Дисертації з теми "Algorithms- Protein"
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Derevyanko, Georgy. "Structure-based algorithms for protein-protein interactions." Thesis, Grenoble, 2014. http://www.theses.fr/2014GRENY070/document.
Повний текст джерелаThe phenotype of every known living organism is determined mainly by the complicated interactions between the proteins produced in this organism. Understanding the orchestration of the organismal responses to the external or internal stimuli is based on the understanding of the interactions of individual proteins and their complexes structures. The prediction of a complex of two or more proteins is the problem of the protein-protein docking field. Docking algorithms usually have two major steps: exhaustive 6D rigid-body search followed by the scoring. In this work we made contribution to both of these steps. We developed a novel algorithm for 6D exhaustive search, HermiteFit. It is based on Hermite decomposition of 3D functions into the Hermite basis. We implemented this algorithm in the program for fitting low-resolution electron density maps. We showed that it outperforms existing algorithms in terms of time-per-point while maintaining the same output model accuracy. We also developed a novel approach to computation of a scoring function, which is based on simple logical arguments and avoids an ambiguous computation of the reference state. We compared it to the existing scoring functions on the widely used protein-protein docking benchmarks. Finally, we developed an approach to include water-protein interactions into the scoring functions and validated our method during the Critical Assessment of Protein Interactions round 47
Lassmann, Timo. "Algorithms for building and evaluating multiple sequence alignments /." Stockholm, 2006. http://diss.kib.ki.se/2006/91-7140-887-8/.
Повний текст джерелаHosur, Raghavendra. "Structure-based algorithms for protein-protein interaction prediction." Thesis, Massachusetts Institute of Technology, 2012. http://hdl.handle.net/1721.1/75843.
Повний текст джерелаThis electronic version was submitted by the student author. The certified thesis is available in the Institute Archives and Special Collections.
Cataloged from student submitted PDF version of thesis.
Includes bibliographical references (p. 109-124).
Protein-protein interactions (PPIs) play a central role in all biological processes. Akin to the complete sequencing of genomes, complete descriptions of interactomes is a fundamental step towards a deeper understanding of biological processes, and has a vast potential to impact systems biology, genomics, molecular biology and therapeutics. PPIs are critical in maintenance of cellular integrity, metabolism, transcription/ translation, and cell-cell communication. This thesis develops new methods that significantly advance our efforts at structure- based approaches to predict PPIs and boost confidence in emerging high-throughput (HTP) data. The aims of this thesis are, 1) to utilize physicochemical properties of protein interfaces to better predict the putative interacting regions and increase coverage of PPI prediction, 2) increase confidence in HTP datasets by identifying likely experimental errors, and 3) provide residue-level information that gives us insights into structure-function relationships in PPIs. Taken together, these methods will vastly expand our understanding of macromolecular networks. In this thesis, I introduce two computational approaches for structure-based proteinprotein interaction prediction: iWRAP and Coev2Net. iWRAP is an interface threading approach that utilizes biophysical properties specific to protein interfaces to improve PPI prediction. Unlike previous structure-based approaches that use single structures to make predictions, iWRAP first builds profiles that characterize the hydrophobic, electrostatic and structural properties specific to protein interfaces from multiple interface alignments. Compatibility with these profiles is used to predict the putative interface region between the two proteins. In addition to improved interface prediction, iWRAP provides better accuracy and close to 50% increase in coverage on genome-scale PPI prediction tasks. As an application, we effectively combine iWRAP with genomic data to identify novel cancer related genes involved in chromatin remodeling, nucleosome organization and ribonuclear complex assembly - processes known to be critical in cancer. Coev2Net addresses some of the limitations of iWRAP, and provides techniques to increase coverage and accuracy even further. Unlike earlier sequence and structure profiles, Coev2Net explicitly models long-distance correlations at protein interfaces. By formulating interface co-evolution as a high-dimensional sampling problem, we enrich sequence/structure profiles with artificial interacting homologus sequences for families which do not have known multiple interacting homologs. We build a spanning-tree based graphical model induced by the simulated sequences as our interface profile. Cross-validation results indicate that this approach is as good as previous methods at PPI prediction. We show that Coev2Net's predictions correlate with experimental observations and experimentally validate some of the high-confidence predictions. Furthermore, we demonstrate how analysis of the predicted interfaces together with human genomic variation data can help us understand the role of these mutations in disease and normal cells.
by Raghavendra Hosur.
Ph.D.
Bazzoli, A. "Protein structure prediction and protein design with evolutionary algorithms." Doctoral thesis, Università degli Studi di Milano, 2009. http://hdl.handle.net/2434/64478.
Повний текст джерелаLappe, Michael. "Novel algorithms for protein interaction networks." Thesis, University of Cambridge, 2004. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.615625.
Повний текст джерелаSajjadi, Sajdeh [Verfasser]. "Step by step in fast protein-protein docking algorithms / Sajdeh Sajjadi." Lübeck : Zentrale Hochschulbibliothek Lübeck, 2014. http://d-nb.info/1060276887/34.
Повний текст джерелаC, Dukka Bahadur K. "Clique-based algorithms for protein structure prediction." 京都大学 (Kyoto University), 2006. http://hdl.handle.net/2433/143887.
Повний текст джерелаThomas, Dallas, and University of Lethbridge Faculty of Arts and Science. "Algorithms & experiments for the protein chain lattice fitting problem." Thesis, Lethbridge, Alta. : University of Lethbridge, Faculty of Arts and Science, 2006, 2006. http://hdl.handle.net/10133/535.
Повний текст джерелаix, 47 leaves ; 29 cm.
Gamalielsson, Jonas. "Models for Protein Structure Prediction by Evolutionary Algorithms." Thesis, University of Skövde, Department of Computer Science, 2001. http://urn.kb.se/resolve?urn=urn:nbn:se:his:diva-623.
Повний текст джерелаEvolutionary algorithms (EAs) have been shown to be competent at solving complex, multimodal optimisation problems in applications where the search space is large and badly understood. EAs are therefore among the most promising classes of algorithms for solving the Protein Structure Prediction Problem (PSPP). The PSPP is how to derive the 3D-structure of a protein given only its sequence of amino acids. This dissertation defines, evaluates and shows limitations of simplified models for solving the PSPP. These simplified models are off-lattice extensions to the lattice HP model which has been proposed and is claimed to possess some of the properties of real protein folding such as the formation of a hydrophobic core. Lattice models usually model a protein at the amino acid level of detail, use simple energy calculations and are used mainly for search algorithm development. Off-lattice models usually model the protein at the atomic level of detail, use more complex energy calculations and may be used for comparison with real proteins. The idea is to combine the fast energy calculations of lattice models with the increased spatial possibilities of an off-lattice environment allowing for comparison with real protein structures. A hypothesis is presented which claims that a simplified off-lattice model which considers other amino acid properties apart from hydrophobicity will yield simulated structures with lower Root Mean Square Deviation (RMSD) to the native fold than a model only considering hydrophobicity. The hypothesis holds for four of five tested short proteins with a maximum of 46 residues. Best average RMSD for any model tested is above 6Å, i.e. too high for useful structure prediction and excludes significant resemblance between native and simulated structure. Hence, the tested models do not contain the necessary biological information to capture the complex interactions of real protein folding. It is also shown that the EA itself is competent and can produce near-native structures if given a suitable evaluation function. Hence, EAs are useful for eventually solving the PSPP.
Parry-Smith, David John. "Algorithms and data structures for protein sequence analysis." Thesis, University of Leeds, 1990. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.277404.
Повний текст джерелаSingh, Rohit Ph D. Massachusetts Institute of Technology. "Algorithms for the analysis of protein interaction networks." Thesis, Massachusetts Institute of Technology, 2012. http://hdl.handle.net/1721.1/71489.
Повний текст джерелаCataloged from PDF version of thesis.
Includes bibliographical references (p. 107-117).
In the decade since the human genome project, a major research trend in biology has been towards understanding the cell as a system. This interest has stemmed partly from a deeper appreciation of how important it is to understand the emergent properties of cellular systems (e.g., they seem to be the key to understanding diseases like cancer). It has also been enabled by new high-throughput techniques that have allowed us to collect new types of data at the whole-genome scale. We focus on one sub-domain of systems biology: the understanding of protein interactions. Such understanding is valuable: interactions between proteins are fundamental to many cellular processes. Over the last decade, high-throughput experimental techniques have allowed us to collect a large amount of protein-protein interaction (PPI) data for many species. A popular abstraction for representing this data is the protein interaction network: each node of the network represents a protein and an edge between two nodes represents a physical interaction between the two corresponding proteins. This abstraction has proven to be a powerful tool for understanding the systems aspects of protein interaction. We present some algorithms for the augmentation, cleanup and analysis of such protein interaction networks: 1. In many species, the coverage of known PPI data remains partial. Given two protein sequences, we describe an algorithm to predict if two proteins physically interact, using logistic regression and insights from structural biology. We also describe how our predictions may be further improved by combining with functional-genomic data. 2. We study systematic false positives in a popular experimental protocol, the Yeast 2-Hybrid method. Here, some "promiscuous" proteins may lead to many false positives. We describe a Bayesian approach to modeling and adjusting for this error. 3. Comparative analysis of PPI networks across species can provide valuable insights. We describe IsoRank, an algorithm for global network alignment of multiple PPI networks. The algorithm first constructs an eigenvalue problem that encapsulates the network and sequence similarity constraints. The solution of the problem describes a k-partite graph that is further processed to find the alignment. 4. For a given signaling network, we describe an algorithm that combines RNA-interference data with PPI data to produce hypotheses about the structure of the signaling network. Our algorithm constructs a multi-commodity flow problem that expresses the constraints described by the data and finds a sparse solution to it.
by Rohit Singh.
Ph.D.
Djurdjević, Dušan. "Ab initio protein fold prediction using evolutionary algorithms." Thesis, University of Edinburgh, 2006. http://hdl.handle.net/1842/13660.
Повний текст джерелаContreras-Moreira, Bruno. "Algorithms for protein comparative modelling and some evolutionary implications." Thesis, University College London (University of London), 2004. http://discovery.ucl.ac.uk/1446587/.
Повний текст джерелаWang, Xueyi Snoeyink Jack. "Exploring RNA and protein 3D structures by geometric algorithms." Chapel Hill, N.C. : University of North Carolina at Chapel Hill, 2008. http://dc.lib.unc.edu/u?/etd,1905.
Повний текст джерелаTitle from electronic title page (viewed Dec. 11, 2008). "... in partial fulfillment of the requirements for the degree of Doctor of Philosophy in the Department of Computer Science." Discipline: Computer Science; Department/School: Computer Science.
Jiménez, García Brian. "Development and optimization of high-performance computational tools for protein-protein docking." Doctoral thesis, Universitat de Barcelona, 2016. http://hdl.handle.net/10803/398790.
Повний текст джерелаGràcies als recents avenços en computació, el nostre coneixement de la química que suporta la vida ha incrementat enormement i ens ha conduït a comprendre que la química de la vida és més sofisticada del que mai haguéssim pensat. Les proteïnes juguen un paper fonamental en aquesta química i són descrites habitualment com a les fàbriques de les cèl·lules. A més a més, les proteïnes estan involucrades en gairebé tots els processos fonamentals en els éssers vius. Malauradament, el nostre coneixement de la funció de moltes proteïnes és encara escaig degut a les limitacions actuals de molts mètodes experimentals, que encara no són capaços de proporcionar-nos estructures de cristall per a molts complexes proteïna-proteïna. El desenvolupament de tècniques i eines informàtiques d’acoblament proteïna-proteïna pot ésser crucial per a ajudar-nos a reduir aquest forat. En aquesta tesis, hem presentat un nou mètode computacional de predicció d’acoblament proteïna-proteïna, LightDock, que és capaç de fer servir diverses funcions energètiques definides per l’usuari i incloure un model de flexibilitat de la cadena principal mitjançant la anàlisis de modes normals. Segon, diverses eines d’interès per a la comunitat científica i basades en tecnologia web han sigut desenvolupades: un servidor web de predicció d’acoblament proteïna-proteïna, una eina online per a caracteritzar les interfícies d’acoblament proteïna-proteïna i una eina web per a incloure dades experimentals de tipus SAXS. A més a més, les optimitzacions fetes al protocol pyDock i la conseqüent millora en rendiment han propiciat que el nostre grup de recerca obtingués la cinquena posició entre més de 60 grups en les dues darreres avaluacions de l’experiment internacional CAPRI. Finalment, hem dissenyat i compilat els banc de proves d’acoblament proteïna-proteïna (versió 5) i proteïna-ARN (versió 1), molt importants per a la comunitat ja que permeten provar i desenvolupar nous mètodes i analitzar-ne el rendiment en aquest marc de referència comú.
Bondugula, Rajkumar. "A novel framework for protein structure prediction." Diss., Columbia, Mo. : University of Missouri-Columbia, 2007. http://hdl.handle.net/10355/4855.
Повний текст джерелаThe entire dissertation/thesis text is included in the research.pdf file; the official abstract appears in the short.pdf file (which also appears in the research.pdf); a non-technical general description, or public abstract, appears in the public.pdf file. Title from title screen of research.pdf file (viewed on March 23, 2009) Vita. Includes bibliographical references.
Pettitt, Christopher Steven. "Refinement of protein structure models with multi-objective genetic algorithms." Thesis, University College London (University of London), 2007. http://discovery.ucl.ac.uk/1446043/.
Повний текст джерелаBliven, Spencer Edward. "Structure-Preserving Rearrangements| Algorithms for Structural Comparison and Protein Analysis." Thesis, University of California, San Diego, 2015. http://pqdtopen.proquest.com/#viewpdf?dispub=3716489.
Повний текст джерелаProtein structure is fundamental to a deep understanding of how proteins function. Since structure is highly conserved, structural comparison can provide deep information about the evolution and function of protein families. The Protein Data Bank (PDB) continues to grow rapidly, providing copious opportunities for advancing our understanding of proteins through large-scale searches and structural comparisons. In this work I present several novel structural comparison methods for specific applications, as well as apply structure comparison tools systematically to better understand global properties of protein fold space.
Circular permutation describes a relationship between two proteins where the N-terminal portion of one protein is related to the C-terminal portion of the other. Proteins that are related by a circular permutation generally share the same structure despite the rearrangement of their primary sequence. This non-sequential relationship makes them difficult for many structure alignment tools to detect. Combinatorial Extension for Circular Permutations (CE-CP) was developed to align proteins that may be related by a circular permutation. It is widely available due to its incorporation into the RCSB PDB website.
Symmetry and structural repeats are common in protein structures at many levels. The CE-Symm tool was developed in order to detect internal pseudosymmetry within individual polypeptide chains. Such internal symmetry can arise from duplication events, so aligning the individual symmetry units provides insights about conservation and evolution. In many cases, internal symmetry can be shown to be important for a number of functions, including ligand binding, allostery, folding, stability, and evolution.
Structural comparison tools were applied comprehensively across all PDB structures for systematic analysis. Pairwise structural comparisons of all proteins in the PDB have been computed using the Open Science Grid computing infrastructure, and are kept continually up-to-date with the release of new structures. These provide a network-based view of protein fold space. CE-Symm was also applied to systematically survey the PDB for internally symmetric proteins. It is able to detect symmetry in ~20% of all protein families. Such PDB-wide analyses give insights into the complex evolution of protein folds.
Singh, Mona. "Learning algorithms with applications to robot navigation and protein folding." Thesis, Massachusetts Institute of Technology, 1995. http://hdl.handle.net/1721.1/40579.
Повний текст джерелаCrook, James. "New algorithms and methods for protein and DNA sequence comparison." Thesis, University of Edinburgh, 1991. http://hdl.handle.net/1842/13497.
Повний текст джерелаTan, Guanhong. "Study of Protein Identification Algorithms and Ammonia Metabolism in Mosquitoes." Thesis, The University of Arizona, 2006. http://hdl.handle.net/10150/193319.
Повний текст джерелаChoudhury, Salimur Rashid, and University of Lethbridge Faculty of Arts and Science. "Approximation algorithms for a graph-cut problem with applications to a clustering problem in bioinformatics." Thesis, Lethbridge, Alta. : University of Lethbridge, Deptartment of Mathematics and Computer Science, 2008, 2008. http://hdl.handle.net/10133/774.
Повний текст джерелаxiii, 71 leaves : ill. ; 29 cm.
Palmer, Jane. "The application of genetic algorithms to problems in protein structure solution." Thesis, University of Sheffield, 1998. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.286746.
Повний текст джерелаOtero, Fernando E. B. "New ant colony optimisation algorithms for hierarchial classification of protein functions." Thesis, University of Kent, 2010. http://www.cs.kent.ac.uk/pubs/2010/3057.
Повний текст джерелаChippington-Derrick, T. C. "Models, methods and algorithms for constraint dynamics simulations of long chain molecules." Thesis, University of Reading, 1988. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.234776.
Повний текст джерелаIshivatari, Luís Henrique Uchida. "Função de avaliação dinâmica em algoritmos genéticos aplicados na predição de estruturas tridimensionais de proteínas." Universidade de São Paulo, 2012. http://www.teses.usp.br/teses/disponiveis/95/95131/tde-27112012-185423/.
Повний текст джерелаThe protein structure prediction can be seen as an optimization problem where given an amino acid sequence, the tertiary protein structure must be found amongst many possible by obtaining energy functions minima. Many researchers have been proposing Evolutionary Computation strategies to find tridimensional structures of proteins; however results are not always satisfactory since among other factors, there are always a great number of local optima in the search space. Usually, the fitness functions used by optimization algorithms are based on force fields with different energy terms with parameters from those terms being adjusted a priori, kept static through the optimization process. Some researchers suggest that the use of dynamic functions, i.e., that can be changed during the evolutionary process, can help the population to escape from local optima in highly multimodal problems. In this work we propose that the force field parameters can be changed during the optimization process of Genetic Algorithms (GAs) in the protein structure prediction problem, being increased or decreased, for instance, according with its influence on formation of secondary structures and its fine tuning. Since the cost function will be changed during the optimization process, the protein tridimensional structure prediction becomes a dynamic optimization problem and specific Genetic Algorithms for this kind of problem, like the hypermutation GA and random immigrants GA are investigated. We also propose a new metric related to the proteins secondary structure alignment to help the analysis of obtained data. Results indicate that the dynamic function algorithms obtained better results than static algorithms since changes on the fitness function allow the population to escape local optima, as well as an increase on the population diversity.
Herndon, Nic. "Domain adaptation algorithms for biological sequence classification." Diss., Kansas State University, 2016. http://hdl.handle.net/2097/35242.
Повний текст джерелаDepartment of Computing and Information Sciences
Doina Caragea
The large volume of data generated in the recent years has created opportunities for discoveries in various fields. In biology, next generation sequencing technologies determine faster and cheaper the exact order of nucleotides present within a DNA or RNA fragment. This large volume of data requires the use of automated tools to extract information and generate knowledge. Machine learning classification algorithms provide an automated means to annotate data but require some of these data to be manually labeled by human experts, a process that is costly and time consuming. An alternative to labeling data is to use existing labeled data from a related domain, the source domain, if any such data is available, to train a classifier for the domain of interest, the target domain. However, the classification accuracy usually decreases for the domain of interest as the distance between the source and target domains increases. Another alternative is to label some data and complement it with abundant unlabeled data from the same domain, and train a semi-supervised classifier, although the unlabeled data can mislead such classifier. In this work another alternative is considered, domain adaptation, in which the goal is to train an accurate classifier for a domain with limited labeled data and abundant unlabeled data, the target domain, by leveraging labeled data from a related domain, the source domain. Several domain adaptation classifiers are proposed, derived from a supervised discriminative classifier (logistic regression) or a supervised generative classifier (naïve Bayes), and some of the factors that influence their accuracy are studied: features, data used from the source domain, how to incorporate the unlabeled data, and how to combine all available data. The proposed approaches were evaluated on two biological problems -- protein localization and ab initio splice site prediction. The former is motivated by the fact that predicting where a protein is localized provides an indication for its function, whereas the latter is an essential step in gene prediction.
Chi, Pin-Hao. "Efficient protein tertiary structure retrievals and classifications using content based comparison algorithms." Diss., Columbia, Mo. : University of Missouri-Columbia, 2007. http://hdl.handle.net/10355/4817.
Повний текст джерелаThe entire dissertation/thesis text is included in the research.pdf file; the official abstract appears in the short.pdf file (which also appears in the research.pdf); a non-technical general description, or public abstract, appears in the public.pdf file. Title from title screen of research.pdf file (viewed on September 19, 2007) Vita. Includes bibliographical references.
Park, Daniel K. (Daniel Kyu). "Web servers, databases, and algorithms for the analysis of protein interaction networks." Thesis, Massachusetts Institute of Technology, 2013. http://hdl.handle.net/1721.1/79146.
Повний текст джерелаThis electronic version was submitted by the student author. The certified thesis is available in the Institute Archives and Special Collections.
Cataloged from student-submitted PDF version of thesis.
Includes bibliographical references (p. 41-44).
Understanding the cell as a system has become one of the foremost challenges in the post-genomic era. As a result of advances in high-throughput (HTP) methodologies, we have seen a rapid growth in new types of data at the whole-genome scale. Over the last decade, HTP experimental techniques such as yeast two-hybrid assays and co-affinity purification couple with mass spectrometry have generated large amounts of data on protein-protein interactions (PPI) for many organisms. We focus on the sub-domain of systems biology related to understanding the interactions between proteins that ultimately drive all cellular processes. Representing PPIs as a protein interaction network has proved to be a powerful tool for understanding PPIs at the systems level. In this representation, each node represents a protein and each edge between two nodes represents a physical interaction between the corresponding two proteins. With this abstraction, we present algorithms for the prediction and analysis of such PPI networks as well as web servers and databases for their public availability: 1. In many organisms, the coverage of experimental determined PPI data remains relatively noisy and limited. Given two protein sequences, we describe an algorithm, called Struct2Net, to predict if two proteins physically interact, using insights from structural biology and logistic regression. Furthermore, we create a community-wide web-resource that predicts interactions between any protein sequence pair and provides proteome-wide pre-computed PPI predictions for Homo sapiens, Drosophila melanogaster, and Saccharomyces cerevisiae. 2. Comparative analysis of PPI networks across organisms can provide valuable insights into evolutionary conservation. We describe an algorithm, called IsoRank, for global alignment of multiple PPI networks. The algorithm first constructs an eigenvalue problem that models the network and sequence similarity constraints. The solution of the problem describes a k partite graph that is further processed to find the alignments. Furthermore, we create a communitywide web database, called IsoBase, that provides network alignments and orthology mappings for the most commonly studied eukaryotic model organisms: Homo sapiens, Mus musculus, Drosophila melanogaster, Caenorhabditis elegans, and Saccharomyces cerevisiae.
by Daniel K. Park.
S.M.
Li, Wenzhou. "Protein Identification Algorithms Developed from Statistical Analysis of MS/MS Fragmentation Patterns." Diss., The University of Arizona, 2012. http://hdl.handle.net/10150/242432.
Повний текст джерелаKarimpour-Fard, Anis. "Prediction of protein-protein interactions and function in bacteria /." Connect to full text via ProQuest. Limited to UCD Anschutz Medical Campus, 2008.
Знайти повний текст джерелаTypescript. Includes bibliographical references (leaves 141-150). Free to UCD Anschutz Medical Campus. Online version available via ProQuest Digital Dissertations;
Akkaladevi, Somasheker. "Decision Fusion for Protein Secondary Structure Prediction." Digital Archive @ GSU, 2006. http://digitalarchive.gsu.edu/cs_diss/9.
Повний текст джерелаPlanas, Iglesias Joan 1980. "On the study of 3D structure of proteins for developing new algorithms to complete the interactome and cell signalling networks." Doctoral thesis, Universitat Pompeu Fabra, 2013. http://hdl.handle.net/10803/104152.
Повний текст джерелаLes proteïnes tenen un paper indispensable en virtualment qualsevol procés biològic. Les funcions de les proteïnes estan determinades per la seva estructura tridimensional (3D) i són coordinades per mitjà d’una complexa xarxa d’interaccions protiques (en anglès, protein-protein interactions, PPIs). Axí doncs, una comprensió en profunditat d’aquestes xarxes és fonamental per entendre la biologia cel•lular. Per a l’anàlisi de les xarxes d’interacció de proteïnes, l’ús de tècniques computacionals ha esdevingut fonamental als darrers temps. Els mètodes in silico aprofiten el coneixement actual sobre les interaccions proteiques per fer prediccions de noves interaccions o de les funcions de les proteïnes. Actualment existeixen diferents mètodes per a la predicció de noves interaccions de proteines. De tota manera, resultats recents demostren que aquests mètodes poden beneficiar-se del coneixement sobre parelles de proteïnes no interaccionants (en anglès, non-interacting pairs, NIPs). Per a la tasca de predir la funció de les proteïnes, el principi de “culpable per associació” (en anglès, guilt by association, GBA) és usat per extendre l’anotació de proteïnes de funció coneguda a través de xarxes d’interacció de proteïnes. En aquesta tesi es presenta un nou mètode pre a la predicció d’interaccions proteiques i un nou protocol basat per a completar xarxes de senyalització cel•lular. iLoops és un mètode que utilitza dades de parells no interaccionants i coneixement de l’estructura 3D de les proteïnes per a predir interaccions de proteïnes. També s’ha desenvolupat un nou protocol per a completar xarxes de senyalització cel•lular, una tasca relacionada amb la predicció de les funcions de les proteïnes. Aquest protocol es basa en aplicar el principi GBA a xarxes d’interaccions proteiques.
Zhao, Zhiyu. "Robust and Efficient Algorithms for Protein 3-D Structure Alignment and Genome Sequence Comparison." ScholarWorks@UNO, 2008. http://scholarworks.uno.edu/td/851.
Повний текст джерелаKim, Wooyoung. "Innovative Algorithms and Evaluation Methods for Biological Motif Finding." Digital Archive @ GSU, 2012. http://digitalarchive.gsu.edu/cs_diss/63.
Повний текст джерелаReyaz-Ahmed, Anjum B. "Protein Secondary Structure Prediction Using Support Vector Machines, Nueral Networks and Genetic Algorithms." Digital Archive @ GSU, 2007. http://digitalarchive.gsu.edu/cs_theses/43.
Повний текст джерелаYerardi, Jason T. "The Implementation and Evaluation of Bioinformatics Algorithms for the Classification of Arabinogalactan-Proteins in Arabidopsis thaliana." Ohio University / OhioLINK, 2011. http://rave.ohiolink.edu/etdc/view?acc_num=ohiou1301069861.
Повний текст джерелаNorth, Benjamin H. "A Comparison of Clustering Algorithms for the Study of Antibody Loop Structures." Master's thesis, Temple University Libraries, 2017. http://cdm16002.contentdm.oclc.org/cdm/ref/collection/p245801coll10/id/464867.
Повний текст джерелаM.S.
Antibodies are the fundamental agents of the immune system. The CDRs, or Complementarity Determining Regions act as the functional surfaces in binding antibodies to their targets. These CDR structures, which are peptide loops, are diverse in both amino acid sequence and structure. In 2011, we surveyed a database of CDR loop structures using the affinity propagation clustering algorithm of Frey and Dueck. With the growth of the number of structures deposited in the Protein Data Bank, the number of antibody CDRs has approximately tripled. In addition, although the affinity clustering in 2011 was successful in many ways, the methods used left too much noise in the data, and the affinity clustering algorithm tended to clump diverse structures together. This work revisits the antibody CDR clustering problem and uses five different clustering algorithms to categorize the data. Three of the clustering algorithms use DBSCAN but differ in the data comparison functions used. One uses the sum of the dihedral distances, while another uses the supremum of the dihedral distances, and the third uses the Jarvis-Patrick shared nearest neighbor similarity, where the nearest neighbor lists are compiled using the sum of the dihedral distances. The other two clustering methods use the k-medoids algorithm, one of which has been modified to include the use of pairwise constraints. Overall, the DBSCAN using the sum of dihedral distances and the supremum of the dihedral distances produced the best clustering results as measured by the average silhouette coefficient, while the constrained k-medoids clustering algorithm had the worst clustering results overall.
Temple University--Theses
Parkinson, Scott. "Rational Design Inspired Application of Natural Language Processing Algorithms to Red Shift mNeptune684." Thesis, Université d'Ottawa / University of Ottawa, 2021. http://hdl.handle.net/10393/41928.
Повний текст джерелаYaveroglu, Omer Nebil. "Identification Of Functionally Orthologous Protein Groups In Different Species Based On Protein Network Alignment." Master's thesis, METU, 2010. http://etd.lib.metu.edu.tr/upload/12612395/index.pdf.
Повний текст джерелаShah, Anuj R. "Improving protein remote homology detection using supervised and semi-supervised support vector machines." Online access for everyone, 2008. http://www.dissertations.wsu.edu/Dissertations/Spring2008/A_Shah_042408.pdf.
Повний текст джерелаKlaib, Ahmad. "Exact string matching algorithms for searching DNA and protein sequences and searching chemical databases." Thesis, University of Huddersfield, 2014. http://eprints.hud.ac.uk/id/eprint/24266/.
Повний текст джерелаOlandersson, Sandra. "Evaluation of Machine Learning Algorithms for Classification of Short-Chain Dehydrogenase/Reductase Protein Sequences." Thesis, Blekinge Tekniska Högskola, Institutionen för programvaruteknik och datavetenskap, 2003. http://urn.kb.se/resolve?urn=urn:nbn:se:bth-3828.
Повний текст джерелаKlassificeringen av proteinsekvenser är ett område inom Bioinformatik, vilket idag drar till sig ett stort intresse. Maskininlärningsalgoritmer anses här kunna förbättra utförandet av klassificeringsfasen. Denna uppsats rör tillämpandet av olika maskininlärningsalgoritmer för klassificering av ett dataset med short-chain dehydrogenases/reductases (SDR) proteiner. Klassificeringen rör både indelningen av proteinerna i två huvudklasser, Classic och Extended, och deras olika subklasser. Resultaten av de olika algoritmerna jämförs för att välja ut den mest lämpliga algoritmen för detta specifika klassificeringsproblem.
Sandra Olandersson Blåbärsvägen 27 372 38 Ronneby home: 0457-12084
Denarie, Laurent. "Robotics-inspired methods to enhance protein design." Phd thesis, Toulouse, INPT, 2017. http://oatao.univ-toulouse.fr/18677/1/Denarie.pdf.
Повний текст джерелаWistrand, Markus. "Hidden Markov models for remote protein homology detection /." Stockholm, 2005. http://diss.kib.ki.se/2006/91-7140-598-4/.
Повний текст джерелаHom, Geoffrey Deshaies Raymond Joseph. "Advances in computational protein design : development of more efficient search algorithms and their application to the full-sequence design of larger proteins /." Diss., Pasadena, Calif. : California Institute of Technology, 2005. http://resolver.caltech.edu/CaltechETD:etd-05302005-223153.
Повний текст джерелаBakare, Olalekan Olanrewaju. "Identification and Molecular validation of Biomarkers for the accurate and sensitive diagnosis of bacterial and viral Pneumonia." University of Western Cape, 2019. http://hdl.handle.net/11394/7421.
Повний текст джерелаPneumonia remains the major cause of death in children and the elderly and several efforts have been intensified to reduce the rate of pneumonia infection. The major breakthrough has been the discovery of certain biomarkers for the diagnosis of pneumonia through immunogenic techniques.
Kolar, Michal. "Statistical Physics and Message Passing Algorithms. Two Case Studies: MAX-K-SAT Problem and Protein Flexibility." Doctoral thesis, SISSA, 2006. http://hdl.handle.net/20.500.11767/4659.
Повний текст джерелаLan, Liang. "Data Mining Algorithms for Classification of Complex Biomedical Data." Diss., Temple University Libraries, 2012. http://cdm16002.contentdm.oclc.org/cdm/ref/collection/p245801coll10/id/214773.
Повний текст джерелаPh.D.
In my dissertation, I will present my research which contributes to solve the following three open problems from biomedical informatics: (1) Multi-task approaches for microarray classification; (2) Multi-label classification of gene and protein prediction from multi-source biological data; (3) Spatial scan for movement data. In microarray classification, samples belong to several predefined categories (e.g., cancer vs. control tissues) and the goal is to build a predictor that classifies a new tissue sample based on its microarray measurements. When faced with the small-sample high-dimensional microarray data, most machine learning algorithm would produce an overly complicated model that performs well on training data but poorly on new data. To reduce the risk of over-fitting, feature selection becomes an essential technique in microarray classification. However, standard feature selection algorithms are bound to underperform when the size of the microarray data is particularly small. The best remedy is to borrow strength from external microarray datasets. In this dissertation, I will present two new multi-task feature filter methods which can improve the classification performance by utilizing the external microarray data. The first method is to aggregate the feature selection results from multiple microarray classification tasks. The resulting multi-task feature selection can be shown to improve quality of the selected features and lead to higher classification accuracy. The second method jointly selects a small gene set with maximal discriminative power and minimal redundancy across multiple classification tasks by solving an objective function with integer constraints. In protein function prediction problem, gene functions are predicted from a predefined set of possible functions (e.g., the functions defined in the Gene Ontology). Gene function prediction is a complex classification problem characterized by the following aspects: (1) a single gene may have multiple functions; (2) the functions are organized in hierarchy; (3) unbalanced training data for each function (much less positive than negative examples); (4) missing class labels; (5) availability of multiple biological data sources, such as microarray data, genome sequence and protein-protein interactions. As participants in the 2011 Critical Assessment of Function Annotation (CAFA) challenge, our team achieved the highest AUC accuracy among 45 groups. In the competition, we gained by focusing on the 5-th aspect of the problem. Thus, in this dissertation, I will discuss several schemes to integrate the prediction scores from multiple data sources and show their results. Interestingly, the experimental results show that a simple averaging integration method is competitive with other state-of-the-art data integration methods. Original spatial scan algorithm is used for detection of spatial overdensities: discovery of spatial subregions with significantly higher scores according to some density measure. This algorithm is widely used in identifying cluster of disease cases (e.g., identifying environmental risk factors for child leukemia). However, the original spatial scan algorithm only works on static spatial data. In this dissertation, I will propose one possible solution for spatial scan on movement data.
Temple University--Theses
Mathuriya, Amrita. "Prediction of secondary structures for large RNA molecules." Thesis, Atlanta, Ga. : Georgia Institute of Technology, 2009. http://hdl.handle.net/1853/28195.
Повний текст джерелаCommittee Chair: Bader, David; Committee Co-Chair: Heitsch, Christine; Committee Member: Harvey, Stephen; Committee Member: Vuduc, Richard.