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Articles 601 - 630 of 755

Full-Text Articles in Computational Biology

Physiologically-Based Pharmacokinetic Modeling For Predicting Drug-Drug Interactions, David M. Ng, Ali Navid Aug 2011

Physiologically-Based Pharmacokinetic Modeling For Predicting Drug-Drug Interactions, David M. Ng, Ali Navid

STAR Program Research Presentations

Dynamics of interactions between the drugs caffeine and ciprofloxacin are predicted using physiologically-based pharmacokinetic (PBPK) modeling. Pharmacokinetic means the model determines where the drugs are distributed in the body over time. Physiologically-based means the anatomy and physiology of the human body is reflected in the structure and functioning of the model. Multiple drugs can interact to increase or decrease their beneficial and/or undesired effects. This is important because some common substances, such as caffeine in coffee and soft drinks, are actually drugs that affect the body. By implementing the model as a computer program, it is relatively straightforward to perform …

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A Unified Approach To Non-Negative Matrix Factorization And Probabilistic Latent Semantic Indexing, Karthik Devarajan, Guoli Wang, Nader Ebrahimi Jul 2011

A Unified Approach To Non-Negative Matrix Factorization And Probabilistic Latent Semantic Indexing, Karthik Devarajan, Guoli Wang, Nader Ebrahimi

COBRA Preprint Series

Non-negative matrix factorization (NMF) by the multiplicative updates algorithm is a powerful machine learning method for decomposing a high-dimensional nonnegative matrix V into two matrices, W and H, each with nonnegative entries, V ~ WH. NMF has been shown to have a unique parts-based, sparse representation of the data. The nonnegativity constraints in NMF allow only additive combinations of the data which enables it to learn parts that have distinct physical representations in reality. In the last few years, NMF has been successfully applied in a variety of areas such as natural language processing, information retrieval, image processing, speech recognition …

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Evolving Hard Problems: Generating Human Genetics Datasets With A Complex Etiology, Daniel S Himmelstein, Casey S Greene, Jason H Moore Jul 2011

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.

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Stability Analysis And Application Of A Mathematical Cholera Model, Shu Liao, Jim Wang Jul 2011

Stability Analysis And Application Of A Mathematical Cholera Model, Shu Liao, Jim Wang

Mathematics & Statistics Faculty Publications

In this paper, we conduct a dynamical analysis of the deterministic cholera model proposed in [9]. We study the stability of both the disease-free and endemic equilibria so as to explore the complex epidemic and endemic dynamics of the disease. We demonstrate a real-world application of this model by investigating the recent cholera outbreak in Zimbabwe. Meanwhile, we present numerical simulation results to verify the analytical predictions.

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A Bayesian Model Averaging Approach For Observational Gene Expression Studies, Xi Kathy Zhou, Fei Liu, Andrew J. Dannenberg Jun 2011

A Bayesian Model Averaging Approach For Observational Gene Expression Studies, Xi Kathy Zhou, Fei Liu, Andrew J. Dannenberg

COBRA Preprint Series

Identifying differentially expressed (DE) genes associated with a sample characteristic is the primary objective of many microarray studies. As more and more studies are carried out with observational rather than well controlled experimental samples, it becomes important to evaluate and properly control the impact of sample heterogeneity on DE gene finding. Typical methods for identifying DE genes require ranking all the genes according to a pre-selected statistic based on a single model for two or more group comparisons, with or without adjustment for other covariates. Such single model approaches unavoidably result in model misspecification, which can lead to increased error …

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Component Extraction Of Complex Biomedical Signal And Performance Analysis Based On Different Algorithm, Hemant Pasusangai Kasturiwale Jun 2011

Component Extraction Of Complex Biomedical Signal And Performance Analysis Based On Different Algorithm, Hemant Pasusangai Kasturiwale

Johns Hopkins University, Dept. of Biostatistics Working Papers

Biomedical signals can arise from one or many sources including heart ,brains and endocrine systems. Multiple sources poses challenge to researchers which may have contaminated with artifacts and noise. The Biomedical time series signal are like electroencephalogram(EEG),electrocardiogram(ECG),etc The morphology of the cardiac signal is very important in most of diagnostics based on the ECG. The diagnosis of patient is based on visual observation of recorded ECG,EEG,etc, may not be accurate. To achieve better understanding , PCA (Principal Component Analysis) and ICA algorithms helps in analyzing ECG signals . The immense scope in the field of biomedical-signal processing Independent Component Analysis( …

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Removing Technical Variability In Rna-Seq Data Using Conditional Quantile Normalization, Kasper D. Hansen, Rafael A. Irizarry, Zhijin Wu May 2011

Removing Technical Variability In Rna-Seq Data Using Conditional Quantile Normalization, Kasper D. Hansen, Rafael A. Irizarry, Zhijin Wu

Johns Hopkins University, Dept. of Biostatistics Working Papers

The ability to measure gene expression on a genome-wide scale is one of the most promising accomplishments in molecular biology. Microarrays, the technology that first permitted this, were riddled with problems due to unwanted sources of variability. Many of these problems are now mitigated, after a decade’s worth of statistical methodology development. The recently developed RNA sequencing (RNA-seq) technology has generated much excitement in part due to claims of reduced variability in comparison to microarrays. However, we show RNA-seq data demonstrates unwanted and obscuring variability similar to what was first observed in microarrays. In particular, we find GC-content has a …

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Molecular Evolution And Historical Biogeography Of New World Birds, Brian T. Smith May 2011

Molecular Evolution And Historical Biogeography Of New World Birds, Brian T. Smith

UNLV Theses, Dissertations, Professional Papers, and Capstones

Deciphering the patterns of how biodiversity has evolved across time and space has remained a fundamental objective for biologists for the last 200 years. Researchers are faced with the challenge of interpreting the complexity of evolutionary patterns that have been generated over the deep history of the Earth. The advancement of DNA sequencing technology has yielded a new and powerful genetic toolkit that has allowed biologists to address novel evolutionary questions. For my dissertation research, I used molecular genetics and a statistical framework to study the evolution and historical biogeography of birds distributed in North and South America. My dissertation …

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Microarray Data Mining And Gene Regulatory Network Analysis, Ying Li May 2011

Microarray Data Mining And Gene Regulatory Network Analysis, Ying Li

Dissertations

The novel molecular biological technology, microarray, makes it feasible to obtain quantitative measurements of expression of thousands of genes present in a biological sample simultaneously. Genome-wide expression data generated from this technology are promising to uncover the implicit, previously unknown biological knowledge. In this study, several problems about microarray data mining techniques were investigated, including feature(gene) selection, classifier genes identification, generation of reference genetic interaction network for non-model organisms and gene regulatory network reconstruction using time-series gene expression data. The limitations of most of the existing computational models employed to infer gene regulatory network lie in that they either suffer …

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Preliminary Analysis Of An Agent-Based Model For A Tick-Borne Disease, Holly Gaff Apr 2011

Preliminary Analysis Of An Agent-Based Model For A Tick-Borne Disease, Holly Gaff

Biological Sciences Faculty Publications

Ticks have a unique life history including a distinct set of life stages and a single blood meal per life stage. This makes tick-host interactions more complex from a mathematical perspective. In addition, any model of these interactions must involve a significant degree of stochasticity on the individual tick level. In an attempt to quantify these relationships, I have developed an individual-based model of the interactions between ticks and their hosts as well as the transmission of tick-borne disease between the two populations. The results from this model are compared with those from previously published differential equation based population models. …

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Blended Biogeography-Based Optimization For Constrained Optimization, Haiping Ma, Daniel J. Simon Apr 2011

Blended Biogeography-Based Optimization For Constrained Optimization, Haiping Ma, Daniel J. Simon

Electrical and Computer Engineering Faculty Publications

Biogeography-based optimization (BBO) is a new evolutionary optimization method that is based on the science of biogeography. We propose two extensions to BBO. First, we propose a blended migration operator. Benchmark results show that blended BBO outperforms standard BBO. Second, we employ blended BBO to solve constrained optimization problems. Constraints are handled by modifying the BBO immigration and emigration procedures. The approach that we use does not require any additional tuning parameters beyond those that are required for unconstrained problems. The constrained blended BBO algorithm is compared with solutions based on a stud genetic algorithm (SGA) and standard particle swarm …

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Statistical Properties Of The Integrative Correlation Coefficient: A Measure Of Cross-Study Gene Reproducibility, Leslie Cope, Giovanni Parmigiani Jan 2011

Statistical Properties Of The Integrative Correlation Coefficient: A Measure Of Cross-Study Gene Reproducibility, Leslie Cope, Giovanni Parmigiani

Harvard University Biostatistics Working Paper Series

No abstract provided.

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Linear Methods For Analysis And Quality Control Of Relative Expression Ratios From Quantitative Real-Time Polymerase Chain Reaction Experiments, Robert B. Page, Arnold J. Stromberg Jan 2011

Linear Methods For Analysis And Quality Control Of Relative Expression Ratios From Quantitative Real-Time Polymerase Chain Reaction Experiments, Robert B. Page, Arnold J. Stromberg

Biology Faculty Publications

Relative expression quantitative real-time polymerase chain reaction (RT-qPCR) experiments are a common means of estimating transcript abundances across biological groups and experimental treatments. One of the most frequently used expression measures that results from such experiments is the relative expression ratio (RE), which describes expression in experimental samples (i.e., RNA isolated from organisms, tissues, and/or cells that were exposed to one or more experimental or nonbaseline condition) in terms of fold change relative to calibrator samples (i.e., RNA isolated from organisms, tissues, and/or cells that were exposed to a control or baseline condition). Over the past decade, several …

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Computational Network Analysis Of The Anatomical And Genetic Organizations In The Mouse Brain, Shuiwang Ji Jan 2011

Computational Network Analysis Of The Anatomical And Genetic Organizations In The Mouse Brain, Shuiwang Ji

Computer Science Faculty Publications

Motivation: The mammalian central nervous system (CNS) generates high-level behavior and cognitive functions. Elucidating the anatomical and genetic organizations in the CNS is a key step toward understanding the functional brain circuitry. The CNS contains an enormous number of cell types, each with unique gene expression patterns. Therefore, it is of central importance to capture the spatial expression patterns in the brain. Currently, genome-wide atlas of spatial expression patterns in the mouse brain has been made available, and the data are in the form of aligned 3D data arrays. The sheer volume and complexity of these data pose significant challenges …

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Weighted Scores Method For Regression Models With Dependent Data, Aristidis K. Nikoloulopoulos, Harry Joe, N. Rao Chaganty Jan 2011

Weighted Scores Method For Regression Models With Dependent Data, Aristidis K. Nikoloulopoulos, Harry Joe, N. Rao Chaganty

Mathematics & Statistics Faculty Publications

There are copula-based statistical models in the literature for regression with dependent data such as clustered and longitudinal overdispersed counts, for which parameter estimation and inference are straightforward. For situations where the main interest is in the regression and other univariate parameters and not the dependence, we propose a "weighted scores method", which is based on weighting score functions of the univariate margins. The weight matrices are obtained initially fitting a discretized multivariate normal distribution, which admits a wide range of dependence. The general methodology is applied to negative binomial regression models. Asymptotic and small-sample efficiency calculations show that our …

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Minimum Description Length Measures Of Evidence For Enrichment, Zhenyu Yang, David R. Bickel Dec 2010

Minimum Description Length Measures Of Evidence For Enrichment, Zhenyu Yang, David R. Bickel

COBRA Preprint Series

In order to functionally interpret differentially expressed genes or other discovered features, researchers seek to detect enrichment in the form of overrepresentation of discovered features associated with a biological process. Most enrichment methods treat the p-value as the measure of evidence using a statistical test such as the binomial test, Fisher's exact test or the hypergeometric test. However, the p-value is not interpretable as a measure of evidence apart from adjustments in light of the sample size. As a measure of evidence supporting one hypothesis over the other, the Bayes factor (BF) overcomes this drawback of the p-value but lacks …

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Appearance Based Stage Recognition Of Drosophila Embryos, Gopi Chand Nutakki Dec 2010

Appearance Based Stage Recognition Of Drosophila Embryos, Gopi Chand Nutakki

Masters Theses & Specialist Projects

Stages in Drosophila development denote the time after fertilization at which certain specific events occur in the developmental cycle. Stage information of a host embryo, as well as spatial information of a gene expression region is indispensable input for the discovery of the pattern of gene-gene interaction. Manual labeling of stages is becoming a bottleneck under the circumstance of high throughput embryo images. Automatic recognition based on the appearances of embryos is becoming a more desirable scheme. This problem, however, is very challenging due to severe variations of illumination and gene expressions. In this research thesis, we propose an appearance …

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Toxicogenomics Analysis Of Non-Model Transcriptomes Using Next-Generation Sequencing And Microarray, Arun Rawat Dec 2010

Toxicogenomics Analysis Of Non-Model Transcriptomes Using Next-Generation Sequencing And Microarray, Arun Rawat

Dissertations

With the advent of next generation technologies like Roche/454 Life Sciences that require low cost and less time for sequencing will help in providing a workable draft of non-model species genomes. Availability of high throughput microarray technologies for gene expression profiling provides low-cost tools for investigation of highly-integrated responses to various stimuli. These advancements along with bioinformatics processing have led to an increasing number of non-model species having well-annotated transcriptomes. The project focuses on the life cycle of development, functional annotation, and utilization of genomic tools for the avian wildlife species to determine the molecular impacts of exposure to munitions …

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Computational Biology, Harvey Greenberg, Allen Holder Nov 2010

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.

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Dorsal Eye Selector Pannier (Pnr) Suppresses The Eye Fate To Define Dorsal Margin Of The Drosophila Eye, Sarah M. Oros, Meghana Tare, Madhuri Kango-Singh, Amit Singh Oct 2010

Dorsal Eye Selector Pannier (Pnr) Suppresses The Eye Fate To Define Dorsal Margin Of The Drosophila Eye, Sarah M. Oros, Meghana Tare, Madhuri Kango-Singh, Amit Singh

Biology Faculty Publications

Axial patterning is crucial for organogenesis. During Drosophila eye development, dorso-ventral (DV) axis determination is the first lineage restriction event. The eye primordium begins with a default ventral fate, on which the dorsal eye fate is established by expression of the GATA-1 transcription factor pannier (pnr). Earlier, it was suggested that loss of pnr function induces enlargement in the dorsal eye due to ectopic equator formation. Interestingly, we found that in addition to regulating DV patterning, pnr suppresses the eye fate by downregulating the core retinal determination genes eyes absent (eya), sine oculis (so) and dacshund (dac) to define the …

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Using The R Package Crlmm For Genotyping And Copy Number Estimation, Robert B. Scharpf, Rafael Irizarry, Walter Ritchie, Benilton Carvalho, Ingo Ruczinski Sep 2010

Using The R Package Crlmm For Genotyping And Copy Number Estimation, Robert B. Scharpf, Rafael Irizarry, Walter Ritchie, Benilton Carvalho, Ingo Ruczinski

Johns Hopkins University, Dept. of Biostatistics Working Papers

Genotyping platforms such as Affymetrix can be used to assess genotype-phenotype as well as copy number-phenotype associations at millions of markers. While genotyping algorithms are largely concordant when assessed on HapMap samples, tools to assess copy number changes are more variable and often discordant. One explanation for the discordance is that copy number estimates are susceptible to systematic differences between groups of samples that were processed at different times or by different labs. Analysis algorithms that do not adjust for batch effects are prone to spurious measures of association. The R package crlmm implements a multilevel model that adjusts for …

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Genome Sequence Of The Model Mushroom Schizophyllum Commune, Robin A. Ohm, Jan F. De Jong, Luis G. Lugones, Andrea Aerts, Erika Kothe, Jason E. Stajich, Ronald P. De Vries, Eric Record, Anthony Levasseur, Scott E. Baker, Kirk A. Bartholomew, Pedro M. Coutinho, Susann Erdmann, Thomas J. Fowler, Allen C. Gathmen, Vincent Lombard, Bernard Henrissat, Nicole Knabe, Ursula Kues, Walt W. Lily Sep 2010

Genome Sequence Of The Model Mushroom Schizophyllum Commune, Robin A. Ohm, Jan F. De Jong, Luis G. Lugones, Andrea Aerts, Erika Kothe, Jason E. Stajich, Ronald P. De Vries, Eric Record, Anthony Levasseur, Scott E. Baker, Kirk A. Bartholomew, Pedro M. Coutinho, Susann Erdmann, Thomas J. Fowler, Allen C. Gathmen, Vincent Lombard, Bernard Henrissat, Nicole Knabe, Ursula Kues, Walt W. Lily

Biology Faculty Publications

Much remains to be learned about the biology of mushroom-forming fungi, which are an important source of food, secondary metabolites and industrial enzymes. The wood-degrading fungus Schizophyllum commune is both a genetically tractable model for studying mushroom development and a likely source of enzymes capable of efficient degradation of lignocellulosic biomass. Comparative analyses of its 38.5-megabase genome, which encodes 13,210 predicted genes, reveal the species's unique wood-degrading machinery. One-third of the 471 genes predicted to encode transcription factors are differentially expressed during sexual development of S. commune. Whereas inactivation of one of these, fst4, prevented mushroom formation, inactivation of another, …

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G-Lattices For An Unrooted Perfect Phylogeny, Monica Grigg Aug 2010

G-Lattices For An Unrooted Perfect Phylogeny, Monica Grigg

Mathematical Sciences Technical Reports (MSTR)

We look at the Pure Parsimony problem and the Perfect Phylogeny Haplotyping problem. From the Pure Parsimony problem we consider structures of genotypes called g-lattices. These structures either provide solutions or give bounds to the pure parsimony problem. In particular, we investigate which of these structures supports an unrooted perfect phylogeny, a condition that adds biological interpretation. By understanding which g-lattices support an unrooted perfect phylogeny, we connect two of the standard biological inference rules used to recreate how genetic diversity propagates across generations.

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A Perturbation Method For Inference On Regularized Regression Estimates, Jessica Minnier, Lu Tian, Tianxi Cai Aug 2010

A Perturbation Method For Inference On Regularized Regression Estimates, Jessica Minnier, Lu Tian, Tianxi Cai

Harvard University Biostatistics Working Paper Series

No abstract provided.

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A Decision-Theory Approach To Interpretable Set Analysis For High-Dimensional Data, Simina Maria Boca, Hector C. Bravo, Brian Caffo, Jeffrey T. Leek, Giovanni Parmigiani Jul 2010

A Decision-Theory Approach To Interpretable Set Analysis For High-Dimensional Data, Simina Maria Boca, Hector C. Bravo, Brian Caffo, Jeffrey T. Leek, Giovanni Parmigiani

Johns Hopkins University, Dept. of Biostatistics Working Papers

A ubiquitous problem in igh-dimensional analysis is the identification of pre-defined sets that are enriched for features showing an association of interest. In this situation, inference is performed on sets, not individual features. We propose an approach which focuses on estimating the fraction of non-null features in a set. We search for unions of disjoint sets (atoms), using as the loss function a weighted average of the number of false and missed discoveries. We prove that the solution is equivalent to thresholding the atomic false discovery rate and that our approach results in a more interpretable set analysis.

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Improved Ibd Detection Using Incomplete Haplotype Information, Giulio Genovese, Gregory Leibon, Martin R. Pollak, Daniel N. Rockmore Jun 2010

Improved Ibd Detection Using Incomplete Haplotype Information, Giulio Genovese, Gregory Leibon, Martin R. Pollak, Daniel N. Rockmore

Dartmouth Scholarship

The availability of high density genetic maps and genotyping platforms has transformed human genetic studies. The use of these platforms has enabled population-based genome-wide association studies. However, in inheritance-based studies, current methods do not take full advantage of the information present in such genotyping analyses. In this paper we describe an improved method for identifying genetic regions shared identical-by-descent (IBD) from recent common ancestors. This method improves existing methods by taking advantage of phase information even if it is less than fully accurate or missing. We present an analysis of how using phase information increases the accuracy of IBD detection …

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The Strength Of Statistical Evidence For Composite Hypotheses: Inference To The Best Explanation, David R. Bickel Jun 2010

The Strength Of Statistical Evidence For Composite Hypotheses: Inference To The Best Explanation, David R. Bickel

COBRA Preprint Series

A general function to quantify the weight of evidence in a sample of data for one hypothesis over another is derived from the law of likelihood and from a statistical formalization of inference to the best explanation. For a fixed parameter of interest, the resulting weight of evidence that favors one composite hypothesis over another is the likelihood ratio using the parameter value consistent with each hypothesis that maximizes the likelihood function over the parameter of interest. Since the weight of evidence is generally only known up to a nuisance parameter, it is approximated by replacing the likelihood function with …

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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 Jun 2010

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 …

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Powerful Snp Set Analysis For Case-Control Genome Wide Association Studies, Michael C. Wu, Peter Kraft, Michael P. Epstein, Deanne M. Taylor, Stephen J. Chanock, David J. Hunter, Xihong Lin May 2010

Powerful Snp Set Analysis For Case-Control Genome Wide Association Studies, Michael C. Wu, Peter Kraft, Michael P. Epstein, Deanne M. Taylor, Stephen J. Chanock, David J. Hunter, Xihong Lin

Harvard University Biostatistics Working Paper Series

No abstract provided.

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Error Correcting Codes And The Human Genome., Suzanne Mclean Lyle May 2010

Error Correcting Codes And The Human Genome., Suzanne Mclean Lyle

Electronic Theses and Dissertations

In this work, we study error correcting codes and generalize the concepts with a view toward a novel application in the study of DNA sequences. The author investigates the possibility that an error correcting linear code could be included in the human genome through application and research. The author finds that while it is an accepted hypothesis that it is reasonable that some kind of error correcting code is used in DNA, no one has actually been able to identify one. The author uses the application to illustrate how the subject of coding theory can provide a teaching enrichment activity …

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