Harnessing the power of DNA microarray technology requires the existence of analysis methods that accurately interpret microarray data. Current literature abounds with algorithms meant for the investigation of microarray data. However, there is need for an efficient approach that combines different techniques of microarray data analysis and provides a viable solution to dimensionality reduction of microarray data. Reducing the high dimensionality of microarray data is one approach in striving to better understand the information contained within the data. We propose a novel approach for dimensionality reduction of microarray data that effectively combines different techniques in the study of DNA microarrays. Our method, <strong><em>KAS</em></strong> (<em>kernel alignment with semidefinite embedding</em>), aids the visualization of microarray data in two dimensions and shows improvement over existing dimensionality reduction methods such as PCA, LLE and Isomap.

Pairwise sequence alignment is a fundamental problem in bioinformatics with wide applicability. This thesis presents three new algorithms for this well-studied problem. First, we present a new algorithm, RDA, which aligns sequences in small segments, rather than by individual bases. Then, we present two algorithms for aligning long genomic sequences: CAPE, a pairwise global aligner, and FEAST, a pairwise local aligner. RDA produces interesting alignments that can be substantially different in structure than traditional alignments. It is also better than traditional alignment at the task of homology detection. However, its main negative is a very slow run time. Further, although it produces alignments with different structure, it is not clear if the differences have a practical value in genomic research. Our main success comes from our local aligner, FEAST. We describe two main improvements: a new more descriptive model of evolution, and a new local extension algorithm that considers all possible evolutionary histories rather than only the most likely. Our new model of evolution provides for improved alignment accuracy, and substantially improved parameter training. In particular, we produce a new parameter set for aligning human and mouse sequences that properly describes regions of weak similarity and regions of strong similarity. The second result is our new extension algorithm. Depending on heuristic settings, our new algorithm can provide for more sensitivity than existing extension algorithms, more specificity, or a combination of the two. By comparing to CAPE, our global aligner, we find that the sensitivity increase provided by our local extension algorithm is so substantial that it outperforms CAPE on sequence with 0.9 or more expected substitutions per site. CAPE itself gives improved sensitivity for sequence with 0.7 or more expected substitutions per site, but at a great run time cost. FEAST and our local extension algorithm improves on this too, the run time is only slightly slower than existing local alignment algorithms and asymptotically the same.

Query composition is an often complicated and cumbersome task for persons performing a literature search. This thesis is part of a project which aims to present possible queries to the user in form of natural language expressions. The thesis presents methods of ontology slice generation. Slices are parts of ontologies connecting two concepts along all possible paths between them. Those slices hence represent all relevant queries connecting the concepts and the paths can in a later step be translated into natural language expressions. Methods of slice alignment, connecting slices that originate from different ontologies, are also presented. The thesis concludes with some example scenarios and comparisons to related work.

Due to increasing numbers of non-coding RNA (ncRNA) being discovered recently, there is interest in identifying homologs of a given structured RNA sequence. Exhaustive homology searching for structured RNA molecules using covariance models is infeasible on genome-length sequences. Hence, heuristic methods are employed, but they largely ignore structural information in the query. We present a novel method, which uses secondary structure information, to perform homology searches for a structured RNA molecule. We define the concept of a <em>pair seed</em> and theoretically model alignments of random and related paired regions to compute expected sensitivity and specificity. We show that our method gives theoretical gains in sensitivity and specificity compared to a BLAST-based heuristic approach. We provide experimental verification of this gain. <br /><br /> We also show that pair seeds can be effectively combined with the spaced seeds approach to nucleotide homology search. The hybrid search method has theoretical specificity superior to that of the BLAST seed. We provide experimental evaluation of our hypotheses. Finally, we note that our method is easily modified to process pseudo-knotted regions in the query, something outside the scope of covariance model based methods.

This thesis introduces new techniques for finding genes in genomic sequences. Genes are regions of a genome encoding proteins of an organism. Identification of genes in a genome is an important step in the annotation process after a new genome is sequenced. The prediction accuracy of gene finding can be greatly improved by using experimental evidence. This evidence includes homologies between the genome and databases of known proteins, or evolutionary conservation of genomic sequence in different species. <br /><br /> We propose a flexible framework to incorporate several different sources of such evidence into a gene finder based on a hidden Markov model. Various sources of evidence are expressed as partial probabilistic statements about the annotation of positions in the sequence, and these are combined with the hidden Markov model to obtain the final gene prediction. The opportunity to use partial statements allows us to handle missing information transparently and to cope with the heterogeneous character of individual sources of evidence. On the other hand, this feature makes the combination step more difficult. We present a new method for combining partial probabilistic statements and prove that it is an extension of existing methods for combining complete probability statements. We evaluate the performance of our system and its individual components on data from the human and fruit fly genomes. <br /><br /> The use of sequence evolutionary conservation as a source of evidence in gene finding requires efficient and sensitive tools for finding similar regions in very long sequences. We present a method for improving the sensitivity of existing tools for this task by careful modeling of sequence properties. In particular, we build a hidden Markov model representing a typical homology between two protein coding regions and then use this model to optimize a component of a heuristic algorithm called a spaced seed. The seeds that we discover significantly improve the accuracy and running time of similarity search in protein coding regions, and are directly applicable to our gene finder.

Dental records have been widely used as tools in forensic identification. With the vast volume of cases that need to be investigated by forensic odontologists, a move towards a computer-aided dental identification system is necessary. We propose a computer-aided framework for efficient matching of dental x-rays for human identification purposes. Given a dental x-ray with a marked region of interest (ROI), we search the database of x-rays (presumed to be taken from known individuals) to retrieve a closest match. In this work we use a slightly extended Weighted Sum of Squared Differences (SSD) cost function to express the degree of similarity/overlap between two dental radiographs. Unlike other iterative Least Squares methods that use local information for gradient-based optimization, our method finds the globally optimal translation. In 90% of the identification trials, our method ranked the correct match in the top 10% using a database of 571 images. Experiments indicate that matching dental records using the extended SSD cost function is a viable method for human dental identification.

Harnessing the power of DNA microarray technology requires the existence of analysis methods that accurately interpret microarray data. Current literature abounds with algorithms meant for the investigation of microarray data. However, there is need for an efficient approach that combines different techniques of microarray data analysis and provides a viable solution to dimensionality reduction of microarray data. Reducing the high dimensionality of microarray data is one approach in striving to better understand the information contained within the data. We propose a novel approach for dimensionality reduction of microarray data that effectively combines different techniques in the study of DNA microarrays. Our method, <strong><em>KAS</em></strong> (<em>kernel alignment with semidefinite embedding</em>), aids the visualization of microarray data in two dimensions and shows improvement over existing dimensionality reduction methods such as PCA, LLE and Isomap.

Pairwise sequence alignment is a fundamental problem in bioinformatics with wide applicability. This thesis presents three new algorithms for this well-studied problem. First, we present a new algorithm, RDA, which aligns sequences in small segments, rather than by individual bases. Then, we present two algorithms for aligning long genomic sequences: CAPE, a pairwise global aligner, and FEAST, a pairwise local aligner. RDA produces interesting alignments that can be substantially different in structure than traditional alignments. It is also better than traditional alignment at the task of homology detection. However, its main negative is a very slow run time. Further, although it produces alignments with different structure, it is not clear if the differences have a practical value in genomic research. Our main success comes from our local aligner, FEAST. We describe two main improvements: a new more descriptive model of evolution, and a new local extension algorithm that considers all possible evolutionary histories rather than only the most likely. Our new model of evolution provides for improved alignment accuracy, and substantially improved parameter training. In particular, we produce a new parameter set for aligning human and mouse sequences that properly describes regions of weak similarity and regions of strong similarity. The second result is our new extension algorithm. Depending on heuristic settings, our new algorithm can provide for more sensitivity than existing extension algorithms, more specificity, or a combination of the two. By comparing to CAPE, our global aligner, we find that the sensitivity increase provided by our local extension algorithm is so substantial that it outperforms CAPE on sequence with 0.9 or more expected substitutions per site. CAPE itself gives improved sensitivity for sequence with 0.7 or more expected substitutions per site, but at a great run time cost. FEAST and our local extension algorithm improves on this too, the run time is only slightly slower than existing local alignment algorithms and asymptotically the same.

Resource management in wireless networks is one of the key factors in maximizing the overall throughput. Contrary to popular belief, dividing the resources in a dense network does not yield the best results. A method that has been developed recently shares the spectrum amongst all the users in such a way that each node can potentially utilize about half of all the available resources. This new technique is often referred to as Interference Alignment and excels based on the fact that the amount of the network resources assigned to a user does not go to zero as the number of users in the network increases. Unfortunately it is still very difficult to implement the interference alignment concepts in practice. This thesis investigates some of the low-complexity solutions to integrate interference alignment ideas into the existing wireless networks. In the third and fourth chapters of this thesis, it is shown that introducing relays to a quasi-static wireless network can be very beneficial in terms of achieving higher degrees of freedom. The relays store the signals being communicated in the network and then send a linear combination of those signals. Using the proposed scheme, it is shown that although the relays cannot decode the original information, they can transform the equivalent channel in such a way that performing interference alignment becomes much easier. Investigating the required output power of the relays shows that it can scale either slower or faster than the output power of the main transmitters. This opens new doors for the applications that have constraints on the accessible output powers in the network nodes. The results are valid for both $X$ Channel and Interference Channel network topologies. In Chapter Five, the similarities between full-duplex transmitters and relays are examined. The results suggest that the transmitters can play the relay roles for offering easier interference alignment. Similar to the relay-based alignment, in the presented scheme full-duplex transmitters listen to the signals from other transmitters and use this information during the subsequent transmission periods. Studying the functionality of the full-duplex transmitters from the receivers' side shows the benefits of having a minimal cooperation between transmitters without even being able to decode the signals. It is also proved that the degrees of freedom for the $N$-user Interference Channel with full-duplex transmitters can be $\sqrt{\frac{N}{2}}$. The results offer an easy way to recover a portion of degrees of freedom with manageable complexity suited for practical systems.

This report is a case study with the aim of examining the link between business strategy and the strategy of projects. The field of project management in strategy of projects and their link to the strategy of parent company has yet to be explored. The existing body of literature presents the alignment of project to strategy in two main views which are that projects should have a similar strategy with the parent or that projects should be independent in strategy and follow its own approach. Researchers acknowledge that the limited theoretical frameworks in this stream suffer from the lack of empirical research. Thus this research is based on the question “What is the relation between company’s business strategy and project’s strategy in innovation projects following the position driven alignment approach?” The researchers utilize the position-driven alignment framework as propositioned by Artto, Kujala, Dietrich and Martinsuo (2007). The factors of stakeholder complexity and project autonomy are examined to explore the relationship between the parent strategy and the project strategy. The study conducted is a single case study design on an IT Platform in a large insurance company. Analysis from the data reveal interesting results; that i) The obedience of the project creates risk on the parent strategy, ii) parent strategy changes as the project progresses and that iii) the perception of importance of the project by the parent influences the project autonomy. Further evidence through empirical research is suggested on the other project positions in this framework.