Thesis (M.S.)--State University of New York at Binghamton, Department of Computer Science, Thomas J. Watson School of Engineering and Applied Science, 2007. / Includes bibliographical references.

Proteins provide evidence that a given gene is expressed, and machine learning algorithms can be applied to various proteomics problems in order to gain information about the underlying biology. This dissertation applies machine learning algorithms to proteomics data in order to predict whether or not a given peptide is observable by mass spectrometry, whether a given peptide can serve as a cell penetrating peptide, and then utilizes the peptides observed through mass spectrometry to aid in the structural annotation of the chicken genome. Peptides observed by mass spectrometry are used to identify proteins, and being able to accurately predict which peptides will be seen can allow researchers to analyze to what extent a given protein is observable. Cell penetrating peptides can possibly be utilized to allow targeted small molecule delivery across cellular membranes and possibly serve a role as drug delivery peptides. Peptides and proteins identified through mass spectrometry can help refine computational gene models and improve structural genome annotations.

To estimate the delivered dose to the patient during intra-fraction or throughout the whole treatment, it is important to determine the contribution of dose accumulated at different patient geometries to the overall dose. Dose mapping utilizes deformable image registration to map doses deposited on patient geometries at different times. Inputs to the dose mapping process are the irradiated and reference images, the displacement vector field, and a dose mapping algorithm. Thus accuracy of the mapped dose depends on the DVF and dose mapping algorithm. Dose mapping had been the subject of many research studies however, up to now there is no gold standard DIR or dose mapping algorithm. This thesis compares current dose mapping algorithms under different conditions such as choosing the planning target and dose grid size, and introduces new tool to estimate the required spatial accuracy of a DVF. 11 lung patients were used for this thesis work. IMRT plans were generated on the end of inhale breathing phases with 66 Gy as the prescription dose. Demons DVF’s were generated using the Pinnacle treatment planning system DIR interface. Dtransform, Tri-linear with sub-voxel division, and Pinnacle dose mapping algorithms were compared to energy transfer with mass sub-voxel mapping. For breathing phase 50% on 11 patients, tissue density gradients were highest around the edge of the tumor compared to the CTV and the PTV edge voxels. Thus treatment plans generated with margin equal to zero on the tumor might yield the highest dose mapping error (DME). For plans generated on the tumor, there was no clinical effect of DME on the MLD, lung V20, and Esophagus volume indices. Statistically, MLD and lung V20 DME were significant. Two patients had D98 Pinnacle-DME of 4.4 and 1.2 Gy. In high dose gradient regions DVF spatial accuracy of ~ 1 mm is needed while 8 to 10 mm DVF accuracy can be tolerated before introducing any considerable dose mapping errors inside the CTV. By using ETM with mass sub-voxel mapping and adapting the reported DVF accuracy, the findings of this thesis have the potential to increase the accuracy of 4D lung planning.

Next generation sequencing technologies are changing the way scientific experiments and diseases diagnostics are performed and thus will allow what is called personalized medicine. The sense of presented thesis is to make survey of new approaches to DNA sequencing and demonstrate usage and constraints of bioinformatic analytical tools available to day. Discussed techniques are then applied to the case study of finding molecular basis for rare hereditary disease. Introductory part deals with overview of commercially available sequencing techniques (454 Life Science, Applied Biosystems, Illumina, Helicos). Fundamentals of each method are described and possible further development is outlined. Post sequencing data analysis is than discussed in details. In practical section we demonstrate genome analysis techniques successfully used to reveal causal mutation in the gene responsible for adult form of autozomal neuronal ceroid lipofuscinosis (ANCL). Combination of linkage analysis (Merlin), copy number variant analysis (Genome-Wide Human SNP Array 6.0), analysis of expression profiles (HumanRef-8 v2 Expression BeadChips) and exome sequencing (SOLiD™ 4 System) has been applied to members of one ANCL family. We also paid attention to comparison, evaluation and selection of available mapping algorithms used in...

[pt] Esse trabalho apresenta uma abordagem baseada em programação matemática para a solução de problemas de valor inicial de contorno constitutivo elastoplástico. Considerando a plasticidade associativa, as equações constitutivas locais, em sua forma discreta, são formuladas como problemas de otimização cônica. Especificamente, é demonstrado que métodos implícitos de retorno a superfície para os critérios mais conhecidos da literatura, como o de Rankine, von Mises, Tresca, Drucker-Prager e Mohr Coulomb, podem ser expressos como problemas de otimização cônica de segunda ordem e semidefinida. Além disso, um novo método numérico para a determinação do operador elastoplástico consistente, baseado na derivada paramétrica de primeira ordem das soluções ótimas, é proposto. / [en] This work presents a mathematical programming approach for elastoplastic constitutive initial boundary value problems. Considering associative plasticity, the local discrete constitutive equations are formulated as conic programs. Specifically, it is demonstrated that implicit return-mapping schemes for well-known yield criteria, such as the Rankine, von Mises, Tresca, Drucker-Prager, and Mohr-Coulomb criteria, can be expressed as secondorder and semidefinite conic programs. Additionally, a novel scheme for the numerical evaluation of the consistent elastoplastic tangent operator, based on a first-order parameter derivative of the optimal solutions, is proposed.

La technologie actuelle permet aux scientifiques de divers domaines d'obtenir des données de plus en plus précises et volumineuses, Afin de résoudre ces problèmes associés à l'obtention de ces données, les architectures de calcul évoluent, en fournissant toujours plus de ressources, notamment grâce à des machines plus puissantes et à leur mutualisation. Dans cette thèse, nous proposons d’étudier dans un premier temps le placement des tâches d'applications itératives asynchrones dans des environnements hétérogènes et volatils. Notre solution nous permet également de s'affranchir de l(hétérogénéité des machines hôtes tout en offrent une implantation facilitée de politiques de tolérance aux pannes, les expérimentations que nous avons menées sont encourageantes et montrent qu'il existe un réel potentiel quand à l'utilisation d'une telle plateforme pour l'exécution d'applications scientifiques. / The current technology allows scientists of several domains to obtain more precise and large data. In the same time, computing architectures evolve too, by providing even more computing resources, with more powerful machines and the pooling of them. In this thesis, in a first time we propose to study the problem of the mapping of asynchronous iterative applications tasks into heterogeneous and volatile environments. Our solution allows also to overcome the heterogeneity of host machines while offering an easier implementation of policies for fault tolerance. The experiments we have conducted are encouraging ad show that there is real potential for the use of such a platform for running scientific applications.