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Description
Genes have widely different pertinences to the etiology and pathology of diseases. Thus, they can be ranked according to their disease-significance on a genomic scale, which is the subject of gene prioritization. Given a set of genes known to be related to a disease, it is reasonable to use them as a basis to determine the significance of other candidate genes, which will then be ranked based on the association they exhibit with respect to the given set of known genes. Experimental and computational data of various kinds have different reliability and relevance to a disease under study. This work presents a gene prioritization method based on integrated biological networks that incorporates and models the various levels of relevance and reliability of diverse sources. The method is shown to achieve significantly higher performance as compared to two well-known gene prioritization algorithms. Essentially, no bias in the performance was seen as it was applied to diseases of diverse ethnology, e.g., monogenic, polygenic and cancer. The method was highly stable and robust against significant levels of noise in the data. Biological networks are often sparse, which can impede the operation of associationbased gene prioritization algorithms such as the one presented here from a computational perspective. As a potential approach to overcome this limitation, we explore the value that transcription factor binding sites can have in elucidating suitable targets. Transcription factors are needed for the expression of most genes, especially in higher organisms and hence genes can be associated via their genetic regulatory properties. While each transcription factor recognizes specific DNA sequence patterns, such patterns are mostly unknown for many transcription factors. Even those that are known are inconsistently reported in the literature, implying a potentially high level of inaccuracy. We developed computational methods for prediction and improvement of transcription factor binding patterns. Tests performed on the improvement method by employing synthetic patterns under various conditions showed that the method is very robust and the patterns produced invariably converge to nearly identical series of patterns. Preliminary tests were conducted to incorporate knowledge from transcription factor binding sites into our networkbased model for prioritization, with encouraging results. Genes have widely different pertinences to the etiology and pathology of diseases. Thus, they can be ranked according to their disease-significance on a genomic scale, which is the subject of gene prioritization. Given a set of genes known to be related to a disease, it is reasonable to use them as a basis to determine the significance of other candidate genes, which will then be ranked based on the association they exhibit with respect to the given set of known genes. Experimental and computational data of various kinds have different reliability and relevance to a disease under study. This work presents a gene prioritization method based on integrated biological networks that incorporates and models the various levels of relevance and reliability of diverse sources. The method is shown to achieve significantly higher performance as compared to two well-known gene prioritization algorithms. Essentially, no bias in the performance was seen as it was applied to diseases of diverse ethnology, e.g., monogenic, polygenic and cancer. The method was highly stable and robust against significant levels of noise in the data. Biological networks are often sparse, which can impede the operation of associationbased gene prioritization algorithms such as the one presented here from a computational perspective. As a potential approach to overcome this limitation, we explore the value that transcription factor binding sites can have in elucidating suitable targets. Transcription factors are needed for the expression of most genes, especially in higher organisms and hence genes can be associated via their genetic regulatory properties. While each transcription factor recognizes specific DNA sequence patterns, such patterns are mostly unknown for many transcription factors. Even those that are known are inconsistently reported in the literature, implying a potentially high level of inaccuracy. We developed computational methods for prediction and improvement of transcription factor binding patterns. Tests performed on the improvement method by employing synthetic patterns under various conditions showed that the method is very robust and the patterns produced invariably converge to nearly identical series of patterns. Preliminary tests were conducted to incorporate knowledge from transcription factor binding sites into our networkbased model for prioritization, with encouraging results. To validate these approaches in a disease-specific context, we built a schizophreniaspecific network based on the inferred associations and performed a comprehensive prioritization of human genes with respect to the disease. These results are expected to be validated empirically, but computational validation using known targets are very positive.
ContributorsLee, Jang (Author) / Gonzalez, Graciela (Thesis advisor) / Ye, Jieping (Committee member) / Davulcu, Hasan (Committee member) / Gallitano-Mendel, Amelia (Committee member) / Arizona State University (Publisher)
Created2011
Description
Service based software (SBS) systems are software systems consisting of services based on the service oriented architecture (SOA). Each service in SBS systems provides partial functionalities and collaborates with other services as workflows to provide the functionalities required by the systems. These services may be developed and/or owned by different entities and physically distributed across the Internet. Compared with traditional software system components which are usually specifically designed for the target systems and bound tightly, the interfaces of services and their communication protocols are standardized, which allow SBS systems to support late binding, provide better interoperability, better flexibility in dynamic business logics, and higher fault tolerance. The development process of SBS systems can be divided to three major phases: 1) SBS specification, 2) service discovery and matching, and 3) service composition and workflow execution. This dissertation focuses on the second phase, and presents a privacy preserving service discovery and ranking approach for multiple user QoS requirements. This approach helps service providers to register services and service users to search services through public, but untrusted service directories with the protection of their privacy against the service directories. The service directories can match the registered services with service requests, but do not learn any information about them. Our approach also enforces access control on services during the matching process, which prevents unauthorized users from discovering services. After the service directories match a set of services that satisfy the service users' functionality requirements, the service discovery approach presented in this dissertation further considers service users' QoS requirements in two steps. First, this approach optimizes services' QoS by making tradeoff among various QoS aspects with users' QoS requirements and preferences. Second, this approach ranks services based on how well they satisfy users' QoS requirements to help service users select the most suitable service to develop their SBSs.
ContributorsYin, Yin (Author) / Yau, Stephen S. (Thesis advisor) / Candan, Kasim (Committee member) / Dasgupta, Partha (Committee member) / Santanam, Raghu (Committee member) / Arizona State University (Publisher)
Created2011
Description
Individuals' experiences, environment, and education greatly impact their entire being. Similarly, a designer is affected by these elements, which impacts how, what and why they design. In order for design education to generate designers who are more socially aware problem solvers, that education must introduce complex social matters and not just design skills. Traditionally designers learned through apprenticing a master. Most design education has moved away from this traditional model and has begun incorporating a well-rounded program of study, yet there are still more improvements to be made. This research proposes a new Integrated Transformational Experience Model, ITEM, for design education which will be rooted in sustainability, cultural integration, social embeddedness, and discipline collaboration. The designer will be introduced to new ideas and experiences from the immersion of current social issues where they will gain experience creating solutions to global problems enabling them to become catalysts of change. This research is based on interviews with industrial design students to gain insights, benefits and drawbacks of the current model of design education. This research will expand on the current model for design education, combining new ideas that will shed light on the future of design disciplines through the education and motivation of designers. The desired outcome of this study is to incorporate hands on learning through social issues in design classrooms, identify ways to educate future problem solvers, and inspire more research on this issue.
ContributorsWingate, Andrea (Author) / Takamura, John (Thesis advisor) / Stamm, Jill (Committee member) / Bender, Diane (Committee member) / Arizona State University (Publisher)
Created2011
Description
The uncertainty of change inherent in issues such as climate change and regional growth has created a significant challenge for public decision makers trying to decide what adaptation actions are needed to respond to these possible changes. This challenge threatens the resiliency and thus the long term sustainability of our social-ecological systems. Using an empirical embedded case study approach to explore the application of advanced scenario analysis methods to regional growth visioning projects in two regions, this dissertation provides empirical evidence that for issues with high uncertainty, advanced scenario planning (ASP) methods are effective tools for helping decision makers to anticipate and prepare to adapt to change.
ContributorsQuay, Ray (Author) / Pijawka, David (Thesis advisor) / Shangraw, Ralph (Committee member) / Holway, James (Committee member) / Arizona State University (Publisher)
Created2011
Description
Advancements in computer vision and machine learning have added a new dimension to remote sensing applications with the aid of imagery analysis techniques. Applications such as autonomous navigation and terrain classification which make use of image classification techniques are challenging problems and research is still being carried out to find better solutions. In this thesis, a novel method is proposed which uses image registration techniques to provide better image classification. This method reduces the error rate of classification by performing image registration of the images with the previously obtained images before performing classification. The motivation behind this is the fact that images that are obtained in the same region which need to be classified will not differ significantly in characteristics. Hence, registration will provide an image that matches closer to the previously obtained image, thus providing better classification. To illustrate that the proposed method works, naïve Bayes and iterative closest point (ICP) algorithms are used for the image classification and registration stages respectively. This implementation was tested extensively in simulation using synthetic images and using a real life data set called the Defense Advanced Research Project Agency (DARPA) Learning Applied to Ground Robots (LAGR) dataset. The results show that the ICP algorithm does help in better classification with Naïve Bayes by reducing the error rate by an average of about 10% in the synthetic data and by about 7% on the actual datasets used.
ContributorsMuralidhar, Ashwini (Author) / Saripalli, Srikanth (Thesis advisor) / Papandreou-Suppappola, Antonia (Committee member) / Turaga, Pavan (Committee member) / Arizona State University (Publisher)
Created2011
Description
This thesis proposed a novel approach to establish the trust model in a social network scenario based on users' emails. Email is one of the most important social connections nowadays. By analyzing email exchange activities among users, a social network trust model can be established to judge the trust rate between each two users. The whole trust checking process is divided into two steps: local checking and remote checking. Local checking directly contacts the email server to calculate the trust rate based on user's own email communication history. Remote checking is a distributed computing process to get help from user's social network friends and built the trust rate together. The email-based trust model is built upon a cloud computing framework called MobiCloud. Inside MobiCloud, each user occupies a virtual machine which can directly communicate with others. Based on this feature, the distributed trust model is implemented as a combination of local analysis and remote analysis in the cloud. Experiment results show that the trust evaluation model can give accurate trust rate even in a small scale social network which does not have lots of social connections. With this trust model, the security in both social network services and email communication could be improved.
ContributorsZhong, Yunji (Author) / Huang, Dijiang (Thesis advisor) / Dasgupta, Partha (Committee member) / Syrotiuk, Violet (Committee member) / Arizona State University (Publisher)
Created2011
Description
In the last few decades, the rapid development of electronic music technologies has changed the way society interacts with music, which in turn impacts the profession of music therapy. Except for a few cases, music therapy has not extensively explored the integration of new technology. However, current research trends show a willingness and excitement to explore the possibilities (Nagler, 2011; Ramsey, 2011; Magee, et al., 2011; Magee & Burland, 2008; Magee 2006). The project described in this paper intends to demonstrate one of these possibilities by combining modern technologies to create an interactive musical system with practical applications in music therapy. In addition to designing a practical tool, the project aims to question the role of technology in music therapy and to initiate dialogue between technologists and music therapists. The project, entitled MIST: A Musical Interactive Space for Therapy, uses modern gestural technology (the Microsoft® Kinect®) to capture body movements and turn them into music. It is intended for use in a clinical setting with children with mild to moderate disabilities. The system is a software/hardware package that is inexpensive, user-friendly, and portable. There are two functional modes of the system: the first sonifies specific movement tasks of reaching and balancing; the second is an interactive musical play space in which an entire room becomes responsive to presence and movement, creating a sonic playground. The therapeutic goals of the system are to motivate and train physical movement, encourage exploration of space and the body, and allow for musical expression, play, auditory perception, and social interaction.
ContributorsHeadlee, Kimberlee (Author) / Ingalls, Todd M (Thesis advisor) / Crowe, Barbara J. (Thesis advisor) / Stauffer, Sandra L (Committee member) / Arizona State University (Publisher)
Created2011
Description
S-Taliro is a fully functional Matlab toolbox that searches for trajectories of minimal robustness in hybrid systems that are implemented as either m-functions or Simulink/State flow models. Trajectories with minimal robustness are found using automatic testing of hybrid systems against user specifications. In this work we use Metric Temporal Logic (MTL) to describe the user specifications for the hybrid systems. We then try to falsify the MTL specification using global minimization of robustness metric. Global minimization is carried out using stochastic optimization algorithms like Monte-Carlo (MC) and Extended Ant Colony Optimization (EACO) algorithms. Irrespective of the type of the model we provide as an input to S-Taliro, the user needs to specify the MTL specification, the initial conditions and the bounds on the inputs. S-Taliro then uses this information to generate test inputs which are used to simulate the system. The simulation trace is then provided as an input to Taliro which computes the robustness estimate of the MTL formula. Global minimization of this robustness metric is performed to generate new test inputs which again generate simulation traces which are closer to falsifying the MTL formula. Traces with negative robustness values indicate that the simulation trace falsified the MTL formula. Traces with positive robustness values are also of great importance because they indicate how robust the system is against the given specification. S-Taliro has been seamlessly integrated into the Matlab environment, which is extensively used for model-based development of control software. Moreover the toolbox has been developed in a modular fashion and therefore adding new optimization algorithms is easy and straightforward. In this work I present the architecture of S-Taliro and its working on a few benchmark problems.
ContributorsAnnapureddy, Yashwanth Singh Rahul (Author) / Fainekos, Georgios (Thesis advisor) / Lee, Yann-Hang (Committee member) / Gupta, Sandeep (Committee member) / Arizona State University (Publisher)
Created2011
Description
Community Supported Agriculture programs (CSAs) have become a viable local source of fresh agricultural goods and represent a potentially new way to improve fruit and vegetable consumption among individuals and families. Studies concerning CSAs have focused mainly on characteristics of the typical CSA member and motivations and barriers to join a CSA program. The purpose of this study was to examine whether behavior and attitudinal differences existed between current CSA members and a nonmember control group. Specifically, ecological attitudes, eating out behaviors, composting frequency, and family participation in food preparation were assessed. This study utilized an online survey comprising items from previous survey research as well as newly created items. A total of 115 CSA member and 233 control survey responses were collected. CSA members were more likely to be older, have more education, and have a higher income than the control group. The majority of CSA members surveyed were female, identified as non-Hispanic and Caucasian, earned a higher income, and reported being the primary food shopper and preparer. The majority of members also noted that the amount and variety of fruits and vegetables they ate and served their family increased as a result of joining a CSA. CSA members were more ecologically minded compared to the control group. Frequency of eating out was not significantly different between groups. However, eating out behaviors were different between income categories. CSA members spent significantly more money at each meal eaten away from home and spent significantly more money on eating out each week. In both cases, controlling for income attenuated differences between groups. CSA members composted at a significantly higher rate and took part in other eco-friendly behaviors more often than the control group. Finally, no significant difference was evident between the two groups when analyzing family involvement in food preparation and meal decision-making. Overall, some significant attitudinal and behavioral differences existed between CSA members and non-CSA members. Further research is necessary to examine other distinctions between the two groups and whether these differences occur as a result of CSA membership.
ContributorsMacMillan Uribe, Alexandra L (Author) / Wharton, Christopher (Christopher Mack), 1977- (Thesis advisor) / Winham, Donna (Committee member) / Eakin, Hallie (Committee member) / Arizona State University (Publisher)
Created2011
Description
In many classication problems data samples cannot be collected easily, example in drug trials, biological experiments and study on cancer patients. In many situations the data set size is small and there are many outliers. When classifying such data, example cancer vs normal patients the consequences of mis-classication are probably more important than any other data type, because the data point could be a cancer patient or the classication decision could help determine what gene might be over expressed and perhaps a cause of cancer. These mis-classications are typically higher in the presence of outlier data points. The aim of this thesis is to develop a maximum margin classier that is suited to address the lack of robustness of discriminant based classiers (like the Support Vector Machine (SVM)) to noise and outliers. The underlying notion is to adopt and develop a natural loss function that is more robust to outliers and more representative of the true loss function of the data. It is demonstrated experimentally that SVM's are indeed susceptible to outliers and that the new classier developed, here coined as Robust-SVM (RSVM), is superior to all studied classier on the synthetic datasets. It is superior to the SVM in both the synthetic and experimental data from biomedical studies and is competent to a classier derived on similar lines when real life data examples are considered.
ContributorsGupta, Sidharth (Author) / Kim, Seungchan (Thesis advisor) / Welfert, Bruno (Committee member) / Li, Baoxin (Committee member) / Arizona State University (Publisher)
Created2011