Global ETD Search
Search theses and dissertations gathered from participating repositories worldwide. Every result links back to the library that holds it. No account is needed.
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Showing 1 to 20 of 20 for “"biological sequence"”.
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Aspects of biological sequence comparison
Thesis (Ph. D)--Massachusetts Institute of Technology, Dept. of Mathematics, 1987.
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Stochastic modeling of biological sequence evolution
Markov models of sequence evolution are a fundamental building block for making inferences in biological research. This thesis reviews several major techniques developed to estimate parameters of Markov models of sequence evolution and presents a new approach for evaluating and comparing estimation …
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Searching Biological Sequence Databases Using Distributed Adaptive Computing
… common type of processing involves searching a sequence database for the most similar sequences. Here we present a distributed database search system that utilizes adaptive computing technologies. The search is performed using the Smith-Waterman algorithm, a common sequence comparison algorithm. …
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Improving neural networks for biological sequence analysis through problem-specific customizations
Submission original under an indefinite embargo labeled 'Open Access'. The submission was exported from vireo on 2024-09-16 without embargo terms
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Improving neural networks for biological sequence analysis through problem-specific customizations
Submission original under an indefinite embargo labeled 'Open Access'. The submission was exported from vireo on 2024-09-16 without embargo terms
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Probabilistic arithmetic automata : applications of a stochastic computational framework in biological sequence analysis
The immense amount of biological sequence data available these days requires efficient and sensitive analysis in order to provide e.g. the identification of unknown proteins, or information about the similarity between DNA sequences. Furthermore, new challenges to computational sequence analysis …
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On the use of locality aware distributed hash tables for homology searches over voluminous biological sequence data
… challenges for frequently performed sequence analytics routines such as DNA and protein homology searches; these must also preferably be done in real-time. This thesis proposes a scalable and similarity-aware distributed storage framework, Mendel, that enables retrieval of …
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Mathematical Models, Algorithms, and Statistics of Sequence Alignment
<p>The problem of biological sequence comparison arises naturally in an attempt to explain many biological phenomena. Due to the combinatorial structure and pattern preserving properties of the sequences it attracted not only biologists, but also mathematicians, statisticians and computer …
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Methods for identifying regulatory grammars
… relationships between motifs in sets of DNA sequences. For example, the method will identify spatially constrained motifs colocated in the same regulatory region. We apply our method to biological sequence data and recover previously known prokaryotic promoter spacing constraints …
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Structure-based realignment of non-coding RNAs in multiple whole genome alignments
… genome alignments have become a central tool in biological sequence analysis. A major application is the de novo prediction of non-coding RNAs (ncRNAs) from structural conservation visible in the alignment. However, current methods for constructing genome alignments do so by explicitly optimizing …
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Machine learning models for functional genomics and therapeutic design
… models is to learn how the composition of a biological sequence encodes a functional phenotype and then leverage such knowledge to provide insight for target discovery and therapeutic design.
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CLASSIFYING PEROXIREDOXIN SUBGROUPS AND IDENTIFYING DISCRIMINATING MOTIFS VIA MACHINE LEARNING
… lagging far behind the exponential growth in biological sequence databases. In this thesis, I present our recent development of machine learning methods for high-throughput, accurate, sequence-based functional annotation. Chapter 1 describes the biological and computational background of this …
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Data Driven Models for Language Evolution
… several techniques originally developed for biological sequence analysis, we have designed a data driven orthographic learning system for measuring string similarity and we have successfully applied it to the tasks of cognate identification and phylogenetic inference. Our system has …
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Ensemble modeling of [beta]-sheet proteins
… a growing divide between our knowledge of biological sequence and structure. Structural modeling algorithms offer the hope to bridge this gap through computational exploration of the sequence determinants of structure diversity. In this thesis, we introduce new algorithms that enable the …
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Principled Methods and Models for Deep Learning Based Functional Genomics
… neural network architectures that are trained on biological sequence data, as is common in functional genomics. The second project describes a two-pronged approach to study the determinants of cell type-specific chromatin accessibility, with an ensemble of neural networks trained on DNase-seq data …
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Entropy Measurements and Ball Cover Construction for Biological Sequences
… is making it easier to select or engineer DNA sequences that produce dangerous proteins, it is important to be able to predict whether a novel DNA sequence is potentially dangerous by determining its taxonomic identity and functional characteristics. These tasks can be facilitated by the ever …
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Taxonomic classification of metagenomic sequences
… microbes are unculturable and thus cannot be sequenced by means of traditional methods. The recently upcoming discipline of metagenomics provides various in vivo- and in silico-tools to overcome this limitation. In particular, high-throughput sequencing techniques like 454 or Solexa-Illumina …
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Homology-Based Functional Proteomics By Mass Spectrometry and Advanced Informatic Methods
… isolated protein with a specific gene or protein sequence in silico, thus inferring its specific biochemical function based upon previous characterizations of that protein or a similar protein having that sequence identity. By performing this analysis on a large scale in conjunction with …
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Large Scale Machine Learning in Biology
… informative patterns that could lead to deeper biological understanding. Large volumes of data provided by such technologies, however, are not analyzable using hypothesis-driven significance tests and other cornerstones of orthodox statistics. We present powerful tools in machine learning and …
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Unsupervised inference models for structural and functional properties of protein sequences
L'abstract è presente nell'allegato / the abstract is in the attachment