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Articles 31 - 40 of 40
Full-Text Articles in Computational Biology
Gene Ontology Analysis Of Pairwise Genetic Associations In Two Genome-Wide Studies Of Sporadic Als, Nora Chung Kim, Peter C. Andrews, Folkert W. Asselbergs, H Robert Frost, Scott M. Williams, Brent T. Harris, Cynthia Read, Kathleen D. Askland, Jason H. Moore
Gene Ontology Analysis Of Pairwise Genetic Associations In Two Genome-Wide Studies Of Sporadic Als, Nora Chung Kim, Peter C. Andrews, Folkert W. Asselbergs, H Robert Frost, Scott M. Williams, Brent T. Harris, Cynthia Read, Kathleen D. Askland, Jason H. Moore
Dartmouth Scholarship
It is increasingly clear that common human diseases have a complex genetic architecture characterized by both additive and nonadditive genetic effects. The goal of the present study was to determine whether patterns of both additive and nonadditive genetic associations aggregate in specific functional groups as defined by the Gene Ontology (GO).
Evolving Hard Problems: Generating Human Genetics Datasets With A Complex Etiology, Daniel S Himmelstein, Casey S Greene, Jason H Moore
Evolving Hard Problems: Generating Human Genetics Datasets With A Complex Etiology, Daniel S Himmelstein, Casey S Greene, Jason H Moore
Dartmouth Scholarship
BackgroundA goal of human genetics is to discover genetic factors that influence individuals' susceptibility to common diseases. Most common diseases are thought to result from the joint failure of two or more interacting components instead of single component failures. This greatly complicates both the task of selecting informative genetic variants and the task of modeling interactions between them. We and others have previously developed algorithms to detect and model the relationships between these genetic factors and disease. Previously these methods have been evaluated with datasets simulated according to pre-defined genetic models.
Computational Biology, Harvey Greenberg, Allen Holder
Computational Biology, Harvey Greenberg, Allen Holder
Mathematical Sciences Technical Reports (MSTR)
Computational biology is an interdisciplinary field that applies the techniques of computer science, applied mathematics, and statistics to address biological questions. OR is also interdisciplinary and applies the same mathematical and computational sciences, but to decision-making problems. Both focus on developing mathematical models and designing algorithms to solve them. Models in computational biology vary in their biological domain and can range from the interactions of genes and proteins to the relationships among organisms and species.
Constraint-Based Model Of Shewanella Oneidensis Mr-1 Metabolism: A Tool For Data Analysis And Hypothesis Generation, Grigoriy E. Pinchuk, Eric A. Hill, Oleg V. Geydebrekht, Jessica De Ingeniis, Xiaolin Zhang, Andrei Osterman, James H. Scott
Constraint-Based Model Of Shewanella Oneidensis Mr-1 Metabolism: A Tool For Data Analysis And Hypothesis Generation, Grigoriy E. Pinchuk, Eric A. Hill, Oleg V. Geydebrekht, Jessica De Ingeniis, Xiaolin Zhang, Andrei Osterman, James H. Scott
Dartmouth Scholarship
Shewanellae are gram-negative facultatively anaerobic metal-reducing bacteria commonly found in chemically (i.e., redox) stratified environments. Occupying such niches requires the ability to rapidly acclimate to changes in electron donor/acceptor type and availability; hence, the ability to compete and thrive in such environments must ultimately be reflected in the organization and utilization of electron transfer networks, as well as central and peripheral carbon metabolism. To understand how Shewanella oneidensis MR-1 utilizes its resources, the metabolic network was reconstructed. The resulting network consists of 774 reactions, 783 genes, and 634 unique metabolites and contains biosynthesis pathways for all cell constituents. Using constraint-based …
Optimization Algorithms For Functional Deimmunization Of Therapeutic Proteins, Andrew S. Parker, Wei Zheng, Karl E. Griswold, Chris Bailey-Kellogg
Optimization Algorithms For Functional Deimmunization Of Therapeutic Proteins, Andrew S. Parker, Wei Zheng, Karl E. Griswold, Chris Bailey-Kellogg
Dartmouth Scholarship
To develop protein therapeutics from exogenous sources, it is necessary to mitigate the risks of eliciting an anti-biotherapeutic immune response. A key aspect of the response is the recognition and surface display by antigen-presenting cells of epitopes, short peptide fragments derived from the foreign protein. Thus, developing minimal-epitope variants represents a powerful approach to deimmunizing protein therapeutics. Critically, mutations selected to reduce immunogenicity must not interfere with the protein's therapeutic activity.
Evaluation Of Annotation Performances Between Automated And Curated Databases Of E.Coli Using The Correlation Coefficient, Reddysalilaja Marpuri
Evaluation Of Annotation Performances Between Automated And Curated Databases Of E.Coli Using The Correlation Coefficient, Reddysalilaja Marpuri
Masters Theses & Specialist Projects
This project compared the performance of the correlation coefficient to show similarities in annotations between a predictive automated bacterial annotation database and the curated EcoCyc database. EcoCyc is a conservative multidimensional annotation system that is exclusively based on experimentally validated findings by over 15,000 publications. The automated annotation system, used in the comparison was BASys. It is often used as a first pass annotation tool that tries to add as many annotations as possible by drawing upon over 30 information sources. Gene ontology served as one basis of comparison between these databases because of the limited common terms in the …
Micrornas And The Advent Of Vertebrate Morphological Complexity, Alysha M. Heimberg, Lorenzo F. Sempere, Vanessa N. Moy, Phillip C. J. Donoghue, Kevin J. Peterson
Micrornas And The Advent Of Vertebrate Morphological Complexity, Alysha M. Heimberg, Lorenzo F. Sempere, Vanessa N. Moy, Phillip C. J. Donoghue, Kevin J. Peterson
Dartmouth Scholarship
The causal basis of vertebrate complexity has been sought in genome duplication events (GDEs) that occurred during the emergence of vertebrates, but evidence beyond coincidence is wanting. MicroRNAs (miRNAs) have recently been identified as a viable causal factor in increasing organismal complexity through the action of these ≈22-nt noncoding RNAs in regulating gene expression. Because miRNAs are continuously being added to animalian genomes, and, once integrated into a gene regulatory network, are strongly conserved in primary sequence and rarely secondarily lost, their evolutionary history can be accurately reconstructed. Here, using a combination of Northern analyses and genomic searches, we show …
Principal Component Analysis For Predicting Transcription-Factor Binding Motifs From Array-Derived Data, Yunlong Liu, Matthew P Vincenti, Hiroki Yokota
Principal Component Analysis For Predicting Transcription-Factor Binding Motifs From Array-Derived Data, Yunlong Liu, Matthew P Vincenti, Hiroki Yokota
Dartmouth Scholarship
The responses to interleukin 1 (IL-1) in human chondrocytes constitute a complex regulatory mechanism, where multiple transcription factors interact combinatorially to transcription-factor binding motifs (TFBMs). In order to select a critical set of TFBMs from genomic DNA information and an array-derived data, an efficient algorithm to solve a combinatorial optimization problem is required. Although computational approaches based on evolutionary algorithms are commonly employed, an analytical algorithm would be useful to predict TFBMs at nearly no computational cost and evaluate varying modelling conditions. Singular value decomposition (SVD) is a powerful method to derive primary components of a given matrix. Applying SVD …
Bioconductor: Open Software Development For Computational Biology And Bioinformatics, Robert C. Gentleman, Vincent J. Carey, Douglas J. Bates, Benjamin M. Bolstad, Marcel Dettling, Sandrine Dudoit, Byron Ellis, Laurent Gautier, Yongchao Ge, Jeff Gentry, Kurt Hornik, Torsten Hothorn, Wolfgang Huber, Stefano Iacus, Rafael Irizarry, Friedrich Leisch, Cheng Li, Martin Maechler, Anthony J. Rossini, Guenther Sawitzki, Colin Smith, Gordon K. Smyth, Luke Tierney, Yee Hwa Yang, Jianhua Zhang
Bioconductor: Open Software Development For Computational Biology And Bioinformatics, Robert C. Gentleman, Vincent J. Carey, Douglas J. Bates, Benjamin M. Bolstad, Marcel Dettling, Sandrine Dudoit, Byron Ellis, Laurent Gautier, Yongchao Ge, Jeff Gentry, Kurt Hornik, Torsten Hothorn, Wolfgang Huber, Stefano Iacus, Rafael Irizarry, Friedrich Leisch, Cheng Li, Martin Maechler, Anthony J. Rossini, Guenther Sawitzki, Colin Smith, Gordon K. Smyth, Luke Tierney, Yee Hwa Yang, Jianhua Zhang
Bioconductor Project Working Papers
The Bioconductor project is an initiative for the collaborative creation of extensible software for computational biology and bioinformatics. We detail some of the design decisions, software paradigms and operational strategies that have allowed a small number of researchers to provide a wide variety of innovative, extensible, software solutions in a relatively short time. The use of an object oriented programming paradigm, the adoption and development of a software package system, designing by contract, distributed development and collaboration with other projects are elements of this project's success. Individually, each of these concepts are useful and important but when combined they have …
Smart Sequence Similarity Search (S⁴) System, Zhuo Chen
Smart Sequence Similarity Search (S⁴) System, Zhuo Chen
Theses Digitization Project
Sequence similarity searching is commonly used to help clarify the biochemical and physiological features of newly discovered genes or proteins. An efficient similarity search relies on the choice of tools and their associated subprograms and numerous parameter settings. To assist researchers in selecting optimal programs and parameter settings for efficient sequence similarity searches, the web-based expert system, Smart Sequence Similarity Search (S4) was developed.