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Articles 1 - 30 of 33
Full-Text Articles in Computational Biology
Minimum Description Length Measures Of Evidence For Enrichment, Zhenyu Yang, David R. Bickel
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 …
Appearance Based Stage Recognition Of Drosophila Embryos, Gopi Chand Nutakki
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 …
Toxicogenomics Analysis Of Non-Model Transcriptomes Using Next-Generation Sequencing And Microarray, Arun Rawat
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 …
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.
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
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 …
Using The R Package Crlmm For Genotyping And Copy Number Estimation, Robert B. Scharpf, Rafael Irizarry, Walter Ritchie, Benilton Carvalho, Ingo Ruczinski
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 …
Sample Size And Statistical Power Considerations In High-Dimensionality Data Settings: A Comparative Study Of Classification Algorithms, Yu Guo, Armin Garber, Raji Balasubramanian
Sample Size And Statistical Power Considerations In High-Dimensionality Data Settings: A Comparative Study Of Classification Algorithms, Yu Guo, Armin Garber, Raji Balasubramanian
Raji Balasubramanian
Background: Data generated using ‘omics’ technologies are characterized by high dimensionality, where the number of features measured per subject vastly exceeds the number of subjects in the study. In this paper, we consider issues relevant in the design of biomedical studies in which the goal is the discovery of a subset of features and an associated algorithm that can predict a binary outcome, such as disease status. We compare the performance of four commonly used classifiers (K-Nearest Neighbors, Prediction Analysis for Microarrays, Random Forests and Support Vector Machines) in high-dimensionality data settings. We evaluate the effects of varying levels of …
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
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, …
G-Lattices For An Unrooted Perfect Phylogeny, Monica Grigg
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.
A Perturbation Method For Inference On Regularized Regression Estimates, Jessica Minnier, Lu Tian, Tianxi Cai
A Perturbation Method For Inference On Regularized Regression Estimates, Jessica Minnier, Lu Tian, Tianxi Cai
Harvard University Biostatistics Working Paper Series
No abstract provided.
The Maximum Clique Problem: Algorithms, Applications, And Implementations, John David Eblen
The Maximum Clique Problem: Algorithms, Applications, And Implementations, John David Eblen
Doctoral Dissertations
Computationally hard problems are routinely encountered during the course of solving practical problems. This is commonly dealt with by settling for less than optimal solutions, through the use of heuristics or approximation algorithms. This dissertation examines the alternate possibility of solving such problems exactly, through a detailed study of one particular problem, the maximum clique problem. It discusses algorithms, implementations, and the application of maximum clique results to real-world problems. First, the theoretical roots of the algorithmic method employed are discussed. Then a practical approach is described, which separates out important algorithmic decisions so that the algorithm can be easily …
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
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.
Molecular Probes For The Detection Of Cyanophage As-1 And Its Cyanobacterial Hosts., Tin-Chun Chu, Jonathan Jimenez, Lauren Strawn, Michelle Reed, Lauren Pohren, Lee Lee
Molecular Probes For The Detection Of Cyanophage As-1 And Its Cyanobacterial Hosts., Tin-Chun Chu, Jonathan Jimenez, Lauren Strawn, Michelle Reed, Lauren Pohren, Lee Lee
Tin-Chun Chu, Ph.D.
No abstract provided.
Improved Ibd Detection Using Incomplete Haplotype Information, Giulio Genovese, Gregory Leibon, Martin R. Pollak, Daniel N. Rockmore
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 …
The Strength Of Statistical Evidence For Composite Hypotheses: Inference To The Best Explanation, David R. Bickel
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 …
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 …
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
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.
Error Correcting Codes And The Human Genome., Suzanne Mclean Lyle
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 …
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.
Why Genes Evolve Faster On Secondary Chromosomes In Bacteria, Vaughn S. Cooper, Samuel H. Vohr, Sarah C. Wrocklage, Philip J. Hatcher
Why Genes Evolve Faster On Secondary Chromosomes In Bacteria, Vaughn S. Cooper, Samuel H. Vohr, Sarah C. Wrocklage, Philip J. Hatcher
Molecular, Cellular & Biomedical Sciences
In bacterial genomes composed of more than one chromosome, one replicon is typically larger, harbors more essential genes than the others, and is considered primary. The greater variability of secondary chromosomes among related taxa has led to the theory that they serve as an accessory genome for specific niches or conditions. By this rationale, purifying selection should be weaker on genes on secondary chromosomes because of their reduced necessity or usage. To test this hypothesis we selected bacterial genomes composed of multiple chromosomes from two genera, Burkholderia and Vibrio, and quantified the evolutionary rates (dN and dS) of all orthologs …
Permutation-Based Pathway Testing Using The Super Learner Algorithm, Paul Chaffee, Alan E. Hubbard, Mark L. Van Der Laan
Permutation-Based Pathway Testing Using The Super Learner Algorithm, Paul Chaffee, Alan E. Hubbard, Mark L. Van Der Laan
U.C. Berkeley Division of Biostatistics Working Paper Series
Many diseases and other important phenotypic outcomes are the result of a combination of factors. For example, expression levels of genes have been used as input to various statistical methods for predicting phenotypic outcomes. One particular popular variety is the so-called gene set enrichment analysis (GSEA). This paper discusses an augmentation to an existing strategy to estimate the significance of an associations between a disease outcome and a predetermined combination of biological factors, based on a specific data adaptive regression method (the "Super Learner," van der Laan et al., 2007). The procedure uses an aggressive search procedure, potentially resulting in …
Accurate Genome-Scale Percentage Dna Methylation Estimates From Microarray Data, Martin J. Aryee, Zhijin Wu, Christine Ladd-Acosta, Brian Herb, Andrew P. Feinberg, Srinivasan Yegnasurbramanian, Rafael A. Irizarry
Accurate Genome-Scale Percentage Dna Methylation Estimates From Microarray Data, Martin J. Aryee, Zhijin Wu, Christine Ladd-Acosta, Brian Herb, Andrew P. Feinberg, Srinivasan Yegnasurbramanian, Rafael A. Irizarry
Johns Hopkins University, Dept. of Biostatistics Working Papers
DNA methylation is a key regulator of gene function in a multitude of both normal and abnormal biological processes, but tools to elucidate its roles on a genome-wide scale are still in their infancy. Methylation sensitive restriction enzymes and microarrays provide a potential high-throughput, low-cost platform to allow methylation profiling. However, accurate absolute methylation estimates have been elusive due to systematic errors and unwanted variability. Previous microarray pre-processing procedures, mostly developed for expression arrays, fail to adequately normalize methylation-related data since they rely on key assumptions that are violated in the case of DNA methylation. We develop a normalization strategy …
Modeling Dependent Gene Expression, Donatello Telesca, Peter Muller, Giovanni Parmigiani, Ralph S. Freedman
Modeling Dependent Gene Expression, Donatello Telesca, Peter Muller, Giovanni Parmigiani, Ralph S. Freedman
Harvard University Biostatistics Working Paper Series
No abstract provided.
Wavelet Based Functional Models For Transcriptome Analysis With Tiling Arrays, Lieven Clement, Kristof Debeuf, Ciprian Crainiceanu, Olivier Thas, Marnik Vuylsteke, Rafael Irizarry
Wavelet Based Functional Models For Transcriptome Analysis With Tiling Arrays, Lieven Clement, Kristof Debeuf, Ciprian Crainiceanu, Olivier Thas, Marnik Vuylsteke, Rafael Irizarry
Johns Hopkins University, Dept. of Biostatistics Working Papers
For a better understanding of the biology of an organism a complete description is needed of all regions of the genome that are actively transcribed. Tiling arrays can be used for this purpose. Such arrays allow the discovery of novel transcripts and the assessment of differential expression between two or more experimental conditions such as genotype, treatment, tissue, etc. Much of the initial methodological efforts were designed for transcript discovery, while more recent developments also focus on differential expression. To our knowledge no methods for tiling arrays are described in the literature that can both assess transcript discovery and identify …
An Integrative -Omics Approach To Identify Functional Sub-Networks In Human Colorectal Cancer, Rod K. Nibbe, Mehmet Koyutürk, Mark R. Chance
An Integrative -Omics Approach To Identify Functional Sub-Networks In Human Colorectal Cancer, Rod K. Nibbe, Mehmet Koyutürk, Mark R. Chance
Faculty Scholarship
Emerging evidence indicates that gene products implicated in human cancers often cluster together in "hot spots" in protein-protein interaction (PPI) networks. Additionally, small sub-networks within PPI networks that demonstrate synergistic differential expression with respect to tumorigenic phenotypes were recently shown to be more accurate classifiers of disease progression when compared to single targets identified by traditional approaches. However, many of these studies rely exclusively on mRNA expression data, a useful but limited measure of cellular activity. Proteomic profiling experiments provide information at the post-translational level, yet they generally screen only a limited fraction of the proteome. Here, we demonstrate that …
Bayesian Methods For Network-Structured Genomics Data, Stefano Monni, Hongzhe Li
Bayesian Methods For Network-Structured Genomics Data, Stefano Monni, Hongzhe Li
UPenn Biostatistics Working Papers
Graphs and networks are common ways of depicting information. In biology, many different processes are represented by graphs, such as regulatory networks, metabolic pathways and protein-protein interaction networks. This information provides useful supplement to the standard numerical genomic data such as microarray gene expression data. Effectively utilizing such an information can lead to a better identification of biologically relevant genomic features in the context of our prior biological knowledge. In this paper, we present a Bayesian variable selection procedure for network-structured covariates for both Gaussian linear and probit models. The key of our approach is the introduction of a Markov …
Wavelet-Based Functional Linear Mixed Models: An Application To Measurement Error–Corrected Distributed Lag Models, Elizabeth J. Malloy, Jeffrey S. Morris, Sara D. Adar, Helen Suh, Diane R. Gold, Brent A. Coull
Wavelet-Based Functional Linear Mixed Models: An Application To Measurement Error–Corrected Distributed Lag Models, Elizabeth J. Malloy, Jeffrey S. Morris, Sara D. Adar, Helen Suh, Diane R. Gold, Brent A. Coull
Jeffrey S. Morris
Frequently, exposure data are measured over time on a grid of discrete values that collectively define a functional observation. In many applications, researchers are interested in using these measurements as covariates to predict a scalar response in a regression setting, with interest focusing on the most biologically relevant time window of exposure. One example is in panel studies of the health effects of particulate matter (PM), where particle levels are measured over time. In such studies, there are many more values of the functional data than observations in the data set so that regularization of the corresponding functional regression coefficient …
Members’ Discoveries: Fatal Flaws In Cancer Research, Jeffrey S. Morris
Members’ Discoveries: Fatal Flaws In Cancer Research, Jeffrey S. Morris
Jeffrey S. Morris
A recent article published in The Annals of Applied Statistics (AOAS) by two MD Anderson researchers—Keith Baggerly and Kevin Coombes—dissects results from a highly-influential series of medical papers involving genomics-driven personalized cancer therapy, and outlines a series of simple yet fatal flaws that raises serious questions about the veracity of the original results. Having immediate and strong impact, this paper, along with related work, is providing the impetus for new standards of reproducibility in scientific research.
Statistical Contributions To Proteomic Research, Jeffrey S. Morris, Keith A. Baggerly, Howard B. Gutstein, Kevin R. Coombes
Statistical Contributions To Proteomic Research, Jeffrey S. Morris, Keith A. Baggerly, Howard B. Gutstein, Kevin R. Coombes
Jeffrey S. Morris
Proteomic profiling has the potential to impact the diagnosis, prognosis, and treatment of various diseases. A number of different proteomic technologies are available that allow us to look at many proteins at once, and all of them yield complex data that raise significant quantitative challenges. Inadequate attention to these quantitative issues can prevent these studies from achieving their desired goals, and can even lead to invalid results. In this chapter, we describe various ways the involvement of statisticians or other quantitative scientists in the study team can contribute to the success of proteomic research, and we outline some of the …
Informatics And Statistics For Analyzing 2-D Gel Electrophoresis Images, Andrew W. Dowsey, Jeffrey S. Morris, Howard G. Gutstein, Guang Z. Yang
Informatics And Statistics For Analyzing 2-D Gel Electrophoresis Images, Andrew W. Dowsey, Jeffrey S. Morris, Howard G. Gutstein, Guang Z. Yang
Jeffrey S. Morris
Whilst recent progress in ‘shotgun’ peptide separation by integrated liquid chromatography and mass spectrometry (LC/MS) has enabled its use as a sensitive analytical technique, proteome coverage and reproducibility is still limited and obtaining enough replicate runs for biomarker discovery is a challenge. For these reasons, recent research demonstrates the continuing need for protein separation by two-dimensional gel electrophoresis (2-DE). However, with traditional 2-DE informatics, the digitized images are reduced to symbolic data though spot detection and quantification before proteins are compared for differential expression by spot matching. Recently, a more robust and automated paradigm has emerged where gels are directly …