bluuebunny/crossref_metadata_2025_split huggingface dataset:
A joint regression modeling framework for analyzing bivariate binary data in R
Authors: Marra Giampiero , Radice Rosalba 
Published: December 2017

Abstract
We discuss some of the features of the R add-on package GJRM which implements a flexible joint modeling framework for fitting a number of multivariate response regression models under various sampling schemes. In particular,we focus on the case inwhich the user wishes to fit bivariate binary regression models in the presence of several forms of selection bias. The framework allows for Gaussian and non-Gaussian dependencies through the use of copulae, and for the association and mean parameters to depend on flexible functions of covariates. We describe some of the methodological details underpinning the bivariate binary models implemented in the package and illustrate them by fitting interpretable models of different complexity on three data-sets.
ImageServer, a Tool for On-line Processing and Analysis of Biological Images
Authors: Pisarev Andrei , Poustelnikova Ekaterina , Kozlov Konstantin , Myasnikova Ekaterina , Samsonova Maria , Reinitz John 
Published: December 2017

Abstract We present a novel tool for processing and analysis of images of gene expression patterns stored in a relational database. This tool, known as ImageServer, is portable across different software/hardware platforms and supports both basic and subject domain oriented operations on images. The tool is used to process and analyze images stored in FlyEx database (http://urchin.spbcas.ru/flyex); software and documentation are available for download from http://urchin.spbcas.ru/downloads/IS/IS.htm.
Stochastic activation of the response regulator PhoB by noncognate histidine kinases
Authors: Zhou Lu , Grégori Gérald , Blackman Jennifer Masella , Robinson J. Paul , Wanner Barry L. 
Published: December 2017

Abstract Two-component systems (TCS) are the most prevalent gene regulatory mechanism in bacteria. A typical TCS is comprised of a histidine kinase (HK) and a partner response regulator (RR). Specific environment signals lead to autophosphorylation of different HKs, which in turn act as phosphoryl donors for autophosphorylation of their partner RRs. Nonpartner HKs and RRs also interact, giving rise to cross regulation among TCSs in response to diverse signals.PhoR (HK) and PhoB (RR) constitute the TCS for detection of environmental (extracellular) inorganic phosphate (Pi). The PhoR/PhoB TCS controls the expression of a large number of genes for acquisition of alternative phosphorus sources, including phoA, which encodes the non-specific phosphohydrolase bacterial alkaline phosphatase (Bap). Cross activation of PhoB by the nonpartner HK CreC is now a classic example of cross regulation among TCSs. A systematic search for other cross talking HKs revealed five additional HKs that activate (phosphorylate) PhoB (J. M. B. and B. L. W., unpublished data).Examination of cross activation of PhoB by these non-partner HKs by flow cytometry at the single-cell level revealed a bimodal, “all-or-none,” distribution pattern for expression of a phoAp-gfp (green fluorescent protein) reporter fusion. Although the basis of the observed stochastic behavior is unclear, it seems to reflect an inherent property of TCSs. We propose that cells exploit the stochastic character of TCSs to achieve nongenetic (epigenetic) diversity within genetically homogeneous cell populations in order to facilitate adaptation to environmental changes.
A system for integrative and post-planned analysis of 2-DE/MS centered proteomics data
Authors: Wang Juhui , Caron Christophe , He Xuefeng , Carpentier Audrey , Mistou Michel-Yves , Trubuil Alain , Gitton Christophe , Henry Céline , Guillot Alain 
Published: December 2017

Summary Proteomic analysis is intrinsically an iterative, incremental process. Information is usually acquired gradually by researchers, and in different projects. At the same time, there are relatively few examples of biological data management systems which take into account this reality, most of them usually treat the experiment generated data as static and unchangeable: data are never reconsidered, or seldom, whereas technology becomes more powerful or that other researchers have brought information on data correction. And yet, post-planned analysis [21] which involves multiple iterations and subsequent re-investigations of previously prepared data might bring tremendous benefits.Named PARIS (Proteomic Analysis and Resources Indexation System), the system we developed here seeks to address this requirement. Compliant with the majority of 2-DE analysis and MALDI-TOF based protein identification softwares, it automatically takes data from them and stores the raw and processed data in a relational database suitable for advanced exploration. Taking into account the standards proposed by PSI (Proteomics Standard Initiative), the system exports the stored data in XML format for data exchange and knowledge sharing. PARIS also manages information about experiments and their biological contexts, and allows the user to search and analyze a large data collection in a global manner. It provides tools for data integration and advanced, cross multi-experiment, multi-experimenter data exploration, and supports visual verification and correction of the analysis results. Implemented in Java, the system is platform independent, accessible to multiple users through Internet. It is also scalable for use for one or many laboratories, and therefore suitable to inter-institute collaborative work.PARIS can be tested and downloaded at http://genome.jouy.inra.fr/paris
Managing Evidence from Multiple Gene Finding Resources via an XML-based Integration Architecture
Authors: Malousi Andigoni , Koutkias Vassilis , Maglaveras Nicos 
Published: December 2017

SummaryWhile biological processes underlying gene expression are still under experimental research, computational gene prediction techniques have reached high level of sophistication with the employment of efficient intrinsic and extrinsic methods that identify protein-coding regions within query genomic sequences. Their ability though to delineate the exact exon boundaries is characterized by a trade off between sensitivity and specificity and still is prone to alternations in gene regulation during transcription and splicing and to inherent complexities introduced by the implemented methodology. Evaluation studies have shown that combinatorial approaches exhibit improved accuracy levels through the integration of evidence data from multiple resources that are further assessed in order to end up with the most probable gene assembly.In this work, we present an integration and information handling architecture that exploits evidence derived from multiple gene finding resources, in order to generate machine-readable representations of optimal/suboptimal gene structure predictions, signal features identification and high scoring similarity matches. Unlike most combinatorial techniques, which end up with the most probable gene assembly, the objective of this architecture is to support advanced information handling mechanisms that may give more in depth insights on the underlying gene expression machinery and the alternations that may occur. Technically, XML was adopted to build and interchange structured data among the architecture’s components together with relevant technologies offering graphical representations and queries formulation/execution over single/multiple information sources.
Kinetic Modelling with the Systems Biology Modelling Environment SyBME
Authors: Junker Björn H. , Koschützki Dirk , Schreiber Falk 
Published: December 2017

Summary Kinetic modelling and simulation is an important approach in systems biology. While the focus of current modelling tools is on simulation, model development is a highly iterative process which is currently only partly supported. To support the development of biochemical models, their simulation, and graphical understanding, we designed and implemented SyBME, the Systems Biology Modelling Environment. Here we present the architecture and the main components of SyBME and show its use by modelling sucrose breakdown in developing potato tubers.
Functional classification of transcription factor binding sites: Information content as a metric
Authors: Reddy D. Ashok , Prasad B. V. L. S. , Mitra Chanchal K. 
Published: December 2017

Summary The information content (relative entropy) of transcription factor binding sites (TFBS) is used to classify the transcription factors (TFs). The TF classes are clustered based on the TFBS clustering using information content. Any TF belonging to the TF class cluster has a chance of binding to any TFBS of the clustered group. Thus, out of the 41 TFBS (in humans), perhaps only 5 -10 TFs may be actually needed and in case of mouse instead of 13 TFs, we may have actually 5 or so TFs. The JASPAR database of TFBS are used in this study. The experimental data on TFs of specific gene expression from TRRD database is also coinciding with our computational results. This gives us a new way to look at the protein classification- not based on their structure or function but by the nature of their TFBS.
CoryneRegNet 2: An Integrative Bioinformatics Approach for Reconstruction and Comparison of Transcriptional Regulatory Networks in Prokaryotes
Authors: Baumbach Jan , Brinkrolf Karina , Wittkop Tobias , Tauch Andreas , Rahmann Sven 
Published: December 2017

SummaryCoryneRegNet is an ontology-based data warehouse of corynebacterial transcription factors and regulatory networks. Initially, it was designed to provide methods for the analysis and visualization of the gene regulatory network of Corynebacterium glutamicum. Now we integrated the genomes and transcriptional interactions of three other corynebacteria, C. diphtheriae, C. efficiens, and C. jeikeium into CoryneRegNet; providing comparative analysis and visualization with GraphVis. We also integrated the high-performance PSSM search tool PoSSuM search to detect potential transcription factor binding sites within and across species. As an application, we reconstruct in silico the regulatory network of the iron metabolism regulator DtxR in the four corynebacteria.CoryneRegNet is freely accessible at https://www.cebitec.uni-bielefeld.de/groups/gi/software/coryneregnet/. The final slash (/) is mandatory. In order to use the GraphVis feature, Java (at least version 1.4.2) is required.
InSilico Proteomics System: Integration and Application of Protein and Protein-Protein Interaction Data using Microsoft .NET
Authors: Straßer Wolfgang , Siegl Doris , Önder Kamil , Bauer Johann 
Published: December 2017

Summary In the last decades, biological databases became the major knowledge resource for researchers in the field of molecular biology. The distribution of information among these databases is one of the major problems. An overview about the subject area of data access and representation of protein and protein-protein interaction data within public biological databases is described. For a comprehensive and consistent way of searching and analysing integrated protein and protein-protein interaction data, the InSilico Proteomics (ISP) project has been initiated. Its three main objectives are (1) to provide an integrated knowledge pool for data investigation and global network analysis functions for a better understanding of a cell’s interactome, (2) employment of public data for plausibility analysis and validation of in-house experimental data and (3) testing the applicability of Microsoft’s .NET architecture for bioinformatics applications. Data integrated into the ISP database can be queried through the Web portal PRIMOS (PRotein Interaction and MOlecule Search) which is freely available at http://biomis.fh-hagenberg.at/isp/primos.
Metabolite profiles as a reflection of physiological status – a methodological validation
Authors: Steinfath Matthias , Repsilber Dirk , Hische Manuela , Schauer Nicolas , Fernie Alisdair R. , Selbig Joachim 
Published: December 2017

Summary Biological ”omics” data comprise numerous variables (metabolites, gene expression, physiological quantities) and comparatively few samples. These samples represent either measurements for slightly different genotypes in identical environments, or for different environmental conditions affecting the same genotype. Given this kind of data, it is intriguing to ask for possible measurable associations between molecular variables and the phenotypical or physiological status.To evaluate such correlations we need a model for the functional dependency of the physiological state on given molecular variables. Supervised machine learning methods such as neural networks, decision trees, or support vector machines may be used to reveal such correlations. The simplest model is certainly a linear approach. To investigate the association between molecular and phenotypical variables, we ask if the correlation between predictor and response is statistically significant, and how much of the phenotypical variance of the response can be explained by a given set of predictors. When confronted with a set of molecular data not all of them are generally relevant for each physiological trait. Given this fact the problem of feature selection arises.Different regression methods have been developed to answer this question: Ordinary Least Squares (OLS) yields an unbiased solution, but normally has a high mean square error. In particular, there is no dimension reduction included in this method and, hence, overfitting is a critical problem. In contrast, Principle Component Regression (PCR) offers such a dimension reduction, however, the principle components are found without considering the response. Partial Least Squares Regression (PLSR) is utilised as an alternative method since it considers the variance within the predictors as well as between predictors and response, whilst Ridge Regression is a further alternative worthy of consideration.In our study we applied these methods to data resulting from a tomato metabolite experimental series. Comparison of the results for this dataset with experimentally relevant correlation structure between variables and samples allows us to test the relative merits of the regression methods with respect to the questions raised above. Given certain prerequisite knowledge it also allows us to conjecture the true biological correlation. Our results show that under most circumstances OLS is worst with respect to prediction. However, the ranking of methods seems to change considerably if the question of feature selection is considered. Understanding and discussing these differences is a relevant contribution to the task of choice of suitable approach of correlation analysis for “omics” datasets with respect to the biological interpretation in question.
Modeling Microarray Data: Interpreting and communicating the biological results
Authors: Pittelkow Y. E. , Wilson S. R. 
Published: December 2017

Summary Various statistical models have been proposed for detecting differential gene expression in data from microarray experiments. Given such detection, we are usually interested in describing the differential expression patterns. Due to the large number of genes that are typically analysed in microarray experiments, possibly more than ten thousand, the tasks of interpretation and communication of all the corresponding statistical models pose a considerable challenge, except perhaps in the simplest experiment involving only two groups. A further challenge is to find methods to summarize the resulting models. These challenges increase with experimental complexity.Biologists often wish to sort genes into ‘classes’ with similar response profiles/patterns. So, in this paper we describe a likelihood approach for assigning genes to these different class patterns for data from a replicated experimental design.The number of potential patterns increases very quickly as the number of combinations in the experimental design increases. In a two group experimental design there are only three patterns required to describe the mean response: up, down and no difference. For a factorial design with three treatments there are 13 different patterns, and with four levels there are 75 potential patterns to be considered, and so on. The approach is applied to the identification of differential response patterns in gene expression from a microarray experiment using RNAextracted from the leaves of Arabidopsis thaliana plants. We compare patterns of response found using additive and multiplicative models. A multiplicative model is more commonly used in the statistical analysis of microarray data because of the variance stabilizing properties of the logarithmic function. Then the error structure of the model is taken to be log-Normal. On the other hand, for the additive model the gene expression value is modeled directly as being from a gamma distribution which successfully accounts for the constant coefficient of variation often observed. Appropriate visualization displays for microarray data are important as a way of communicating the patterns of response amongst the genes. Here we use graphical ‘icons’ to represent the patterns of up/down and no response and two alternative displays, the Gene-plot and a grid layout to provide rapid overall summaries of the gene expression patterns.
Inference of Gene Coexpression Networks by Integrative Analysis across Microarray Experiments
Authors: Elo Laura L. , Aittokallio Tero 
Published: December 2017

Summary We improve the reliability of detecting coexpressed gene pairs from microarray data by introducing a novel probe-level quality-weighted similarity measure for combining data across different Affymetrix experiments. In construction of gene coexpression networks, the proposed procedure is less sensitive to noise than the corresponding single-experiment approaches or the conventional integrative approaches, even when a relatively small number of samples and conditions is available. The present results indicate how the accumulated microarray data can be effectively exploited to increase the quality of the inferred networks. In particular, we demonstrate its biological relevance in identifying coexpressions in mouse T helper cell differentiation.
Combining biomedical knowledge and transcriptomic data to extract new knowledge on genes
Authors: Guérin Emilie , Marquet Gwenaëlle , Chabalier Julie , Troadec Marie-Bérengère , Guguen-Guillouzo Christiane , Loréal Olivier , Burgun Anita , Moussouni Fouzia 
Published: December 2017

Abstract In biomedical research, interpretation of microarray data requires confrontation of data and knowledge from heterogeneous resources, either in the biomedical domain or in genomics, as well as restitution and analysis methods adapted to huge amounts of data. We present a combined approach that relies on two components: BioMeKE annotates sets of genes using biomedical GO and UMLS concepts, and GEDAW, a Gene Expression Data Warehouse, uses BioMeKE to enrich experimental results with biomedical concepts, thus performing complex analyses of expression measurements through analysis workflows. The strength of our approach has been demonstrated within the framework of analysis of data resulting from the liver transcriptome study. It allowed new genes potentially associated with liver diseases to be highlighted.
Exact and Approximate Stochastic Simulations of the MAPK Pathway and Comparisons of Simulations’ Results
Authors: Purutçuoǧlu Vilda , Wit Ernst 
Published: December 2017

Summary The MAPK (mitogen-activated protein kinase) or its synonymous ERK (extracellular signal regulated kinase) pathway whose components are Ras, Raf, and MEK proteins with many biochemical links, is one of the major signalling systems involved in cellular growth control of eukaryotes including cell proliferation, transformation, differentiation, and apoptosis. In this study we describe the MAPK/ERK pathway via (quasi) biochemical reactions and then implement the pathway by a stochastic Markov process. A novelty of our approach is to use multiple parametrizations in order to deal with molecules for which localization in the cell is an intricate part of the dynamic process and to describe the protein using different binding sites and various phosphorylations. We simulate the system by exact and different approximate simulations, e.g. via the Poisson τ-leap, the Binomial τ-leap and the diffusion methods, in which we introduce a new updating plan for dependent columns of the diffusion matrix. Finally we compare the results of different algorithms by the current biological knowledge and find out new relations about this complex system.
An assessment of machine and statistical learning approaches to inferring networks of protein-protein interactions
Authors: Browne Fiona , Wang Haiying , Zheng Huiru , Azuaje Francisco 
Published: December 2017

Abstract Protein-protein interactions (PPI) play a key role in many biological systems. Over the past few years, an explosion in availability of functional biological data obtained from high-throughput technologies to infer PPI has been observed. However, results obtained from such experiments show high rates of false positives and false negatives predictions as well as systematic predictive bias. Recent research has revealed that several machine and statistical learning methods applied to integrate relatively weak, diverse sources of large-scale functional data may provide improved predictive accuracy and coverage of PPI. In this paper we describe the effects of applying different computational, integrative methods to predict PPI in Saccharomyces cerevisiae. We investigated the predictive ability of combining different sets of relatively strong and weak predictive datasets. We analysed several genomic datasets ranging from mRNA co-expression to marginal essentiality. Moreover, we expanded an existing multi-source dataset from S. cerevisiae by constructing a new set of putative interactions extracted from Gene Ontology (GO)- driven annotations in the Saccharomyces Genome Database. Different classification techniques: Simple Naive Bayesian (SNB), Multilayer Perceptron (MLP) and K-Nearest Neighbors (KNN) were evaluated. Relatively simple classification methods (i.e. less computing intensive and mathematically complex), such as SNB, have been proven to be proficient at predicting PPI. SNB produced the “highest” predictive quality obtaining an area under Receiver Operating Characteristic (ROC) curve (AUC) value of 0.99. The lowest AUC value of 0.90 was obtained by the KNN classifier. This assessment also demonstrates the strong predictive power of GO-driven models, which offered predictive performance above 0.90 using the different machine learning and statistical techniques. As the predictive power of single-source datasets became weaker MLP and SNB performed better than KNN. Moreover, predictive performance saturation may be reached independently of the classification models applied, which may be explained by predictive bias and incompleteness of existing “Gold Standards”. More comprehensive and accurate PPI maps will be produced for S. cerevisiae and beyond with the emergence of largescale datasets of better predictive quality and the integration of intelligent classification methods.