Дисертації з теми "Analysis of biological data"
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Droop, Alastair Philip. "Correlation Analysis of Multivariate Biological Data." Thesis, University of York, 2009. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.507622.
Повний текст джерелаMcCormick, Paul Stephen. "Statistical analysis of biological expression data." Thesis, University of Cambridge, 2006. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.613819.
Повний текст джерелаHasegawa, Takanori. "Reconstructing Biological Systems Incorporating Multi-Source Biological Data via Data Assimilation Techniques." 京都大学 (Kyoto University), 2015. http://hdl.handle.net/2433/195985.
Повний текст джерелаWaterworth, Alan Richard. "Data analysis techniques of measured biological impedance." Thesis, University of Sheffield, 2000. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.340146.
Повний текст джерелаBecker, Katinka [Verfasser]. "Logical Analysis of Biological Data / Katinka Becker." Berlin : Freie Universität Berlin, 2021. http://d-nb.info/1241541779/34.
Повний текст джерелаREHMAN, HAFEEZ UR. "Integration and Analysis of Heterogeneous Biological Data." Doctoral thesis, Politecnico di Torino, 2014. http://hdl.handle.net/11583/2537092.
Повний текст джерелаLi, Yehua. "Topics in functional data analysis with biological applications." [College Station, Tex. : Texas A&M University, 2006. http://hdl.handle.net/1969.1/ETD-TAMU-1867.
Повний текст джерелаChen, Li. "Integrative Modeling and Analysis of High-throughput Biological Data." Diss., Virginia Tech, 2010. http://hdl.handle.net/10919/30192.
Повний текст джерелаPh. D.
Causey, Jason L. "Studying Low Complexity Structures in Bioinformatics Data Analysis of Biological and Biomedical Data." Thesis, University of Arkansas at Little Rock, 2018. http://pqdtopen.proquest.com/#viewpdf?dispub=10750808.
Повний текст джерелаBiological, biomedical, and radiological data tend to be large, complex, and noisy. Gene expression studies contain expression levels for thousands of genes and hundreds or thousands of patients. Chest Computed Tomography images used for diagnosing lung cancer consist of hundreds of 2-D image ”slices”, each containing hundreds of thousands of pixels. Beneath the size and apparent complexity of many of these data are simple and sparse structures. These low complexity structures can be leveraged into new approaches to biological, biomedical, and radiological data analyses. Two examples are presented here. First, a new framework SparRec (Sparse Recovery) for imputation of GWAS data, based on a matrix completion (MC) model taking advantage of the low-rank and low number of co-clusters of GWAS matrices. SparRec is flexible enough to impute meta-analyses with multiple cohorts genotyped on different sets of SNPs, even without a reference panel. Compared with Mendel-Impute, another MC method, our low-rank based method achieves similar accuracy and efficiency even with up to 90% missing data; our co-clustering based method has advantages in running time. MC methods are shown to have advantages over statistics-based methods, including Beagle and fastPhase. Second, we demonstrate NoduleX, a method for predicting lung nodule malignancy from chest Computed Tomography (CT) data, based on deep convolutional neural networks. For training and validation, we analyze >1000 lung nodules in images from the LIDC/IDRI cohort and compare our results with classifications provided by four experienced thoracic radiologists who participated in the LIDC project. NoduleX achieves high accuracy for nodule malignancy classification, with an AUC of up to 0.99, commensurate with the radiologists’ analysis. Whether they are leveraged directly or extracted using mathematical optimization and machine learning techniques, low complexity structures provide researchers with powerful tools for taming complex data.
Zandegiacomo, Cella Alice. "Multiplex network analysis with application to biological high-throughput data." Master's thesis, Alma Mater Studiorum - Università di Bologna, 2016. http://amslaurea.unibo.it/10495/.
Повний текст джерелаNarayan, Chaya. "Study of Optically Active Biological Fluids Using Polarimetric Data Analysis." University of Akron / OhioLINK, 2011. http://rave.ohiolink.edu/etdc/view?acc_num=akron1314038487.
Повний текст джерелаBarnicki, Steven Louis. "An integrated data acquisition and analysis system for biological signals /." The Ohio State University, 1989. http://rave.ohiolink.edu/etdc/view?acc_num=osu1487599963593257.
Повний текст джерелаWirth, Henry. "Analysis of large-scale molecular biological data using self-organizing maps." Doctoral thesis, Universitätsbibliothek Leipzig, 2012. http://nbn-resolving.de/urn:nbn:de:bsz:15-qucosa-101298.
Повний текст джерелаGünther, Clara-Cecilie. "Statistical analysis of biological data – diagnostic tests, geneontology and gene expression." Doctoral thesis, Norges teknisk-naturvitenskapelige universitet, Institutt for matematiske fag, 2009. http://urn.kb.se/resolve?urn=urn:nbn:no:ntnu:diva-5748.
Повний текст джерелаUhlmann, Johannes [Verfasser], and Rolf [Akademischer Betreuer] Niedermeier. "Multivariate Algorithmics in Biological Data Analysis / Johannes Uhlmann. Betreuer: Rolf Niedermeier." Berlin : Universitätsbibliothek der Technischen Universität Berlin, 2011. http://d-nb.info/1014971853/34.
Повний текст джерелаScelfo, Tony (Tony W. ). "Data visualization of biological microscopy image analyses." Thesis, Massachusetts Institute of Technology, 2006. http://hdl.handle.net/1721.1/37073.
Повний текст джерелаIncludes bibliographical references.
The Open Microscopy Environment (OME) provides biologists with a framework to store, analyze and manipulate large sets of image data. Current microscopes are capable of generating large numbers of images and when coupled with automated analysis routines, researchers are able to generate intractable sets of data. I have developed an extension to the OME toolkit, named the LoViewer, which allows researchers to quickly identify clusters of images based on relationships between analytically measured parameters. By identifying unique subsets of data, researchers are able to make use of the rest of the OME client software to view interesting images in high resolution, classify them into category groups and apply further analysis routines. The design of the LoViewer itself and its integration with the rest of the OME toolkit will be discussed in detail in body of this thesis.
by Tony Scelfo.
M.Eng.and S.B.
Ge, Tian. "Some novel models and methods for neuroimaging data analysis." Thesis, University of Warwick, 2013. http://wrap.warwick.ac.uk/58416/.
Повний текст джерелаde, Vito Roberta. "Multi-study factor models for high-dimensional biological data." Doctoral thesis, Università degli studi di Padova, 2016. http://hdl.handle.net/11577/3424398.
Повний текст джерелаLe analisi scientifiche su un alto numero di campioni (high-throughput assays) stanno trasformando gli studi biologici. In particolare gli high-throughput assays generano una ricca, complessa e varia collezione di dati a più dimensioni. Estrarre informazioni significative in maniera sistematica da questo tipo di dati richiede un processo progressivo che si basa sull’analisi simultanea di risorse, studi e tecnologie differenti. La crescente disponibilità di numerosi studi clinici su rilevanti gruppi, popolazioni e diversi studi genetici genera due categorie: la prima, una categoria relativa ai fattori condivisi da tutti gli studi ed una seconda, relativa a fattori specifici di ogni studio. Per catturare queste due differenti categorie abbiamo proposto, nell'ambito di tale tesi, una nuova classe di modellizzazione di analisi fattoriale che abbiamo sviluppato in un approccio sia frequentista che Bayesiano. Nell'approccio frequentista, è stato proposto un algoritmo ECM per la stima di massima verosimiglianza dei parametri. Inoltre, in questa tesi, si è proposto un approccio Bayesiano per adattare questo modello ad un contesto di più variabili che soggetti, p>n. Nel modellizzare la dipendenza tra variabili, si è assunta una struttura sparsa per sottolineare le associazioni tra i geni. Entrambi i metodi hanno consentito di modellizzare i diversi studi. Inoltre, i risultati hanno permesso di poter identificare un segnale biologico riproducibile e comune in tutti gli studi, nonché ad eliminare quella parte di varianza che oscura questo segnale.
Handl, Julia [Verfasser]. "Multiobjective approaches to the data-driven analysis of biological systems / Julia Handl." Aachen : Shaker, 2006. http://d-nb.info/1166508315/34.
Повний текст джерелаPettit, Jean-Baptiste Olivier Georges. "Spatial analysis of complex biological tissues from single cell gene expression data." Thesis, University of Cambridge, 2015. https://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.708750.
Повний текст джерелаZhang, Yuji. "Module-based Analysis of Biological Data for Network Inference and Biomarker Discovery." Diss., Virginia Tech, 2010. http://hdl.handle.net/10919/28482.
Повний текст джерелаPh. D.
Kelley, Ryan Matthew. "The analysis and integration of high-throughput biological data for pathway discovery." Diss., Connect to a 24 p. preview or request complete full text in PDF format. Access restricted to UC campuses, 2009. http://wwwlib.umi.com/cr/ucsd/fullcit?p3341729.
Повний текст джерелаTitle from first page of PDF file (viewed February 6, 2009). Available via ProQuest Digital Dissertations. Vita. Includes bibliographical references (p. 153-170).
Lu, Yingzhou. "Multi-omics Data Integration for Identifying Disease Specific Biological Pathways." Thesis, Virginia Tech, 2018. http://hdl.handle.net/10919/83467.
Повний текст джерелаMaster of Science
Riley, Michael. "Significant pattern discovery in gene location and phylogeny." Thesis, Aberystwyth University, 2009. http://hdl.handle.net/2160/fbabd607-ae86-44ed-a3f1-9eb2c461da32.
Повний текст джерелаMosquera, Mayo José Luís. "Methods and Models for the Analysis of Biological Signifïcance Based on HighThroughput Data." Doctoral thesis, Universitat de Barcelona, 2014. http://hdl.handle.net/10803/286465.
Повний текст джерелаL'aparició de les tecnologies d'alt rendiment ha generat una quantitat ingent de dades òmiques. Els resultats d'aquests experiment són llargues llistes de gens, que poden ser utilitzats com a biomarcadors. Un dels grans reptes dels investigadors experimentals és atribuir una interpretació o significació biològica a aquests biomarcadors potencials, ja be sigui extraient la informació bioblògica emmagatzemada en recursos com la Gene Ontology (GO) o la Kyoto Encyclopedia of Genes and Genomes (KEGG), o be combinant-les amb altres dades òmiques. Els objectius de la tesis eren: primer, estudiar les propietats matemàtiques de dos tipus de mesures de similaritat semàntica per a explorar categories GO, i segon, classificar i estudiar l'evolució de les eines GO per a l'anàlisi d'enriquiment. La primera mesura de similaritat semàntica considerada, proposada per en Lord et al., es fonamentava en la teoria de grafs, i la segona era un grup de pseudo-distàncies, proposades per Joslyn et al., fonamentades en la teoria dels Partially Ordered Sets (POSETs). L'estudi de les eines GO es va basar en les primeres 26 eines disponibles al web del The GO Consortium. S'ha vist que la mesura d'en Lord et al. és la mateixa mesura que la d'en Resnik, anteriorment publicada. S'ha observat una analogia en la forma de mapejar els gens a la GO via grafs i/o via POSETs. S'han proposat una propietat i un corol·lari que permeten calcular matricialment les la primera mesura de similaritat semàntica. S'ha demostrat que ambdues mesures estan associades a la distància mètrica. A'ha desenvolupat un paquet R, anomenat sims, que permet calcular similaritats semàntiques d'una ontologia arbitraria i comparar perfils de similaritat semàntica de la GO. S'ha proposat un Conjunt de Funcionalitats Estàndard per a classificar eines GO i s'ha desenvolupat un programari web, anomenat SerbGO, dirigit a seleccionar i comparar eines GO. L'estudi estadístic ha revelat que els promotors de les eines GO han introduït millores al llarg del temps, però no s'han detectat models ben definits. S'ha desenvolupat una ontologia, anomenada DeGOT, que proporciona un vocabulari als desenvolupadors per a introduir millores a les eines o dissenyar una de nova.
Georgi, Benjamin [Verfasser]. "Context-specific independence mixture models for cluster analysis of biological data / Benjamin Georgi." Berlin : Freie Universität Berlin, 2009. http://d-nb.info/102366402X/34.
Повний текст джерелаYang, Karren Dai. "Learning causal graphs under interventions and applications to single-cell biological data analysis." Thesis, Massachusetts Institute of Technology, 2021. https://hdl.handle.net/1721.1/130806.
Повний текст джерелаThesis: S.M., Massachusetts Institute of Technology, Department of Electrical Engineering and Computer Science, February, 2021
Cataloged from the official PDF version of thesis.
Includes bibliographical references (pages 49-51).
This thesis studies the problem of learning causal directed acyclic graphs (DAGs) in the setting where both observational and interventional data is available. This setting is common in biology, where gene regulatory networks can be intervened on using chemical reagents or gene deletions. The identifiability of causal DAGs under perfect interventions, which eliminate dependencies between targeted variables and their direct causes, has previously been studied. This thesis first extends these identifiability results to general interventions, which may modify the dependencies between targeted variables and their causes without eliminating them, by defining and characterizing the interventional Markov equivalence class that can be identified from general interventions. Subsequently, this thesis proposes the first provably consistent algorithm for learning DAGs in this setting. Finally, this algorithm as well as related work is applied to analyze biological datasets.
by Karren Dai Yang.
S.M.
S.M.
S.M. Massachusetts Institute of Technology, Department of Biological Engineering
S.M. Massachusetts Institute of Technology, Department of Electrical Engineering and Computer Science
Chittenden, Thomas William. "Quantitative integration of biological knowledge for the analysis of high-throughput genomic data." Thesis, University of Oxford, 2012. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.559860.
Повний текст джерелаGhazanfar, Shila. "Statistical approaches to harness high throughput sequencing data in diverse biological systems." Thesis, The University of Sydney, 2017. http://hdl.handle.net/2123/17268.
Повний текст джерелаFellenberg, Matthias. "A bioinformatic approach to the metabolic and functional analysis of biological high throughput data." [S.l. : s.n.], 2002. http://deposit.ddb.de/cgi-bin/dokserv?idn=966129822.
Повний текст джерелаLeader, Debbie. "Methods for incorporating biological information into the statistical analysis of gene expression microarray data." Thesis, University of Auckland, 2009. http://hdl.handle.net/2292/5609.
Повний текст джерелаJeanmougin, Marine. "Statistical methods for robust analysis of transcriptome data by integration of biological prior knowledge." Thesis, Evry-Val d'Essonne, 2012. http://www.theses.fr/2012EVRY0029/document.
Повний текст джерелаRecent advances in Molecular Biology have led biologists toward high-throughput genomic studies. In particular, the investigation of the human transcriptome offers unprecedented opportunities for understanding cellular and disease mechanisms. In this PhD, we put our focus on providing robust statistical methods dedicated to the treatment and the analysis of high-throughput transcriptome data. We discuss the differential analysis approaches available in the literature for identifying genes associated with a phenotype of interest and propose a comparison study. We provide practical recommendations on the appropriate method to be used based on various simulation models and real datasets. With the eventual goal of overcoming the inherent instability of differential analysis strategies, we have developed an innovative approach called DiAMS, for DIsease Associated Modules Selection. This method was applied to select significant modules of genes rather than individual genes and involves the integration of both transcriptome and protein interactions data in a local-score strategy. We then focus on the development of a framework to infer gene regulatory networks by integration of a biological informative prior over network structures using Gaussian graphical models. This approach offers the possibility of exploring the molecular relationships between genes, leading to the identification of altered regulations potentially involved in disease processes. Finally, we apply our statistical developments to study the metastatic relapse of breast cancer
Curry, Edward William James. "Mining large collections of gene expression data to elucidate transcriptional regulation of biological processes." Thesis, University of Edinburgh, 2011. http://hdl.handle.net/1842/9437.
Повний текст джерелаHe, Xin. "A semi-automated framework for the analytical use of gene-centric data with biological ontologies." Thesis, University of Edinburgh, 2017. http://hdl.handle.net/1842/25505.
Повний текст джерелаPetrizzelli, Marianyela. "Mathematical modelling and integration of complex biological data : analysis of the heterosis phenomenon in yeast." Thesis, Université Paris-Saclay (ComUE), 2019. http://www.theses.fr/2019SACLS204/document.
Повний текст джерелаThe general framework of this thesis is the issue of the genotype-phenotype relationship, through the analysis of the heterosis phenomenon in yeast, in an approach combining biology, mathematics and statistics. Prior to this work, a very large set of heterogeneous data, corresponding to different levels of organization (proteomics, fermentation and life history traits), had been collected on a semi-diallel design involving 11 strains belonging to two species. This type of data is ideally suited for multi-scale modelling and for testing models for predicting the variation of integrated phenotypes from protein and metabolic (flux) traits, taking into account dependence patterns between variables and between observations. I first decomposed, for each trait, the total genetic variance into variances of additive, inbreeding and heterosis effects, and showed that the distribution of these components made it possible to define well-defined groups of proteins in which most of the characters of fermentation and life history traits took place. Within these groups, the correlations between the variances of heterosis and inbreeding effects could be positive, negative or null, which was the first experimental demonstration of a possible decoupling between the two phenomena. The second part of the thesis consisted of interfacing quantitative proteomic data with the yeast genome-scale metabolic model using a constraint-based modelling approach. Using a recent algorithm, I looked, in the space of possible solutions, for the one that minimized the distance between the flux vector and the vector of the observed abundances of proteins. I was able to predict unobserved fluxes, and to compare correlation patterns at different integration levels. Data allowed to distinguish between two major types of fermentation or life history traits whose biochemical interpretation is consistent in terms of trade-off, and which had not been highlighted from quantitative proteomic data alone. Altogether, my thesis work allows a better understanding of the evolution of the genotype-phenotype map
Yee, Thomas William. "The Analysis of binary data in quantitative plant ecology." Thesis, University of Auckland, 1993. http://hdl.handle.net/2292/1973.
Повний текст джерелаWhole document restricted, but available by request, use the feedback form to request access.
Wu, Chiung Ting. "Machine Learning Approaches for Modeling and Correction of Confounding Effects in Complex Biological Data." Diss., Virginia Tech, 2021. http://hdl.handle.net/10919/103739.
Повний текст джерелаDoctor of Philosophy
Due to the complexity of biological data, there are two major pre-processing steps: alignment and deconvolution. The alignment step corrects the time and location related data acquisition distortion by aligning the detected signals to a reference signal. Though many alignment methods are proposed for biological data, most of them fail to consider the relationships among samples carefully. This piece of structure information can help alignment when the data is noisy and/or irregular. To utilize this information, we develop a new method, Neighbor-wise Compound-specific Graphical Time Warping (ncGTW), inspired by graph theory. This new alignment method not only utilizes the structural information but also provides a reference-free solution. We show that the performance of our new method is better than other methods in both simulations and real datasets. When the signal is from a mixture, deconvolution is needed to recover the pure sources. Many biological questions can be better addressed when the data is in the form of single sources, instead of mixtures. There is a classic unsupervised deconvolution method: Convex Analysis of Mixtures (CAM). However, there are some limitations of this method. For example, the time complexity of some steps is very high. Thus, when facing a large dataset or a dataset with many sources, the computation time would be extremely long. Also, since there are some stochastic and heuristic steps, the deconvolution result may be not accurate enough. We improved CAM and the experimental results show that the speed and accuracy of the deconvolution is significantly improved.
Winter, Eitan E. "Evolutionary analyses of protein-coding genes using large biological data sets." Thesis, University of Oxford, 2006. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.427615.
Повний текст джерелаHanisch, Daniel [Verfasser]. "New Analysis Methods for Gene Expression Data via Construction and Incorporation of Biological Networks / Daniel Hanisch." Aachen : Shaker, 2005. http://d-nb.info/1181615232/34.
Повний текст джерелаSun, Guoli. "Significant distinct branches of hierarchical trees| A framework for statistical analysis and applications to biological data." Thesis, State University of New York at Stony Brook, 2015. http://pqdtopen.proquest.com/#viewpdf?dispub=3685086.
Повний текст джерелаOne of the most common goals of hierarchical clustering is finding those branches of a tree that form quantifiably distinct data subtypes. Achieving this goal in a statistically meaningful way requires (a) a measure of distinctness of a branch and (b) a test to determine the significance of the observed measure, applicable to all branches and across multiple scales of dissimilarity.
We formulate a method termed Tree Branches Evaluated Statistically for Tightness (TBEST) for identifying significantly distinct tree branches in hierarchical clusters. For each branch of the tree a measure of distinctness, or tightness, is defined as a rational function of heights, both of the branch and of its parent. A statistical procedure is then developed to determine the significance of the observed values of tightness. We test TBEST as a tool for tree-based data partitioning by applying it to five benchmark datasets, one of them synthetic and the other four each from a different area of biology. With each of the five datasets, there is a well-defined partition of the data into classes. In all test cases TBEST performs on par with or better than the existing techniques.
One dataset uses Cores Of Recurrent Events (CORE) to select features. CORE was developed with my participation in the course of this work. An R language implementation of the method is available from the Comprehensive R Archive Network: cran.r-project.org/web/packages/CORE/index.html.
Based on our benchmark analysis, TBEST is a tool of choice for detection of significantly distinct branches in hierarchical trees grown from biological data. An R language implementation of the method is available from the Comprehensive R Archive Network: cran.r-project.org/web/packages/TBEST/index.html.
Watzl, June Qiongye. "Improved magnetic resonance spectroscopy data acquisition and processing for the study of biological specimens." Thesis, The University of Sydney, 2001. https://hdl.handle.net/2123/27715.
Повний текст джерелаSARTOR, MAUREEN A. "TESTING FOR DIFFERENTIALLY EXPRESSED GENES AND KEY BIOLOGICAL CATEGORIES IN DNA MICROARRAY ANALYSIS." University of Cincinnati / OhioLINK, 2007. http://rave.ohiolink.edu/etdc/view?acc_num=ucin1195656673.
Повний текст джерелаVerzotto, Davide. "Advanced Computational Methods for Massive Biological Sequence Analysis." Doctoral thesis, Università degli studi di Padova, 2011. http://hdl.handle.net/11577/3426282.
Повний текст джерелаCon l'avvento delle moderne tecnologie di sequenziamento, massive quantità di dati biologici, da sequenze proteiche fino a interi genomi, sono disponibili per la ricerca. Questo progresso richiede l'analisi e la classificazione automatica di tali collezioni di dati, al fine di migliorare la conoscenza nel campo delle Scienze della Vita. Nonostante finora siano stati proposti molti approcci per modellare matematicamente le sequenze biologiche, ad esempio cercando pattern e similarità tra sequenze genomiche o proteiche, questi metodi spesso mancano di strutture in grado di indirizzare specifiche questioni biologiche. In questa tesi, presentiamo nuovi metodi computazionali per tre problemi fondamentali della biologia molecolare: la scoperta di relazioni evolutive remote tra sequenze proteiche, l'individuazione di segnali biologici complessi in siti funzionali tra loro correlati, e la ricostruzione della filogenesi di un insieme di organismi, attraverso la comparazione di interi genomi. Il principale contributo è dato dall'analisi sistematica dei pattern che possono interessare questi problemi, portando alla progettazione di nuovi strumenti computazionali efficaci ed efficienti. Vengono introdotti così due paradigmi avanzati per la scoperta e il filtraggio di pattern, basati sull'osservazione che i motivi biologici funzionali, o pattern, sono localizzati in differenti regioni delle sequenze in esame. Questa osservazione consente di realizzare approcci parsimoniosi in grado di evitare un conteggio multiplo degli stessi pattern. Il primo paradigma considerato, ovvero irredundant common motifs, riguarda la scoperta di pattern comuni a coppie di sequenze che hanno occorrenze non coperte da altri pattern, la cui copertura è definita da una maggiore specificità e/o possibile estensione dei pattern. Il secondo paradigma, ovvero underlying motifs, riguarda il filtraggio di pattern che hanno occorrenze non sovrapposte a quelle di altri pattern con maggiore priorità, dove la priorità è definita da proprietà lessicografiche dei pattern al confine tra pattern matching e analisi statistica. Sono stati sviluppati tre metodi computazionali basati su questi paradigmi avanzati. I risultati sperimentali indicano che i nostri metodi sono in grado di identificare le principali similitudini tra sequenze biologiche, utilizzando l'informazione presente in maniera non ridondante. In particolare, impiegando gli irredundant common motifs e le statistiche basate su questi pattern risolviamo il problema della rilevazione di omologie remote tra proteine. I risultati evidenziano che il nostro approccio, chiamato Irredundant Class, ottiene ottime prestazioni su un benchmark impegnativo, e migliora i metodi allo stato dell'arte. Inoltre, per individuare segnali biologici complessi utilizziamo la nozione di underlying motifs, definendo così alcune modalità per il confronto e il filtraggio di motivi degenerati ottenuti tramite moderni strumenti di pattern discovery. Esperimenti su grandi famiglie proteiche dimostrano che il nostro metodo riduce drasticamente il numero di motivi che gli scienziati dovrebbero altrimenti ispezionare manualmente, mettendo in luce inoltre i motivi funzionali identificati in letteratura. Infine, combinando i due paradigmi proposti presentiamo una nuova e pratica funzione di distanza tra interi genomi. Con il nostro metodo, chiamato Unic Subword Approach, relazioniamo tra loro le diverse regioni di due sequenze genomiche, selezionando i motivi conservati durante l'evoluzione. I risultati sperimentali evidenziano che il nostro approccio offre migliori prestazioni rispetto ad altri metodi allo stato dell'arte nella ricostruzione della filogenesi di organismi quali virus, procarioti ed eucarioti unicellulari, identificando inoltre le sottoclassi principali di queste specie.
Lawrence, Michael. "Interactive graphics, graphical user interfaces and software interfaces for the analysis of biological experimental data and networks." [Ames, Iowa : Iowa State University], 2008.
Знайти повний текст джерелаSingh, Nitesh Kumar [Verfasser]. "Integrating diverse biological sources and computational methods for the analysis of high-throughput expression data / Nitesh Kumar Singh." Greifswald : Universitätsbibliothek Greifswald, 2014. http://d-nb.info/1060136937/34.
Повний текст джерелаBlankenship, James R. "Assessing the ability of hyperspectral data to detect Lyngbya SPP a potential biological indicator for presence of metal objects in the littoral environment." Thesis, Monterey, Calif. : Naval Postgraduate School, 2006. http://bosun.nps.edu/uhtbin/hyperion.exe/06Dec%5FBlankenship.pdf.
Повний текст джерелаThesis Advisor(s): Daria Siciliano, R. C. Olsen. "December 2006." Includes bibliographical references (p. 233-239). Also available in print.
Castellano, Escuder Pol. "Statistical methods for intake prediction and biological significance analysis in nutrimetabolomic studies." Doctoral thesis, Universitat de Barcelona, 2021. http://hdl.handle.net/10803/673827.
Повний текст джерелаEberle, Jonas [Verfasser]. "Reconciling Molecular with Biological and Morphological Data Towards an Integrative Analysis of the Evolutionary Biology of Chafers / Jonas Eberle." Bonn : Universitäts- und Landesbibliothek Bonn, 2018. http://d-nb.info/1163662356/34.
Повний текст джерелаSavelli, Raphaël. "Study of microphytobenthos dynamics in temperate intertidal mudflats by using physical-biological coupled modelling and remote sensing data analysis." Thesis, La Rochelle, 2019. http://www.theses.fr/2019LAROS030.
Повний текст джерелаThe high primary production (PP) of intertidal mudflats at temperate latitudes is mostly supported by microphytobenthos (MPB), which support both benthic and pelagic food webs. In the present thesis, we use a physical-biological coupled model to investigate the spatial and temporal variability of MPB dynamics on a large temperate intertidal mudflat of the French Atlantic coast. The model explicitly simulates the MPB biomass and the grazer (Peringia ulvae) biomass and density. The outputs provide key findings on MPB dynamics. In winter-spring, optimal light and mud surface temperature (MST) conditions for MPB growth lead to a MPB spring bloom. Light is the most limiting driver over the year. However, a high MST limits the MPB growth 40% of the time during summer. The photoinhibition of MPB photosynthesis can potentially superimpose on thermoinhibition in spring-summer. Grazing and resuspension of MPB biomass also shape the dynamics of the MPB biomass. Bioturbation by P. ulvae contributes to a chronic export of MPB biomass from the sediment to the water column in spring-summer. Waves contribute to the MPB resuspension through massive resuspension events in winter, spring and fall. 50% of the annual MPB PP is exported to the water column through chronic and massive resuspension events. We also developed a new method that combine remote sensing data with outputs of the physical-biological coupled model into a single algorithm that can predict PP from satellite data. In addition to bring new insights on the MPB dynamics, this work proposes new numerical tools to monitor and predict MPB PP and its fate in coastal waters in a context of climate change
Andorf, Sandra. "A systems biological approach towards the molecular basis of heterosis in Arabidopsis thaliana." Phd thesis, Universität Potsdam, 2011. http://opus.kobv.de/ubp/volltexte/2011/5117/.
Повний текст джерелаAls Heterosis-Effekt wird die Überlegenheit in einem oder mehreren Leistungsmerkmalen (z.B. Blattgröße von Pflanzen) von heterozygoten (mischerbigen) Nachkommen über deren unterschiedlich homozygoten (reinerbigen) Eltern bezeichnet. Dieses Phänomen ist schon seit Beginn des letzten Jahrhunderts bekannt und wird weit verbreitet in der Pflanzenzucht genutzt. Trotzdem sind die genetischen und molekularen Grundlagen von Heterosis noch weitestgehend unbekannt. Es wird angenommen, dass heterozygote Individuen mehr regulatorische Möglichkeiten aufweisen als ihre homozygoten Eltern und sie somit auf eine größere Anzahl an wechselnden Umweltbedingungen richtig reagieren können. Diese erhöhte Anpassungsfähigkeit führt zum Heterosis-Effekt. In dieser Arbeit wird ein systembiologischer Ansatz, basierend auf molekularen Netzwerkstrukturen verfolgt, um zu einem besseren Verständnis von Heterosis beizutragen. Dazu wird eine Netzwerkhypothese für Heterosis vorgestellt, die vorhersagt, dass die heterozygoten Individuen, die Heterosis zeigen, mehr regulatorische Interaktionen in ihren molekularen Netzwerken aufweisen als die homozygoten Eltern. Partielle Korrelationen wurden verwendet, um diesen Unterschied in den globalen Interaktionsstrukturen zwischen den Heterozygoten und ihren homozygoten Eltern zu untersuchen. Die Netzwerkhypothese wurde anhand von Metabolit- und Genexpressionsdaten der beiden homozygoten Arabidopsis thaliana Pflanzenlinien C24 und Col-0 und deren wechselseitigen Kreuzungen getestet. Arabidopsis thaliana Pflanzen sind bekannt dafür, dass sie einen Heterosis-Effekt im Bezug auf ihre Biomasse zeigen. Die heterozygoten Pflanzen weisen bei gleichem Alter eine höhere Biomasse auf als die homozygoten Pflanzen. Die Netzwerkhypothese für Heterosis konnte sowohl im Bezug auf mid-parent Heterosis (Unterschied in der Leistung des Heterozygoten im Vergleich zum Mittelwert der Eltern) als auch auf best-parent Heterosis (Unterschied in der Leistung des Heterozygoten im Vergleich zum Besseren der Eltern) für beide Kreuzungen für die Metabolit- und Genexpressionsdaten bestätigt werden. In einer Überrepräsentations-Analyse wurden die Gene, für die die größte Veränderung in der Anzahl der regulatorischen Interaktionen, an denen sie vermutlich beteiligt sind, festgestellt wurde, mit den Genen aus einer quantitativ genetischen (QTL) Analyse von Biomasse-Heterosis in Arabidopsis thaliana verglichen. Die ermittelten Gene aus beiden Studien zeigen eine größere Überschneidung als durch Zufall erwartet. Das deutet darauf hin, dass jede identifizierte QTL-Region viele Gene, die den Biomasse-Heterosis-Effekt in Arabidopsis thaliana beeinflussen, enthält. Die Gene, die in den Ergebnislisten beider Analyseverfahren überlappen, können mit größerer Zuversicht als Kandidatengene für Biomasse-Heterosis in Arabidopsis thaliana betrachtet werden als die Ergebnisse von nur einer Studie.