed by mothers using the Child Behavior Checklist. BMI was calculated from measured height and weight assessed 7 times between age 2 and 12 years. Linear mixed effects models were used to assess associations between 24 month externalizing behavior and BMI from 2 to 12 years, calculate predicted differences in BMI, and evaluate effect modification. Results: Externalizing behavior at 24 months was associated with a higher BMI at 24 months and through age 12. Results from a linear mixed effects model, controlling for confounding variables and internalizing behavior, predicted a difference in BMI of approximately 3/4 of a unit at 24 months of age comparing children with high levels of externalizing behavior to children with low levels of externalizing behavior. There was some evidence of effect modification by race; among white children, the average BMI difference remained stable through age 12, but it doubled to 1.5 BMI units among children who were black or another race. Conclusions: Our analyses suggest that externalizing behaviors in early childhood are associated with children's weight status early in childhood and throughout the elementary school years, though the magnitude of the effect is modest. Wed, 14 Jul 2010 00:00:00 GMT http://hdl.handle.net/1903/13368 2010-07-14T00:00:00Z http://hdl.handle.net/1903/12998 Title: DOUBLY PENALIZED LOGISTIC REGRESSION FOR GENOMEWIDE ASSOCIATION STUDIES WITH LINEARLY STRUCTURED GENETIC NETWORKS Authors: Li, Xia Abstract: This research aims to integrate linear structures of genetic networks into genomewide analysis studies (GWAS). Lasso penalized logistic regression is ideally suited for continuous model selection in case-control disease gene mapping, especially when the number of predictor variables far exceeds the number of observations. But it fails to consider the structure of genetic networks. Imposing an additional weighted fused lasso can further remove irrelevant predictors. Nesterov's method is employed to handle the high dimensionality and complexity of genetic data. It also resolves the non-differentiability problem of the lasso and fused lasso penalties. In simulation studies, this proposed method shows advantages in some cases compared with lasso and fused lasso. We apply this method to the coeliac data on chromosome 8. Sun, 01 Jan 2012 00:00:00 GMT http://hdl.handle.net/1903/12998 2012-01-01T00:00:00Z