Dissertationen zum Thema „Engineering, Computer|Biology, Bioinformatics|Computer Science“
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Dinh, Hieu Trung. „Algorithms for DNA Sequence Assembly and Motif Search“. University of Connecticut, 2013.
Bao, Shunxing. „Algorithmic Enhancements to Data Colocation Grid Frameworks for Big Data Medical Image Processing“. Thesis, Vanderbilt University, 2019. http://pqdtopen.proquest.com/#viewpdf?dispub=13877282.
Large-scale medical imaging studies to date have predominantly leveraged in-house, laboratory-based or traditional grid computing resources for their computing needs, where the applications often use hierarchical data structures (e.g., Network file system file stores) or databases (e.g., COINS, XNAT) for storage and retrieval. The resulting performance for laboratory-based approaches reveal that performance is impeded by standard network switches since typical processing can saturate network bandwidth during transfer from storage to processing nodes for even moderate-sized studies. On the other hand, the grid may be costly to use due to the dedicated resources used to execute the tasks and lack of elasticity. With increasing availability of cloud-based big data frameworks, such as Apache Hadoop, cloud-based services for executing medical imaging studies have shown promise.
Despite this promise, our studies have revealed that existing big data frameworks illustrate different performance limitations for medical imaging applications, which calls for new algorithms that optimize their performance and suitability for medical imaging. For instance, Apache HBases data distribution strategy of region split and merge is detrimental to the hierarchical organization of imaging data (e.g., project, subject, session, scan, slice). Big data medical image processing applications involving multi-stage analysis often exhibit significant variability in processing times ranging from a few seconds to several days. Due to the sequential nature of executing the analysis stages by traditional software technologies and platforms, any errors in the pipeline are only detected at the later stages despite the sources of errors predominantly being the highly compute-intensive first stage. This wastes precious computing resources and incurs prohibitively higher costs for re-executing the application. To address these challenges, this research propose a framework - Hadoop & HBase for Medical Image Processing (HadoopBase-MIP) - which develops a range of performance optimization algorithms and employs a number of system behaviors modeling for data storage, data access and data processing. We also introduce how to build up prototypes to help empirical system behaviors verification. Furthermore, we introduce a discovery with the development of HadoopBase-MIP about a new type of contrast for medical imaging deep brain structure enhancement. And finally we show how to move forward the Hadoop based framework design into a commercialized big data / High performance computing cluster with cheap, scalable and geographically distributed file system.
Ren, Kaiyu. „Mapping biomedical terms to UMLS concepts by an efficient layered dynamic programming framework“. The Ohio State University, 2014. http://rave.ohiolink.edu/etdc/view?acc_num=osu1398886613.
Sanga, Sandeep. „A Computational Systems Biology Approach to Predictive Oncology| A Computer Modeling and Bioinformatics Study Predicting Tumor Response to Therapy and Cancer Phenotypes“. Thesis, The University of Texas at Austin, 2015. http://pqdtopen.proquest.com/#viewpdf?dispub=3684162.
Technological advances in the recent decades have enabled cancer researchers to probe the disease at multiple resolutions. This wealth of experimental data combined with computational systems biology methods is now leading to predictive models of cancer progression and response to therapy. We begin by presenting our research group's multi-scale in silico framework for modeling cancer, whose core is a tissue-scale computational model capable of tracking the progression of tumors from a diffusion-limited avascular phase through angiogenesis, and into invasive lesions with realistic, complex morphologies. We adapt this core model to consider the delivery of systemically-administered anticancer agents and their effect on lesions once they reach their intended nuclear target. We calibrate the model parameters using in vitro data from the literature, and demonstrate through simulation that transport limitations affecting drug and oxygen distributions play a significant role in hampering the efficacy of chemotherapy; a result that has since been validated by in vitro experimentation. While this study demonstrates the capability of our adapted core model to predict distributions (e.g., cell density, pressure, oxygen, nutrient, drug) within lesions and consequent tumor morphology, nevertheless, the underlying factors driving tumor-scale behavior occur at finer scales. What is needed in our multi-scale approach is to parallel reality, where molecular signaling models predict cellular behavior, and ultimately drive what is seen at the tumor level. Models of signaling pathways linked to cell models are already beginning to surface in the literature. We next transition our research to the molecular level, where we employ data mining and bioinformatics methods to infer signaling relationships underlying a subset of breast cancer that might benefit from targeted therapy of Androgen Receptor and associated pathways. Defining the architecture of signaling pathways is a critical first step towards development of pathways models underlying tumor models, while also providing valuable insight for drug discovery. Finally, we develop an agent-based, cell-scale model focused on predicting motility in response to chemical signals in the microenvironment, generally accepted to be a necessary feature of cancer invasion and metastasis. This research demonstrates the use of signaling models to predict emergent cell behavior, such as motility. The research studies presented in this dissertation are critical steps towards developing a predictive, in silico computational model for cancer progression and response to therapy. Our Laboratory for Computational & Predictive Oncology, in collaboration with research groups throughout in the United States and Europe are following a computational systems biology paradigm where model development is fueled by biological knowledge, and model predictions are refining experimental focus. The ultimate objective is a virtual cancer simulator capable of accurately simulating cancer progression and response to therapy on a patient-specific basis.
Berry, Eric Zachary 1980. „Bioinformatics and database tools for glycans“. Thesis, Massachusetts Institute of Technology, 2004. http://hdl.handle.net/1721.1/27085.
Includes bibliographical references (leaves 75-76).
Recent advances in biology have afforded scientists with the knowledge that polysaccharides play an active role in modulating cellular activities. Glycosaminoglycans (GAGs) are one such family of polysaccharides that play a very important role in regulating the functions of numerous important signaling molecules and enzymes in the cell. Developing bioinformatics tools has been integral to advancing genomics and proteomics. While these tools have been well-developed to store and process sequence and structure information for proteins and DNA, they are very poorly developed for polysaccharides. Glycan structures pose special problems because of their tremendous information density per fundamental unit, their often-branched structures, and the complicated nature of their building blocks. The GlycoBank, an online database of known GAG structures and functions, has been developed to overcome many of these difficulties by developing a common notation for researchers to describe GAG sequences, a common repository to view known structure-function relationships, and the complex tools and searches needed to facilitate their work. This thesis focuses on the development of GlycoBank. In addition, a large, NIGMS-funded consortium, the Consortium for Functional Glycomics, is a larger database that also aims to store polysaccharide structure-function information of a broader collection of polysaccharides. The ideas and concepts implemented in developing GlycoBank were instrumental in developing databases and bioinformatics tools for the Consortium for Functional Glycomics.
by Eric Zachary Berry.
M.Eng.and S.B.
Guo, Xinyu. „Design of A Systolic Array-Based FPGA Parallel Architecture for the BLAST Algorithm and Its Implementation“. University of Toledo / OhioLINK, 2012. http://rave.ohiolink.edu/etdc/view?acc_num=toledo1338478834.
Kho, Soon Jye. „Sample Mislabeling Detection and Correction in Bioinformatics Experimental Data“. Wright State University / OhioLINK, 2021. http://rave.ohiolink.edu/etdc/view?acc_num=wright1629736147173188.
Bozdag, Doruk. „Graph Coloring and Clustering Algorithms for Science and Engineering Applications“. The Ohio State University, 2008. http://rave.ohiolink.edu/etdc/view?acc_num=osu1229459765.
Kalluru, Vikram Gajanan. „Identify Condition Specific Gene Co-expression Networks“. The Ohio State University, 2012. http://rave.ohiolink.edu/etdc/view?acc_num=osu1338304258.
Zhong, Cuncong. „Computational Methods for Comparative Non-coding RNA Analysis: From Structural Motif Identification to Genome-wide Functional Classification“. Doctoral diss., University of Central Florida, 2013. http://digital.library.ucf.edu/cdm/ref/collection/ETD/id/5894.
Ph.D.
Doctorate
Computer Science
Engineering and Computer Science
Computer Science
Sertel, Olcay. „Image Analysis for Computer-aided Histopathology“. The Ohio State University, 2010. http://rave.ohiolink.edu/etdc/view?acc_num=osu1276791696.
Lin, Allen. „Retroactivity, modularity, and insulation in synthetic biology circuits“. Thesis, Massachusetts Institute of Technology, 2011. http://hdl.handle.net/1721.1/76989.
Cataloged from PDF version of thesis.
Includes bibliographical references (p. 141-151).
A central concept in synthetic biology is the reuse of well-characterized modules. Modularity simplifies circuit design by allowing for the decomposition of systems into separate modules for individual construction. Complex regulatory networks can be assembled from a library of devices. However, current devices in synthetic biology may not actually be modular and may instead change behavior upon interconnections, a phenomenon called retroactivity. Addition of a new component to a system can change individual device dynamics within the system, potentially making timeconsuming iterative redesign necessary. Another need for systems construction is the ability to rapidly assemble constructs from part libraries in a combinatorial, highthroughput fashion. In this thesis, a multi-site assembly method that permits the rapid reshuffling of promoters and genes for yeast expression is established. Synthetic circuits in yeast to measure retroactivity and to act as an insulator that attenuates such effect are designed and modeled.
by Allen Lin.
M.Eng.
Kim, Bo S. (Bo Sung). „Robust network calibration and therapy design in systems biology“. Thesis, Massachusetts Institute of Technology, 2010. http://hdl.handle.net/1721.1/62422.
Cataloged from PDF version of thesis.
Includes bibliographical references (p. 115-123).
Mathematical modeling of biological networks is under active research, receiving attention for its ability to quantitatively represent the modeler's systems-level understanding of network functionalities. Computational methods that enhance the usefulness of mathematical models are thus being increasingly sought after, as they face a variety of difficulties that originate from limitations in model accuracy and experimental precision. This thesis explores robust optimization as a tool to counter the effects of these uncertainty-based difficulties in calibrating biological network models and in designing protocols for cancer immunotherapy. The robust approach to network calibration and therapy design aims to account for the worst-case uncertainty scenario that could threaten successful determination of network parameters or therapeutic protocols, by explicitly identifying and sampling the region of potential uncertainties corresponding to worst-case. Through designating individual numerical ranges that uncertain model parameters are each expected to lie within, the region of uncertainties is defined as a hypercube that encompasses a particular uncertainty range along each of its dimensions. For investigating its applicability to parameter estimation, the performance of the optimization method that embodies this robust approach is examined in the context of a model of a unit belonging to the mitogen-activated protein kinase pathway. For its significance in therapeutic design, the method is applied to both a canonical mathematical model of the tumor-immune system and a model specific to treating superficial bladder cancer with Bacillus Calmette-Guirin, which have both been selected to examine the plausibility of applying the method to either discrete-dose or continuous-dose administrations of immunotherapeutic agents. The robust optimization method is evaluated against a standard optimization method by comparing the relative robustness of their respective estimated parameters or designed therapies. Further analysis of the results obtained using the robust method points to properties and limitations, and in turn directions for improvement, of existing models and design frameworks for applying the robust method to network calibration and protocol design. An alternative mathematical formulation to solving the worst-case optimization problem is also studied, one that replaces the sampling process of the previous method with a linearization of the objective function's parameter space over the region of uncertainties. This formulation's relative computational efficiency additionally gives rise to a novel approach to experimental guidance directed at improving modeling efforts under uncertainties, which may potentially further fuel the advancement of quantitative systems biological research.
by Bo S. Kim.
Ph.D.
Kim, Daniel D. 1982. „A biological simulator using a stochastic approach for synthetic biology“. Thesis, Massachusetts Institute of Technology, 2005. http://hdl.handle.net/1721.1/33307.
Includes bibliographical references (leaves 58-59).
Synthetic Biology is a new engineering discipline created by the development of genetic engineering technology. Part of a new engineering discipline is to create new tools to build an integrated engineering environment. In this thesis, I designed and implemented a biological system simulator that will enable synthetic biologists to simulate their systems before they put time into building actual physical cells. Improvements to the current simulators in use include a design that enables extensions in functionality, external input signals, and a GUI that allows user interaction. The significance of the simulation results was tested by comparing them to actual live cellular experiments. The results showed that the new simulator can successfully simulate the trends of a simple synthetic cell.
by Daniel D. Kim.
M.Eng.
Ahn, Andrew In-Kyun 1979. „Fast Phase Dispersion Microscope : a new instrument for cellular biology“. Thesis, Massachusetts Institute of Technology, 2003. http://hdl.handle.net/1721.1/87867.
Includes bibliographical references (p. 143-144).
by Andrew In-Kyun Ahn.
M.Eng.
Wertheimer, Jeremy M. (Jeremy Michael). „Reasoning from experiments to causal models in molecular cell biology“. Thesis, Massachusetts Institute of Technology, 1996. http://hdl.handle.net/1721.1/11050.
Includes bibliographical references (p. 81-83).
by Jeremy M. Wertheimer.
Ph.D.
Tu, Yaa-Lirng. „A framework for teaching biology using StarLogo TNG : from DNA to evolution“. Thesis, Massachusetts Institute of Technology, 2009. http://hdl.handle.net/1721.1/53182.
Includes bibliographical references (p. 65-66).
This thesis outlines a 10-unit biology curriculum implemented in StarLogo TNG. The curriculum moves through units on ecology, the DNA-protein relationship, and evolution. By combining the three topics, it aims to highlight the similarities among different scales and the relationships between them. In particular, through the curriculum, students can see how small-scale changes in molecular processes can create large-scale changes in entire populations. In addition, the curriculum encourages students to engage in problembased learning, by which they are trained to approach questions creatively and independently.
by Yaa-Lirng Tu.
M.Eng.
Sun, Hong. „DETECTING MULTIPLE PROTEIN FOLDING TRAJECTORIES AND STRUCTURAL ALIGNMENT“. The Ohio State University, 2011. http://rave.ohiolink.edu/etdc/view?acc_num=osu1319744262.
Almassian, Amin. „Information Representation and Computation of Spike Trains in Reservoir Computing Systems with Spiking Neurons and Analog Neurons“. PDXScholar, 2016. http://pdxscholar.library.pdx.edu/open_access_etds/2724.
Robbins, Steven M. „Anatomical standardization of the human brain in euclidean 3-space and on the cortical 2-manifold“. Thesis, McGill University, 2003. http://digitool.Library.McGill.CA:80/R/?func=dbin-jump-full&object_id=84315.
Standardization methods in widespread use employ a 3D affine spatial transformation to map from the individual to the template, which matches only overall size and gross shape of the input brain. A wide range of more flexible image deformation algorithms have been developed in order to better match fine detail. All such algorithms involve design choices that are subject to debate, and most have numerical parameters whose value must be specified by the user. In order to provide guidance for such choices, the first part of this thesis develops two measures of alignment consistency that are used to evaluate performance of a standardization method. The performance of different choices for algorithm design, numerical parameters, and template selection strategy for 3D normalization are compared.
Since the processing of brain function occurs on a thin, highly convoluted sheet of cortex along the surface of the brain, there has been much recent interest in studying the structure and function along the brain cortex only, modelled as a 2D manifold. The second part of this thesis proposes an algorithm for highly-flexible deformation in 2D of a template cortex to an individual. The alignment consistency measures developed for 3D are reformulated for the 2D manifold and used to evaluate the algorithm design and numerical parameters. Finally, the question of whether it is better to standardize the 3D images or the 2D cortical manifold is addressed, identifying the problem classes which are best suited to each type of normalization.
Moorman, Andrew(Andrew Robert). „Machine learning inspired synthetic biology: neuromorphic computing in mammalian cells“. Thesis, Massachusetts Institute of Technology, 2020. https://hdl.handle.net/1721.1/129864.
Thesis: S.M., Massachusetts Institute of Technology, Department of Electrical Engineering and Computer Science, February, 2020
Cataloged from student-submitted PDF of thesis.
Includes bibliographical references (pages 109-117).
Synthetic biologists seek to collect, refine, and repackage nature so that it's easier to design new and reliable biological systems, typically at the cellular or multicellular level. These redesigned systems are often referred to as "biological circuits," for their ability to perform operations on biomolecular signals, rather than electrical signals, and for their aim to behave as predictably and modularly as would integrated circuits in a computer. In natural and synthetic biological systems, the abstraction of these circuits' behaviors to digital computation is often appropriate, especially in decision-making settings wherein the output is selected to coordinate a discrete set of outcomes, e.g. developmental networks or disease-state classication circuits. However, there are challenges in engineering entire genetic systems that mimic digital logic.
Biological molecules do not generally exist at only two possible concentrations but vary over an analog range of concentrations, and are ordinarily uncompartmentalized in the cell. As a result, scaling biological circuits which rely on digital logic schemes can prove difficult in practice. Neuromorphic devices represent a promising computing paradigm which aims to reproduce desirable, high-level characteristics inspired by how the brain processes information - features like tunable signal processing and resource ecient scaling. They are a versatile substrate for computation, and, in engineered biological systems, marry the practical benefits of digital and analog signal processing. As the decision-making intelligence of engineered-cell therapies, neuromorphic gene circuits could replace digital logic schemes with a modular and reprogrammable analog template, allowing for more sophisticated computation using fewer resources.
This template could then be adapted either externally or autonomously in long-term single cell medicine. Here, I describe the implementation of in-vivo neuromorphic circuits in human cell culture models as a proof-of-concept for their application to personalized medicine. While biology has long served as inspiration for the artificial intelligence community, this work will help launch a new, interactive relationship between the two fields, in which nature offers more to AI than a helpful metaphor. Synthetic biology provides a rigorous framework to actively probe how learning systems work in living things, closing the loop between traditional machine learning and naturally intelligent systems. This thesis offers a starting point from which to pursue cell therapeutic strategies and multi-step genetic differentiation programs, while exposing the inherent learning capabilities of biology (e.g., self-repair, operation in noisy environments, etc.).
Simultaneously, the results included lay groundwork to analyze the role of machine learning in medicine, where its difficult interpretability contradicts the need to guarantee stable, safe, and efficacious therapies. This thesis should not only spur future research in the use of these approaches for personalized medicine, but also broaden the landscape of academics who nd interest in and relevance to its concerns.
by Andrew Moorman.
S.M.
S.M.
S.M. Massachusetts Institute of Technology, Department of Architecture
S.M. Massachusetts Institute of Technology, Department of Electrical Engineering and Computer Science
Eydgahi, Hoda. „A quantitative framework For large-scale model estimation and discrimination In systems biology“. Thesis, Massachusetts Institute of Technology, 2013. http://hdl.handle.net/1721.1/82347.
Cataloged from PDF version of thesis.
Includes bibliographical references (p. 103-111).
Using models to simulate and analyze biological networks requires principled approaches to parameter estimation and model discrimination. We use Bayesian and Monte Carlo methods to recover the full probability distributions of free parameters (initial protein concentrations and rate constants) for mass action models of receptor-mediated cell death. The width of the individual parameter distributions is largely determined by non-identifiability but co-variation among parameters, even those that are poorly determined, encodes essential information. Knowledge of joint parameter distributions makes it possible to compute the uncertainty of model-based predictions whereas ignoring it (e.g. by treating parameters as a simple list of values and variances) yields nonsensical predictions. Computing the Bayes factor from joint distributions yields the odds ratio (~20-fold) for competing "direct" and "indirect" apoptosis models having different numbers of parameters. The methods presented in this thesis were then extended to make predictions in eight apoptosis mini-models. Despite topological uncertainty, the simulated predictions can be used to drive experimental design. Our results illustrate how Bayesian approaches to model calibration and discrimination combined with single-cell data represent a generally useful and rigorous approach to discriminating between competing hypotheses in the face of parametric and topological uncertainty.
by Hoda Eydgahi.
Ph.D.
Fasani, Rick Anthony. „From Genotype to Phenotype| How Molecular Mechanisms and Environmental Stress Dictate Cell Fate“. Thesis, University of California, Davis, 2013. http://pqdtopen.proquest.com/#viewpdf?dispub=3565502.
In bacteria, stress can produce a variety of distinct phenotypes, including competence, sporulation, persistence, dormancy, and lysis. Yet despite a veritable mountain of genomic data and a growing understanding of the molecular mechanisms, characterizing the rules that dictate the expressed phenotype remains a challenge. This work bridges the gap for three systems at the heart of bacterial stress response: toxin-antitoxin regulation under stress-induced proteolysis, amino acid biosynthesis subject to starvation and the stringent response, and lysogen induction triggered by DNA damage. In each case, a model of the molecular mechanisms is analyzed using novel techniques, and the results quantitatively, rather than qualitatively, describe the kinetic or environmental changes that produce distinct phenotypic behaviors. The results agree with published experiments, answer several open questions, and offer new insights into the links between molecular mechanisms, stress, and cell fate. The core process—the construction and analysis of the system design space—is formalized and automated, offering a tantalizing glimpse of the future in which the full phenotypic repertoire of a system can be predicted and explored.
Yokoo, Rayka 1980. „Biological and computational tools for systems biology : application to Fas signaling pathways in T cells“. Thesis, Massachusetts Institute of Technology, 2004. http://hdl.handle.net/1721.1/18002.
Includes bibliographical references (leaves 43-44).
With the development of new experimental technologies, biologists have begun to take a more global view into cell function, approaching its study in a more systematic manner than previously possible. This thesis develops three new tools to perform systems biology studies of cell death in T cells: A modeling program, JDesigner; high throughput T cell apoptosis assays; and an RNAi sequence prediction program. These tools are then applied to a biological and mathematical analysis of Fas signaling pathways in T cells.
by Rayka Yokoo.
M.Eng.
Kuntala, Prashant Kumar. „Optimizing Biomarkers From an Ensemble Learning Pipeline“. Ohio University / OhioLINK, 2017. http://rave.ohiolink.edu/etdc/view?acc_num=ohiou1503592057943043.
Homer, Daniel C. „Population Fit Threshold: Fully Automated Signal Map generation for Baseline Correction in NMR-based Metabolomics“. Wright State University / OhioLINK, 2010. http://rave.ohiolink.edu/etdc/view?acc_num=wright1271689072.
Alom, Md Zahangir. „Improved Deep Convolutional Neural Networks (DCNN) Approaches for Computer Vision and Bio-Medical Imaging“. University of Dayton / OhioLINK, 2018. http://rave.ohiolink.edu/etdc/view?acc_num=dayton1541685818030003.
Garcia, Krystine. „Bioinformatics Pipeline for Improving Identification of Modified Proteins by Neutral Loss Peak Filtering“. Ohio University / OhioLINK, 2015. http://rave.ohiolink.edu/etdc/view?acc_num=ohiou1440157843.
Martinez, Juan Carlos. „Towards the Prediction of Mutations in Genomic Sequences“. FIU Digital Commons, 2013. http://digitalcommons.fiu.edu/etd/987.
Rajendran, Balakumar. „3D Agent Based Model of Cell Growth“. Cincinnati, Ohio : University of Cincinnati, 2009. http://www.ohiolink.edu/etd/view.cgi?acc_num=ucin1231358178.
Advisors: Carla Purdy PhD (Committee Chair), Daria Narmoneva PhD (Committee Member), Ali Minai PhD (Committee Member). Title from electronic thesis title page (viewed April 30, 2009). Includes abstract. Keywords: Agent based modeling; cell growth; three dimensional. Includes bibliographical references.
Wang, Xiangxue. „A PROGNOSTIC AND PREDICTIVE COMPUTATIONAL PATHOLOGY BASED COMPANION DIAGNOSTIC APPROACH: PRECISION MEDICINE FOR LUNG CANCER“. Case Western Reserve University School of Graduate Studies / OhioLINK, 2019. http://rave.ohiolink.edu/etdc/view?acc_num=case1574125440501667.
Chaumpanich, Kritsakorn. „Kinect™ Based Biology Education System“. University of Akron / OhioLINK, 2015. http://rave.ohiolink.edu/etdc/view?acc_num=akron1427864008.
Woods, Brent J. „Computer-Aided Detection of Malignant Lesions in Dynamic Contrast Enhanced MRI Breast and Prostate Cancer Datasets“. The Ohio State University, 2008. http://rave.ohiolink.edu/etdc/view?acc_num=osu1218155270.
Braman, Nathaniel. „Novel Radiomics and Deep Learning Approaches Targeting the Tumor Environment to Predict Response to Chemotherapy“. Case Western Reserve University School of Graduate Studies / OhioLINK, 2020. http://rave.ohiolink.edu/etdc/view?acc_num=case1586546527544791.
Jaykumar, Nishita. „ResQu: A Framework for Automatic Evaluation of Knowledge-Driven Automatic Summarization“. Wright State University / OhioLINK, 2016. http://rave.ohiolink.edu/etdc/view?acc_num=wright1464628801.
Ramraj, Varun. „Exploiting whole-PDB analysis in novel bioinformatics applications“. Thesis, University of Oxford, 2014. http://ora.ox.ac.uk/objects/uuid:6c59c813-2a4c-440c-940b-d334c02dd075.
Pickrell, Nathan. „Efficiently managing the computer engineering and Computer Science labs“. Thesis, California State University, Long Beach, 2013. http://pqdtopen.proquest.com/#viewpdf?dispub=1522647.
University lab environments are handled differently than corporate, government, and commercial Information Technology (IT) environments. While all environments have the common issues of scalability and cross-platform interoperability, educational lab environments must additionally handle student permissions, student files, student printing, and special education labs. The emphasis is on uniformity across lab machines for a uniform course curriculum.
This thesis construes how a specific set of Computer Science labs are maintained. It describes how documentation is maintained, how the lab infrastructure is setup, how the technicians managing the lab build master lab images, how all of the workstations in the lab are cloned, and how a portion of the maintenance is handled. Additionally, this paper also describes some of the specialty labs provided for courses with functional topics.
Wang, Yuepeng. „Integrative methods for gene data analysis and knowledge discovery on the case study of KEDRI's brain gene ontology a thesis submitted to Auckland University of Technology in partial fulfilment of the requirements for the degree of Master of Computer and Information sciences, 2008 /“. Click here to access this resource online, 2008. http://hdl.handle.net/10292/467.
Chen, Jonathan Jun Feng. „Data Mining/Machine Learning Techniques for Drug Discovery: Computational and Experimental Pipeline Development“. University of Akron / OhioLINK, 2018. http://rave.ohiolink.edu/etdc/view?acc_num=akron1524661027035591.
Patel, Gajendra. „Implementing and Evaluating MQLAIP: A Metabolism Query Language“. Case Western Reserve University School of Graduate Studies / OhioLINK, 2010. http://rave.ohiolink.edu/etdc/view?acc_num=case1289591644.
Gill, Mandeep Singh. „Application of software engineering methodologies to the development of mathematical biological models“. Thesis, University of Oxford, 2013. http://ora.ox.ac.uk/objects/uuid:35178f3a-7951-4f1c-aeab-390cdd622b05.
Mosaliganti, Kishore Rao. „Microscopy Image Analysis Algorithms for Biological Microstructure Characterization“. The Ohio State University, 2008. http://rave.ohiolink.edu/etdc/view?acc_num=osu1211390127.
Kumar, Vivek. „Computational Prediction of Protein-Protein Interactions on the Proteomic Scale Using Bayesian Ensemble of Multiple Feature Databases“. University of Akron / OhioLINK, 2011. http://rave.ohiolink.edu/etdc/view?acc_num=akron1322489637.
Xi, Jiahe. „Cardiac mechanical model personalisation and its clinical applications“. Thesis, University of Oxford, 2013. http://ora.ox.ac.uk/objects/uuid:0db4cf52-4f64-4ee0-8933-3fb49d64aee6.
Nagavaram, Ashish. „Cloud Based Dynamic Workflow with QOS For Mass Spectrometry Data Analysis“. The Ohio State University, 2011. http://rave.ohiolink.edu/etdc/view?acc_num=osu1322681210.
Pappada, Scott Michael. „Prediction of Glucose for Enhancement of Treatment and Outcome: A Neural Network Model Approach“. Toledo, Ohio : University of Toledo, 2010. http://rave.ohiolink.edu/etdc/view?acc%5Fnum=toledo1271302208.
Typescript. "Submitted to the Graduate Faculty as partial fulfillment of the requirements for the Doctor of Philosophy Degree in Engineering." "A dissertation entitled"--at head of title. Title from title page of PDF document. Bibliography: p. 191-212.
Kiritchenko, Svetlana. „Hierarchical text categorization and its application to bioinformatics“. Thesis, University of Ottawa (Canada), 2006. http://hdl.handle.net/10393/29298.
Vieri, Carlin James. „Reversible computer engineering and architecture“. Thesis, Massachusetts Institute of Technology, 1999. http://hdl.handle.net/1721.1/80144.
Includes bibliographical references (p. 162-165).
by Carlin James Vieri.
Ph.D.
Klasson, Filip, und Patrik Väyrynen. „Development of an API for creating and editing openEHR archetypes“. Thesis, Linköping University, Department of Biomedical Engineering, 2009. http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-17558.
Archetypes are used to standardize a way of creating, presenting and distributing health care data. In this master thesis project the open specifications of openEHR was followed. The objective of this master thesis project has been to develop a Java based API for creating and editing openEHR archetypes. The API is a programming toolbox that can be used when developing archetype editors. Another purpose has been to implement validation functionality for archetypes. An important aspect is that the functionality of the API is well documented, this is important to ease the understanding of the system for future developers. The result was a Java based API that is a platform for future archetype editors. The API-kernel has optional immutability so developed archetypes can be locked for modification by making them immutable. The API is compatible with the openEHR specifications 1.0.1, it can load and save archetypes in ADL (Archetype Definition Language) format. There is also a validation feature that verifies that the archetype follows the right structure with respect to predefined reference models. This master thesis report also presents a basic GUI proposal.
Morcos, Karim M. „Genetic network parameter estimation using single and multi-objective particle swarm optimization“. Thesis, Kansas State University, 2011. http://hdl.handle.net/2097/9207.
Department of Electrical and Computer Engineering
Sanjoy Das
Stephen M. Welch
Multi-objective optimization problems deal with finding a set of candidate optimal solutions to be presented to the decision maker. In industry, this could be the problem of finding alternative car designs given the usually conflicting objectives of performance, safety, environmental friendliness, ease of maintenance, price among others. Despite the significance of this problem, most of the non-evolutionary algorithms which are widely used cannot find a set of diverse and nearly optimal solutions due to the huge size of the search space. At the same time, the solution set produced by most of the currently used evolutionary algorithms lacks diversity. The present study investigates a new optimization method to solve multi-objective problems based on the widely used swarm-intelligence approach, Particle Swarm Optimization (PSO). Compared to other approaches, the proposed algorithm converges relatively fast while maintaining a diverse set of solutions. The investigated algorithm, Partially Informed Fuzzy-Dominance (PIFD) based PSO uses a dynamic network topology and fuzzy dominance to guide the swarm of dominated solutions. The proposed algorithm in this study has been tested on four benchmark problems and other real-world applications to ensure proper functionality and assess overall performance. The multi-objective gene regulatory network (GRN) problem entails the minimization of the coefficient of variation of modified photothermal units (MPTUs) across multiple sites along with the total sum of similarity background between ecotypes. The results throughout the current research study show that the investigated algorithm attains outstanding performance regarding optimization aspects, and exhibits rapid convergence and diversity.