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Description
Reverse engineering gene regulatory networks (GRNs) is an important problem in the domain of Systems Biology. Learning GRNs is challenging due to the inherent complexity of the real regulatory networks and the heterogeneity of samples in available biomedical data. Real world biological data are commonly collected from broad surveys (profiling studies) and aggregate highly heterogeneous biological samples. Popular methods to learn GRNs simplistically assume a single universal regulatory network corresponding to available data. They neglect regulatory network adaptation due to change in underlying conditions and cellular phenotype or both. This dissertation presents a novel computational framework to learn common regulatory interactions and networks underlying the different sets of relatively homogeneous samples from real world biological data. The characteristic set of samples/conditions and corresponding regulatory interactions defines the cellular context (context). Context, in this dissertation, represents the deterministic transcriptional activity within the specific cellular regulatory mechanism. The major contributions of this framework include - modeling and learning context specific GRNs; associating enriched samples with contexts to interpret contextual interactions using biological knowledge; pruning extraneous edges from the context-specific GRN to improve the precision of the final GRNs; integrating multisource data to learn inter and intra domain interactions and increase confidence in obtained GRNs; and finally, learning combinatorial conditioning factors from the data to identify regulatory cofactors. The framework, Expattern, was applied to both real world and synthetic data. Interesting insights were obtained into mechanism of action of drugs on analysis of NCI60 drug activity and gene expression data. Application to refractory cancer data and Glioblastoma multiforme yield GRNs that were readily annotated with context-specific phenotypic information. Refractory cancer GRNs also displayed associations between distinct cancers, not observed through only clustering. Performance comparisons on multi-context synthetic data show the framework Expattern performs better than other comparable methods.
ContributorsSen, Ina (Author) / Kim, Seungchan (Thesis advisor) / Baral, Chitta (Committee member) / Bittner, Michael (Committee member) / Konjevod, Goran (Committee member) / Arizona State University (Publisher)
Created2011
Description
Action language C+ is a formalism for describing properties of actions, which is based on nonmonotonic causal logic. The definite fragment of C+ is implemented in the Causal Calculator (CCalc), which is based on the reduction of nonmonotonic causal logic to propositional logic. This thesis describes the language of CCalc in terms of answer set programming (ASP), based on the translation of nonmonotonic causal logic to formulas under the stable model semantics. I designed a standard library which describes the constructs of the input language of CCalc in terms of ASP, allowing a simple modular method to represent CCalc input programs in the language of ASP. Using the combination of system F2LP and answer set solvers, this method achieves functionality close to that of CCalc while taking advantage of answer set solvers to yield efficient computation that is orders of magnitude faster than CCalc for many benchmark examples. In support of this, I created an automated translation system Cplus2ASP that implements the translation and encoding method and automatically invokes the necessary software to solve the translated input programs.
ContributorsCasolary, Michael (Author) / Lee, Joohyung (Thesis advisor) / Ahn, Gail-Joon (Committee member) / Baral, Chitta (Committee member) / Arizona State University (Publisher)
Created2011
ContributorsLougheed, Julia (Performer) / Novak, Gail (Pianist) (Performer) / Bayer, Elizabeth Kennedy (Performer) / Clifton-Armenta, Tyler (Performer) / Park, Julie (Performer) / Javier de Alba, Francisco (Performer) / Vientos Dulces (Performer) / ASU Library. Music Library (Publisher)
Created2018-04-07
ContributorsLovelady, Alexis (Performer) / ASU Library. Music Library (Publisher)
Created2018-04-08
Description
Different logic-based knowledge representation formalisms have different limitations either with respect to expressivity or with respect to computational efficiency. First-order logic, which is the basis of Description Logics (DLs), is not suitable for defeasible reasoning due to its monotonic nature. The nonmonotonic formalisms that extend first-order logic, such as circumscription and default logic, are expressive but lack efficient implementations. The nonmonotonic formalisms that are based on the declarative logic programming approach, such as Answer Set Programming (ASP), have efficient implementations but are not expressive enough for representing and reasoning with open domains. This dissertation uses the first-order stable model semantics, which extends both first-order logic and ASP, to relate circumscription to ASP, and to integrate DLs and ASP, thereby partially overcoming the limitations of the formalisms. By exploiting the relationship between circumscription and ASP, well-known action formalisms, such as the situation calculus, the event calculus, and Temporal Action Logics, are reformulated in ASP. The advantages of these reformulations are shown with respect to the generality of the reasoning tasks that can be handled and with respect to the computational efficiency. The integration of DLs and ASP presented in this dissertation provides a framework for integrating rules and ontologies for the semantic web. This framework enables us to perform nonmonotonic reasoning with DL knowledge bases. Observing the need to integrate action theories and ontologies, the above results are used to reformulate the problem of integrating action theories and ontologies as a problem of integrating rules and ontologies, thus enabling us to use the computational tools developed in the context of the latter for the former.
ContributorsPalla, Ravi (Author) / Lee, Joohyung (Thesis advisor) / Baral, Chitta (Committee member) / Kambhampati, Subbarao (Committee member) / Lifschitz, Vladimir (Committee member) / Arizona State University (Publisher)
Created2012
ContributorsDruesedow, Elizabeth (Performer) / ASU Library. Music Library (Publisher)
Created2018-04-07
ContributorsShi, Zhan (Performer) / ASU Library. Music Library (Publisher)
Created2018-03-26
ContributorsQualls, Aliyah (Performer) / Winseck, Kevin (Performer) / Solari, John (Performer) / ASU Library. Music Library (Publisher)
Created2018-04-03
Description
Automated planning problems classically involve finding a sequence of actions that transform an initial state to some state satisfying a conjunctive set of goals with no temporal constraints. But in many real-world problems, the best plan may involve satisfying only a subset of goals or missing defined goal deadlines. For example, this may be required when goals are logically conflicting, or when there are time or cost constraints such that achieving all goals on time may be too expensive. In this case, goals and deadlines must be declared as soft. I call these partial satisfaction planning (PSP) problems. In this work, I focus on particular types of PSP problems, where goals are given a quantitative value based on whether (or when) they are achieved. The objective is to find a plan with the best quality. A first challenge is in finding adequate goal representations that capture common types of goal achievement rewards and costs. One popular representation is to give a single reward on each goal of a planning problem. I further expand on this approach by allowing users to directly introduce utility dependencies, providing for changes of goal achievement reward directly based on the goals a plan achieves. After, I introduce time-dependent goal costs, where a plan incurs penalty if it will achieve a goal past a specified deadline. To solve PSP problems with goal utility dependencies, I look at using state-of-the-art methodologies currently employed for classical planning problems involving heuristic search. In doing so, one faces the challenge of simultaneously determining the best set of goals and plan to achieve them. This is complicated by utility dependencies defined by a user and cost dependencies within the plan. To address this, I introduce a set of heuristics based on combinations using relaxed plans and integer programming formulations. Further, I explore an approach to improve search through learning techniques by using automatically generated state features to find new states from which to search. Finally, the investigation into handling time-dependent goal costs leads us to an improved search technique derived from observations based on solving discretized approximations of cost functions.
ContributorsBenton, J (Author) / Kambhampati, Subbarao (Thesis advisor) / Baral, Chitta (Committee member) / Do, Minh B. (Committee member) / Smith, David E. (Committee member) / Langley, Pat (Committee member) / Arizona State University (Publisher)
Created2012
ContributorsYu, Xuehui (Performer) / ASU Library. Music Library (Publisher)
Created2018-03-31