A PROBABILISTIC APPROACH TO DATA INTEGRATION IN BIOMEDICAL RESEARCH: THE IsBIG EXPERIMENTS

Indiana University-Purdue University Indianapolis (IUPUI)Biomedical research has produced vast amounts of new information in the last decade but has been slow to find its use in clinical applications. Data from disparate sources such as genetic studies and summary data from published literature have been amassed, but there is a significant gap, primarily due to a lack of normative methods, in combining such information for inference and knowledge discovery. In this research using Bayesian Networks (BN), a probabilistic framework is built to address this gap. BN are a relatively new method of representing uncertain relationships among variables using probabilities and graph theory. Despite their computational complexity of inference, BN represent domain knowledge concisely. In this work, strategies using BN have been developed to incorporate a range of available information from both raw data sources and statistical and summary measures in a coherent framework. As an example of this framework, a prototype model (In-silico Bayesian Integration of GWAS or IsBIG) has been developed. IsBIG integrates summary and statistical measures from the NIH catalog of genome wide association studies (GWAS) and the database of human genome variations from the international HapMap project. IsBIG produces a map of disease to disease associations as inferred by genetic linkages in the population. Quantitative evaluation of the IsBIG model shows correlation with empiric results from our Electronic Medical Record (EMR) – The Regenstrief Medical Record System (RMRS). Only a small fraction of disease to disease associations in the population can be explained by the linking of a genetic variation to a disease association as studied in the GWAS. None the less, the model appears to have found novel associations among some diseases that are not described in the literature but are confirmed in our EMR. Thus, in conclusion, our results demonstrate the potential use of a probabilistic modeling approach for combining data from disparate sources for inference and knowledge discovery purposes in biomedical research

New Techniques for Learning Parameters in Bayesian Networks.

PhDOne of the hardest challenges in building a realistic Bayesian network (BN) model is to construct the node probability tables (NPTs). Even with a fixed predefined model structure and very large amounts of relevant data, machine learning methods do not consistently achieve great accuracy compared to the ground truth when learning the NPT entries (parameters). Hence, it is widely believed that incorporating expert judgment or related domain knowledge can improve the parameter learning accuracy. This is especially true in the sparse data situation. Expert judgments come in many forms. In this thesis we focus on expert judgment that specifies inequality or equality relationships among variables. Related domain knowledge is data that comes from a different but related problem. By exploiting expert judgment and related knowledge, this thesis makes novel contributions to improve the BN parameter learning performance, including: • The multinomial parameter learning model with interior constraints (MPL-C) and exterior constraints (MPL-EC). This model itself is an auxiliary BN, which encodes the multinomial parameter learning process and constraints elicited from the expert judgments. • The BN parameter transfer learning (BNPTL) algorithm. Given some potentially related (source) BNs, this algorithm automatically explores the most relevant source BN and BN fragments, and fuses the selected source and target parameters in a robust way. • A generic BN parameter learning framework. This framework uses both expert judgments and transferred knowledge to improve the learning accuracy. This framework transfers the mined data statistics from the source network as the parameter priors of the target network. Experiments based on the BNs from a well-known repository as well as two realworld case studies using different data sample sizes demonstrate that the proposed new approaches can achieve much greater learning accuracy compared to other state-of-theart methods with relatively sparse data.China Scholarship Counci

Understanding the Role of Motivation in the Reading of Children With ADHD-related Characteristics

This thesis investigated the potential reading benefits of motivation, and particularly story choice (intrinsic motivator) and reward (extrinsic motivator) in children with ADHD-related characteristics (inattention, hyperactivity/impulsivity, poor interference control), who attended mainstream primary schools. Children with ADHD-related characteristics are at risk of reading underachievement, irrespective of the presence of an ADHD diagnosis. Using a repeated measures design with two conditions (Choice, No Choice), Study 1 tested choice effects on the reading of a community sample of children (N = 108, aged 8 to 9 years old, 56 boys) with minimal and severe ADHD-related characteristics, with focus on inattention. Using a repeated measures design with three conditions (Choice, Reward, No Motivation), Study 2 explored choice and reward effects on the reading of children with and without diagnosed ADHD (N = 24, aged 8 to 11 years old, 16 boys). Using the Study 2 sample, Study 3 sought the perspectives of children with and without ADHD about reading motivation and checked for any group qualitative differences. Drawing on the quantitative findings, story choice increased the reading comprehension, and less consistently the reading enjoyment, of both children with minimal and severe ADHD-related characteristics. Study 2 findings pointed towards the benefits of story choice and reward for the reading comprehension and enjoyment of children with and without ADHD, however, results were relatively inconclusive. Choice and reward effects were not found to be more pronounced for children with severe ADHD-related characteristics and/or ADHD than those with such minimal characteristics. In Study 3, children with and without ADHD acknowledged similarly the contribution of motivators, including choice and reward, to reading. Overall, findings offer empirical support for the positive impact of story choice and reward on children with varying degrees of ADHD-related characteristics, stressing the need to consider further their manipulation during reading instruction in the classroom

The Social Epistemology of Experimental Economics

Ana Cristina Cordeiro dos Santos was born in Lisbon, Portugal, in 1971. She received her B.Sc. degree in Economics from Technical University of Lisbon, in Portugal, in 1994, and a MA degree in Social Policy from Roskilde University, in Denmark, in 1995. Since 1996 she has been a teaching assistant at Instituto Superior de Ciências do Trabalho e da Empresa (ISCTE), in Lisbon. She obtained a MPhil degree in Philosophy of Economics at the Erasmus Institute for Philosophy of Economics, Erasmus University Rotterdam, in 2001. She completed her Ph.D. in Philosophy of Economics at the same institute.This thesis analysed the experimental process of knowledge production. It investigated how scientists build their confidence in knowledge generated by a process in which both the means and the outcomes of knowledge production are re-constructed. The study of experimental practice in the natural and human sciences supports the view that scientists are convinced that they have produced the phenomenon of interest when they achieve a three-way coherence between the three components of the experimental system: the experimental procedure, the instrumental model and the phenomenal model. When the three-way coherence is achieved, experimenters believe that they have created an experimental system that succeeded in producing the phenomenon of interest. The relation of coherence among the three components of the experimental system justifies belief in the experimental results because the threeway alignment supports each one of them and thus the experimental result conveyed by the phenomenal model. This was the underlying principle of the argument from coherence that justifies the way by which experimenters form belief in experimental results. However, it was also noted that the three-way alignment is not sufficient to justify belief in experimentally generated knowledge. Two additional arguments were presented that reinforced the epistemic value of the three-way coherence. The argument from materiality asserts that the direct engagement of the subject matter in knowledge production (both in the natural and human domains) renders experimental results and the coherences supporting them non-trivial achievements. The coherent problem-solutions arrived at carry knowledge about the subject under scrutiny because scientists cannot fully control it to meet their prior expectations. However, the argument from materiality does not satisfactorily account for experimenters’ confidence in experimental results. The participation of the subject matter might still be severely constrained by the problem-situation at hand or by the plasticity of the experimental systems. The argument from sociality asserts that the social dimension of knowledge production encourages the generation of fruitful problem-situations and reliable problem-solutions by bringing to the production process a vast number of resources of practice. The three arguments in conjunction lead to a broader conclusion: the greater the number and the greater the heterogeneity of the resources (material, conceptual and social) involved in knowledge production, the higher the epistemic status of the relations of coherence established given that they are the result of practices that have explored relevant courses of action to the resolution of interesting problem-situations

Um processo baseado em redes bayesianas para avaliação da aplicação do scrum em projetos de software.

O aumento na utilização de métodos ágeis tem sido motivado pela necessidade de respostas rápidas a demandas de um mercado volátil na área de software. Em contraste com os tradicionais processos dirigidos a planos, métodos ágeis são focados nas pessoas, orientados à comunicação, flexíveis, rápidos, leves, responsivos e dirigidos à aprendizagem e melhoria contínua. Como consequência, fatores subjetivos tais como colaboração, comunicação e auto-organização são chaves para avaliar a maturidade do desenvolvimento de software ágil. O Scrum, focado no gerenciamento de projetos, é o método ágil mais popular. Ao ser adotado por uma equipe, a aplicação do Scrum deve ser melhorada continuamente sendo complementado com práticas e processos de desenvolvimento e gerenciamento ágeis. Apesar da Reunião de Retrospectiva, evento do Scrum, ser um período reservado ao final de cada sprint para a equipe refletir sobre a melhoria do método de desenvolvimento, não há procedimentos claros e específicos para a realização da mesma. Na literatura, há diversas propostas de soluções, embora nenhuma consolidada, para tal. Desta forma, o problema em questão é: como instrumentar o Scrum para auxiliar na melhoria contínua do método de desenvolvimento com foco na avaliação do processo de engenharia de requisitos, equipe de desenvolvimento e incrementos do produto? Nesta tese, propõe-se um processo sistemático baseado em redes bayesianas para auxiliar na avaliação da aplicação do Scrum em projetos de software, instrumentando o método para auxiliar na sua melhoria contínua com foco na avaliação do processo de engenharia de requisitos, equipe de desenvolvimento e incrementos do produto. A rede bayesiana foi construída por meio de um processo de Engenharia de Conhecimento de Redes Bayesianas. Uma base de dados, elicitada de dezoito projetos reais de uma empresa, foi coletada por meio de um questionário. Essa base de dados foi utilizada para avaliar a acurácia da predição da Rede Bayesiana. Como resultado, a previsão foi correta para quatorze projetos (acurácia de 78%). Dessa forma, conclui-se que o modelo é capaz de realizar previsões com acurácia satisfatória e, dessa forma, é útil para auxiliar nas tomadas de decisões de projetos Scrum.The use of Agile Software Development (ASD) is increasing to satisfy the need to respond to fast moving market demand and gain market share. In contrast with traditional plan-driven processes, ASD are people and communication-oriented, flexible, fast, lightweight, responsive, driven for learning and continuous improvement. As consequence, subjective factors such as collaboration, communication and self-management are key to evaluate the maturity of agile adoption. Scrum, which is focused on project management, is the most popular agile method. Whenever adopted, the usage of Scrum must be continuously improved by complementing it with development and management practices and processes. Even though the Retrospective Meeting, a Scrum event, is a period at the end of each sprint for the team to assess the development method, there are no clear and specific procedures to conduct it. In literature, there are several, but no consolidated, proposed solutions to assist on ASD adoption and assessment. Therefore, the research problem is: how to instrument Scrum to assist on the continuous improvement of the development method focusing on the requirements engineering process, development team and product increment? In this thesis, we propose a Bayesian networks-based process to assist on the assessment of Scrum-based projects, instrumenting the software development method to assist on its continuous improvement focusing on the requirements engineering process, development team and product increments. We have built the Bayesian network using a Knowledge Engineering Bayesian Network (KEBN) process that calculates the customer satisfaction given factors of the software development method. To evaluate its prediction accuracy, we have collected data from 18 industry projects from one organization through a questionnaire. As a result, the prediction was correct for fourteen projects (78% accuracy). Therefore, we conclude that the model is capable of accurately predicting the customer satisfaction and is useful to assist on decision-support on Scrum projects