The technical contributions explore collaborative communication with both no coding and network coding methods. First, I propose a collaborative data delivery protocol to exploit the optimal performance of multiple energy-synchronized paths without network coding, i.e. a new max-flow min-variance algorithm. In consort with this data delivery protocol, a localized TDMA MAC protocol is designed to synchronize nodes' duty-cycles and mitigate media access contentions. However, the energy supply can change dynamically over time, making determined duty cycles synchronization difficult in practice. A probabilistic approach is investigated. Therefore, I present Opportunistic Network Erasure Coding protocol (ONEC), to collaboratively collect data. ONEC derives the probability distribution of coding degree in each node and enable opportunistic in-network recoding, and guarantee the recovery of original sensor data can be achieved with high probability upon receiving any sufficient amount of encoded packets. Next, OnCode, an opportunistic in-network data coding and delivery protocol is proposed to further improve data communication under the constraints of energy synchronization. It is resilient to packet loss and network disruptions, and does not require explicit end-to-end feedback message. Moreover, I present a network Erasure Coding with randomized Power Control (ECPC) mechanism for collaborative data storage in disruptive sensor networks. ECPC only requires each node to perform a single broadcast at each of its several randomly selected power levels. Thus it incurs very low communication overhead. Finally, I propose an integrated algorithm and middleware (Ravine Stream) to improve data delivery throughput as well as data persistency in energy-synchronized sensor network.

This dissertation presents a comprehensive and efficient approach to maintaining integrity constraints. The design enforces data consistency throughout the OWL life cycle, including the processes of OWL generation, maintenance, and interactions with other ontologies. For OWL generation, the Paraconsistent model is used to maintain integrity constraints during the relational database to OWL translation process. Then a new rule-based language with set extension is introduced as a platform to allow users to specify constraints, along with a demonstration of 18 commonly used constraints written in this language. In addition, a new constraint maintenance system, called Jena2Drools, is proposed and implemented, to show its effectiveness and efficiency. To further handle inconsistencies among multiple distributed ontologies, this work constructs a framework to break down global constraints into several sub-constraints for efficient parallel validation.

The new service-oriented paradigm brings new research challenges in composing multiple simulation services in a meaningful and correct way for simulation experiments. To systematically support simulation software as a service (SimSaaS) and service-oriented simulation experiment, we propose a layered framework that includes five layers: an infrastructure layer, a simulation execution engine layer, a simulation service layer, a simulation experiment layer and finally a graphical user interface layer. Within this layered framework, we provide a specification for both simulation experiment and the involved individual simulation services. Such a formal specification is useful in order to support systematic compositions of simulation services as well as automatic deployment of composed services for carrying out simulation experiments. Built on this specification, we identify the issue of mismatch of time granularity and event granularity in composing simulation services at the pragmatic level, and develop four types of granularity handling agents to be associated with the couplings between services. The ultimate goal is to achieve standard and automated approaches for simulation service composition in the emerging service-oriented computing environment. Finally, to achieve more efficient service-oriented simulation, we develop a profile-based partitioning method that exploits a system’s dynamic behavior and uses it as a profile to guide the spatial partitioning for more efficient parallel simulation. We develop the work in this dissertation within the application context of wildfire spread simulation, and demonstrate the effectiveness of our work based on this application.

We focus on two main problems in transcriptome data analysis, namely, transcriptome reconstruction and quantification. Transcriptome reconstruction, also referred to as novel isoform discovery, is the problem of reconstructing the transcript sequences from the sequencing data. Reconstruction can be done de novo or it can be assisted by existing genome and transcriptome annotations. Transcriptome quantification refers to the problem of estimating the expression level of each transcript. We present a genome-guided and annotation-guided transcriptome reconstruction methods as well as methods for transcript and gene expression level estimation. Empirical results on both synthetic and real RNA-seq datasets show that the proposed methods improve transcriptome quantification and reconstruction accuracy compared to previous methods.

Secondly, since there is no performance ratio analysis in previously mentioned work, three problems are investigated and analyzed later. To be specific, the MinMax Degree Maximal Independent Set (MDMIS) problem, the Load-Balanced Virtual Backbone (LBVB) problem, and the MinMax Valid-Degree non Backbone node Allocation (MVBA) problem. Approximation algorithms and comprehensive theoretical analysis of the approximation factors are presented in the dissertation.

On the other hand, in the current related literature, networks are deterministic where two nodes are assumed either connected or disconnected. In most real applications, however, there are many intermittently connected wireless links called lossy links, which only provide probabilistic connectivity. For WSNs with lossy links, we propose a Stochastic Network Model (SNM). Under this model, we measure the quality of CDSs using CDS reliability. In this dissertation, we construct an MCDS while its reliability is above a preset applicationspecified threshold, called Reliable MCDS (RMCDS). We propose a novel Genetic Algorithm (GA) with immigrant schemes called RMCDS-GA to solve the RMCDS problem.

Finally, we apply the constructed LBCDS to a practical application under the realistic SNM model, namely data aggregation. To be specific, a new problem, Load-Balanced Data Aggregation Tree (LBDAT), is introduced finally. Our simulation results show that the proposed algorithms outperform the existing state-of-the-art approaches significantly.

Next an enhanced scheme is introduced using new encoding style. In the new style, information like amino acid substitution matrix, polarity, secondary structure information and relative distance between alpha carbon atoms etc is collected through spatial traversing of the 3D structure to form training vectors. This guarantees that the properties of alpha carbon atoms that are close together in 3D space and thus interacting are used in vector formation. With the use of fuzzy decision tree, we obtained a training accuracy around 90%. There is significant improvement compared to previous encoding technique in prediction accuracy and execution time. This outcome motivates to continue to explore effective machine learning algorithms for accurate protein model quality assessment.

Finally these machines are tested using CASP8 and CASP9 templates and compared with other CASP competitors, with promising results. We further discuss the importance of model quality assessment and other information from proteins that could be considered for the same.

network motifs is still invalidated and currently no databases exist for this purpose.

In this thesis, we focus not only on the computational efficiency but also on the biological meanings of the motifs. We provide an efficient way to incorporate biological information with clustering analysis methods: For example, a sparse nonnegative matrix factorization (SNMF) method is used with Chou-Fasman parameters for the protein motif finding. Biological network motifs are searched by various clustering algorithms with Gene ontology (GO) information. Experimental results show that the algorithms perform better than existing algorithms by producing a larger number of high-quality of biological motifs.

In addition, we apply biological network motifs for the discovery of essential proteins. Essential proteins are defined as a minimum set of proteins which are vital for development to a fertile adult and in a cellular life in an organism. We design a new centrality algorithm with biological network motifs, named MCGO, and score proteins in a protein-protein interaction (PPI) network to find essential proteins. MCGO is also combined with other centrality measures to predict essential proteins using machine learning techniques.

We have three contributions to the study of biological motifs through this thesis; 1) Clustering analysis is efficiently used in this work and biological information is easily integrated with the analysis; 2) We focus more on the biological meanings of motifs by adding biological knowledge in the algorithms and by suggesting biologically related evaluation methods. 3) Biological network motifs are successfully applied to a practical application of prediction of essential proteins.

In this dissertation, we have designed a new scoring method for use in multiple sequence alignment. Our scoring method encapsulates stereo-chemical properties of sequence residues and their substitution probabilities into a tree-structure scoring scheme. This new technique provides a reliable scoring scheme with low computational complexity.

In addition to the new scoring scheme, we have designed an overlapping sequence clustering algorithm to use in our new three multiple sequence alignment algorithms. One of our alignment algorithms uses a dynamic weighted guidance tree to perform multiple sequence alignment in progressive fashion. The use of dynamic weighted tree allows errors in the early alignment stages to be corrected in the subsequence stages. Other two algorithms utilize sequence knowledge-bases and sequence consistency to produce biological meaningful sequence alignments. To improve the speed of the multiple sequence alignment, we have developed a parallel algorithm that can be deployed on reconfigurable computer models. Analytically, our parallel algorithm is the fastest progressive multiple sequence alignment algorithm.

In this dissertation I explore and propose a number of new techniques to analyze and visualize the syntactic and semantic patterns of unstructured English texts.

The syntactic analysis is done through a proposed visualization technique which categorizes and compares different English compositions based on their different reading complexity metrics. For the semantic analysis I use Latent Semantic Analysis (LSA) to analyze the hidden patterns in complex compositions. I have used this technique to analyze comments from a social visualization web site for detecting the irrelevant ones (e.g., spam). The patterns of collaborations are also studied through statistical analysis.

Word sense disambiguation is used to figure out the correct sense of a word in a sentence or composition. Using textual similarity measure, based on the different word similarity measures and word sense disambiguation on collaborative text snippets from social collaborative environment, reveals a direction to untie the knots of complex hidden patterns of collaboration.

The genomic diversity of viral quasispecies is a subject of a great interest, particularly for chronic infections, since it can lead to resistance to existing therapies. High-throughput sequencing is a promising approach to characterizing viral diversity, but unfortunately standard assembly software cannot be used to simultaneously assemble and estimate the abundance of multiple closely related (but non-identical) quasispecies sequences. Here, we introduce a new Viral Spectrum Assembler (ViSpA) for inferring quasispecies spectrum and compare it with the state-of-the-art ShoRAH tool on both synthetic and real 454 pyrosequencing shotgun reads from HCV and HIV quasispecies. While ShoRAH has an advanced error correction algorithm, ViSpA is better at quasispecies assembling, producing more accurate reconstruction of a viral population. We also foresee ViSpA application to the analysis of high-throughput sequencing data from bacterial metagenomic samples and ecological samples of eukaryote populations.

Due to the large data volume in genome-wide association studies, it is desirable to find a small subset of SNPs (tags) that covers the genetic variation of the entire set. We explore the trade-off between the number of tags used per non-tagged SNP and possible overfitting and propose an efficient 2LR-Tagging heuristic.