Rule based classifier for the analysis of gene-gene and gene-environment interactions in genetic association studies

<p>Abstract</p> <p>Background</p> <p>Several methods have been presented for the analysis of complex interactions between genetic polymorphisms and/or environmental factors. Despite the available methods, there is still a need for alternative methods, because no single method will perform well in all scenarios. The aim of this work was to evaluate the performance of three selected rule based classifier algorithms, RIPPER, RIDOR and PART, for the analysis of genetic association studies.</p> <p>Methods</p> <p>Overall, 42 datasets were simulated with three different case-control models, a varying number of subjects (300, 600), SNPs (500, 1500, 3000) and noise (5%, 10%, 20%). The algorithms were applied to each of the datasets with a set of algorithm-specific settings. Results were further investigated with respect to a) the Model, b) the Rules, and c) the Attribute level. Data analysis was performed using WEKA, SAS and PERL.</p> <p>Results</p> <p>The RIPPER algorithm discovered the true case-control model at least once in >33% of the datasets. The RIDOR and PART algorithm performed poorly for model detection. The RIPPER, RIDOR and PART algorithm discovered the true case-control rules in more than 83%, 83% and 44% of the datasets, respectively. All three algorithms were able to detect the attributes utilized in the respective case-control models in most datasets.</p> <p>Conclusions</p> <p>The current analyses substantiate the utility of rule based classifiers such as RIPPER, RIDOR and PART for the detection of gene-gene/gene-environment interactions in genetic association studies. These classifiers could provide a valuable new method, complementing existing approaches, in the analysis of genetic association studies. The methods provide an advantage in being able to handle both categorical and continuous variable types. Further, because the outputs of the analyses are easy to interpret, the rule based classifier approach could quickly generate testable hypotheses for additional evaluation. Since the algorithms are computationally inexpensive, they may serve as valuable tools for preselection of attributes to be used in more complex, computationally intensive approaches. Whether used in isolation or in conjunction with other tools, rule based classifiers are an important addition to the armamentarium of tools available for analyses of complex genetic association studies.</p

Dropout Prevention and the Model-Minority Stereotype: Reflections from an Asian American High School Dropout

A recent review of the research by the U.S. Department of Education Institute of Education Sciences has resulted in the recommendation of six promising practices to ensure that all students are actively engaged in school and on a path to post-secondary success (Dynarski et al. in Dropout prevention: A practice guide (NCEE 2008–4025)). The purpose of this study was to explore the experience and perspective of an Asian American high school dropout and the extent to which his story aligns with dominant thinking, including the six recommended dropout prevention practices and the model minority myth (MMM) of Achievement Orientation, a common belief that Asian Americans exhibit greater success than any other minority ethnic group. The adolescent dropout was interviewed on eight occasions. Findings revealed that the MMM may have contributed to the lack of intervention provided to this student and that the most worthwhile recommendations from his perspective include: assigning adult advocates to at-risk students, the use of a systematic data-tracking system to target and individualize interventions, and the ability of the school to provide academic support and a personalized learning environment

Probabilistic analysis of risk and mitigation of deepwater well blowouts and oil spills

The development of robust risk assessment procedures for offshore oil and gas operations is a major element for the assessment of the potential feedback between planned activities and the environment. We illustrate a methodological and computational framework conducive to (1) a quantitative risk analysis of deepwater well barrier failures and subsequent hydrocarbon release to the environment and (2) the analysis of the value of the deployment of conventional and/or innovative mitigation measures. Our methodological framework is grounded on historical records and combines the use of Dynamic Event Trees and Decision Trees from which we estimate probability of occurrence and impact of post-blowout events. Each sequence of response actions, which are undertaken immediately after the event or in the subsequent days, is considered within the context of appropriately structured event paths. This approach is conducive to an estimate of the expected value of key decisions and underlying technologies, with an emphasis on their potential to reduce the oil spill volume, which can critically impact the environment. Our study yields an original comparative analysis of diverse intervention strategies, and forms a basis to guiding future efforts towards the development and deployment of technologies and operating procedures yielding maximum benefit in terms of safety of operations and environmental protection