bluuebunny/crossref_metadata_2025_split huggingface dataset:
Data Integration for Spatio-Temporal Patterns of Gene Expression of Zebrafish development: the GEMS database
Authors: Belmamoune Mounia , Verbeek Fons J. 
Published: December 2017

SummaryThe Gene Expression Management System (GEMS) is a database system for patterns of gene expression. These patterns result from systematic whole-mount fluorescent in situ hybridization studies on zebrafish embryos. GEMS is an integrative platform that addresses one of the important challenges of developmental biology: how to integrate genetic data that underpin morphological changes during embryogenesis. Our motivation to build this system was by the need to be able to organize and compare multiple patterns of gene expression at tissue level. Integration with other developmental and biomolecular databases will further support our understanding of development. The GEMS operates in concert with a database containing a digital atlas of zebrafish embryo; this digital atlas of zebrafish development has been conceived prior to the expansion of the GEMS. The atlas contains 3D volume models of canonical stages of zebrafish development in which in each volume model element is annotated with an anatomical term. These terms are extracted from a formal anatomical ontology, i.e. the Developmental Anatomy Ontology of Zebrafish (DAOZ). In the GEMS, anatomical terms from this ontology together with terms from the Gene Ontology (GO) are also used to annotate patterns of gene expression and in this manner providing mechanisms for integration and retrieval . The annotations are the glue for integration of patterns of gene expression in GEMS as well as in other biomolecular databases. At the one hand, zebrafish anatomy terminology allows gene expression data within GEMS to be integrated with phenotypical data in the 3D atlas of zebrafish development. At the other hand, GO terms extend GEMS expression patterns integration to a wide range of bioinformatics resources.
BioDWH: A Data Warehouse Kit for Life Science Data Integration
Authors: Töpel Thoralf , Kormeier Benjamin , Klassen Andreas , Hofestädt Ralf 
Published: December 2017

SummaryThis paper presents a novel bioinformatics data warehouse software kit that integrates biological information from multiple public life science data sources into a local database management system. It stands out from other approaches by providing up-to-date integrated knowledge, platform and database independence as well as high usability and customization. This open source software can be used as a general infrastructure for integrative bioinformatics research and development. The advantages of the approach are realized by using a Java-based system architecture and object-relational mapping (ORM) technology. Finally, a practical application of the system is presented within the emerging area of medical bioinformatics to show the usefulness of the approach.The BioDWH data warehouse software is available for the scientific community at http://sourceforge.net/projects/biodwh/.
Global sequence properties for superfamily prediction: a machine learning approach
Authors: Dobson Richard Jb. , Munroe Patricia B , Caulfield Mark J , Saqi Mansoor 
Published: December 2017

SummaryFunctional annotation of a protein sequence in the absence of experimental data or clear similarity to a sequence of known function is difficult. In this study, a simple set of sequence attributes based on physicochemical and predicted structural characteristics were used as input to machine learning methods. In order to improve performance through increasing the data available for training, a technique of sequence enrichment was explored. These methods were used to predict membership to 24 and 49 large and diverse protein superfamiles from the SCOP database.We found the best performance was obtained using an enriched training dataset. Accuracies of 66.3% and 55.6% were achieved on datasets comprising 24 and 49 superfamilies with LibSVM and AdaBoostM1 respectively.The methods used here confirm that domains within superfamilies share global sequence properties. We show machine learning models used to predict categories within the SCOP database can be significantly improved via a simple sequence enrichment step. These approaches can be used to complement profile methods for detecting distant relationships where function is difficult to infer.
Using surveys of Affymetrix GeneChips to study antisense expression
Authors: Sanchez-Graillet Olivia , Stalteri Maria A. , Rowsell Joanna , Upton Graham J.G. , Harrison Andrew P. 
Published: December 2017

SummaryWe have used large surveys of Affymetrix GeneChip HG-U133_Plus_2 data in the public domain to conduct a study of antisense expression across diverse conditions. We derive correlations between groups of probes which map uniquely to the same exon in the antisense direction. When there are no probes assigned to an exon in the sense direction we find that many of the antisense groups fail to detect a coherent block of transcription. We find that only a minority of these groups contain coherent blocks of antisense expression suggesting transcription.We also derive correlations between groups of probes which map uniquely to the same exon in both sense and antisense direction. In some of these cases the locations of sense probes overlap with the antisense probes, and the sense and antisense probe intensities are correlated with each other. This configuration suggests the existence of a Natural Antisense Transcript (NAT) pair. We find the majority of such NAT pairs detected by GeneChips are formed by a transcript of an established gene and either an EST or an mRNA.In order to determine the exact antisense regulatory mechanism indicated by the correlation of sense probes with antisense probes, a further investigation is necessary for every particular case of interest. However, the analysis of microarray data has proved to be a good method to reconfirm known NATs, discover new ones, as well as to notice possible problems in the annotation of antisense transcripts.
An integrated dataset for in silico drug discovery
Authors: Cockell Simon J , Weile Jochen , Lord Phillip , Wipat Claire , Andriychenko Dmytro , Pocock Matthew , Wilkinson Darren , Young Malcolm , Wipat Anil 
Published: December 2017

SummaryDrug development is expensive and prone to failure. It is potentially much less risky and expensive to reuse a drug developed for one condition for treating a second disease, than it is to develop an entirely new compound. Systematic approaches to drug repositioning are needed to increase throughput and find candidates more reliably. Here we address this need with an integrated systems biology dataset, developed using the Ondex data integration platform, for the in silico discovery of new drug repositioning candidates. We demonstrate that the information in this dataset allows known repositioning examples to be discovered. We also propose a means of automating the search for new treatment indications of existing compounds.
Data integration using scanners with SQL output - The Bioscanners project at sourceforge
Authors: Groth Detlef , Hartmann Stefanie , Friemel Martin , Hill Natascha , Müller Stefan , Poustka Albert J. , Panopoulou Georgia 
Published: December 2017

SummaryThere is currently no standardized approach for parsing output that the numerous bioinformatical tools generate. Because the framework approach of the Bio-toolkits has some shortcomings, we searched for alternative approaches. To this end, we evaluated scanner generators for various programming languages with respect to their potential of creating standalone, small, and fast applications that can easily be delivered on any modern and many ancient operating systems. We developed sample applications that generate standard SQL database code and thereby greatly simplify the parsing work of data integration and data analysis. At the sourceforge project page the source code and some binaries for a selection of our applications are freely available at http://bioscanners.sourceforge.net.
An Ontology for Description of Drug Discovery Investigations
Authors: Qi Da , King Ross D. , Hopkins Andrew L. , Bickerton G. Richard J. , Soldatova Larisa N. 
Published: December 2017

SummaryThe paper presents an ontology for the description of Drug Discovery Investigation (DDI). This has been developed through the use of a Robot Scientist “Eve”, and in consultation with industry. DDI aims to define the principle entities and the relations in the research and development phase of the drug discovery pipeline. DDI is highly transferable and extendable due to its adherence to accepted standards, and compliance with existing ontology resources. This enables DDI to be integrated with such related ontologies as the Vaccine Ontology, the Advancing Clinico-Genomic Trials on Cancer Master Ontology, etc. DDI is available at http://purl.org/ddi/wikipedia or http://purl.org/ddi/home
Comparison and Integration of Target Prediction Algorithms for microRNA Studies
Authors: Zhang Yanju , Verbeek Fons J. 
Published: December 2017

SummarymicroRNAs are short RNA fragments that have the capacity of regulating hundreds of target gene expression. Currently, due to lack of high-throughput experimental methods for miRNA target identification, a collection of computational target prediction approaches have been developed. However, these approaches deal with different features or factors are weighted differently resulting in diverse range of predictions. The prediction accuracy remains uncertain. In this paper, three commonly used target prediction algorithms are evaluated and further integrated using algorithm combination, ranking aggregation and Bayesian Network classification. Our results revealed that each individual prediction algorithm displays its advantages as was shown on different test data sets. Among different integration strategies, the application of Bayesian Network classifier on the features calculated from multiple prediction methods significantly improved target prediction accuracy.
Uncovering the expression patterns of chimeric transcripts using surveys of Affymetrix GeneChips
Authors: Rowsell Joanna , Camargo Renata da Silva , Langdon William B. , Stalteri Maria A. , Harrison Andrew P. 
Published: December 2017

Summary Background: A chimeric transcript is a single RNA sequence which results from the transcription of two adjacent genes. Recent studies estimate that at least 4% of tandem human gene pairs may form chimeric transcripts. Affymetrix GeneChip data are used to study the expression patterns of tens of thousands of genes and the probe sequences used in these microarrays can potentially map to exotic RNA sequences such as chimeras.Results: We have studied human chimeras and investigated their expression patterns using large surveys of Affymetrix microarray data obtained from the Gene Expression Omnibus. We show that for six probe sets, a unique probe mapping to a transcript produced by one of the adjacent genes can be used to identify the expression patterns of readthrough transcripts. Furthermore, unique probes mapping to an intergenic exon present only in the MASK-BP3 chimera can be used directly to study the expression levels of this transcript.Conclusions: We have attempted to implement a new method for identifying tandem chimerism. In this analysis unambiguous probes are needed to measure run-off transcription and probes that map to intergenic exons are particularly valuable for identifying the expression of chimeras.
CardioVINEdb: a data warehouse approach for integration of life science data in cardiovascular diseases
Authors: Kormeier Benjamin , Hippe Klaus , Töpel Thoralf , Hofestädt Ralf 
Published: December 2017

SummaryOne of the major challenges in bioinfomatics is to integrate and manage data from different sources as well as experimental microarray data and present them in a user-friendly format. Therefore, we present CardioVINEdb, a data warehouse approach developed to interact with and explore life science data. The data warehouse architecture provides a platform independent web interface that can be used with any common web browser. A monitor component controls and updates the data from the different sources to guarantee up-todateness. In addition, the system provides a “static” and “dynamic” visualization component for interactive graphical exploration of the data.
miReg: a resource for microRNA regulation
Authors: Barh Debmalya , Bhat Dattatraya , Viero Cedric 
Published: December 2017

SummaryMicroRNAs (miRNAs/miRs) are important cellular components that regulate gene expression at posttranscriptional level. Various upstream components regulate miR expression and any deregulation causes disease conditions. Therefore, understanding of miR regulatory network both at upstream and downstream level is crucial and a resource on this aspect will be helpful. Currently available miR databases are mostly related to downstream targets, sequences, or diseases. But as of now, no database is available that provides a complete picture of miR regulation in a specific condition.Our miR regulation web resource (miReg) is a manually curated one that represents validated upstream regulators (transcription factor, drug, physical, and chemical) along with downstream targets, associated biological process, experimental condition or disease state, up or down regulation of the miR in that condition, and corresponding PubMed references in a graphical and user friendly manner, browseable through 5 browsing options. We have presented exact facts that have been described in the corresponding literature in relation to a given miR, whether it’s a feed-back/feed-forward loop or inhibition/activation. Moreover we have given various links to integrate data and to get a complete picture on any miR listed. Current version (Version 1.0) of miReg contains 47 important human miRs with 295 relations using 190 absolute references. We have also provided an example on usefulness of miReg to establish signalling pathways involved in cardiomyopathy. We believe that miReg will be an essential miRNA knowledge base to research community, with its continuous upgrade and data enrichment.This HTML based miReg can be accessed from: www.iioab-mireg.webs.com or www.iioab.webs.com/mireg.htm.
From Binary to Multivalued to Continuous Models: The lac Operon as a Case Study
Authors: Franke Raimo , Theis Fabian J. , Klamt Steffen 
Published: December 2017

SummaryUsing the lac operon as a paradigmatic example for a gene regulatory system in prokaryotes, we demonstrate how qualitative knowledge can be initially captured using simple discrete (Boolean) models and then stepwise refined to multivalued logical models and finally to continuous (ODE) models. At all stages, signal transduction and transcriptional regulation is integrated in the model description. We first show the potential benefit of a discrete binary approach and discuss then problems and limitations due to indeterminacy arising in cyclic networks. These limitations can be partially circumvented by using multilevel logic as generalization of the Boolean framework enabling one to formulate a more realistic model of the lac operon. Ultimately a dynamic description is needed to fully appreciate the potential dynamic behavior that can be induced by regulatory feedback loops. As a very promising method we show how the use of multivariate polynomial interpolation allows transformation of the logical network into a system of ordinary differential equations (ODEs), which then enables the analysis of key features of the dynamic behavior.
An Advanced Environment for Hybrid Modeling of Biological Systems Based on Modelica
Authors: Proß Sabrina , Bachmann Bernhard 
Published: December 2017

Summary Biological systems are often very complex so that an appropriate formalism is needed for modeling their behavior. Hybrid Petri Nets, consisting of time-discrete Petri Net elements as well as continuous ones, have proven to be ideal for this task. Therefore, a new Petri Net library was implemented based on the object-oriented modeling language Modelica which allows the modeling of discrete, stochastic and continuous Petri Net elements by differential, algebraic and discrete equations. An appropriate Modelica-tool performs the hybrid simulation with discrete events and the solution of continuous differential equations. A special sub-library contains so-called wrappers for specific reactions to simplify the modeling process.The Modelica-models can be connected to Simulink-models for parameter optimization, sensitivity analysis and stochastic simulation in Matlab.The present paper illustrates the implementation of the Petri Net component models, their usage within the modeling process and the coupling between the Modelica-tool Dymola and Matlab/Simulink. The application is demonstrated by modeling the metabolism of Chinese Hamster Ovary Cells.
Rapid development of proteomic applications with the AIBench framework
Authors: López-Fernández Hugo , Reboiro-Jato Miguel , Glez-Peña Daniel , Reboredo José R. Méndez , Santos Hugo M. , Carreira Ricardo J. , Capelo-Martínez José L. , Fdez-Riverola Florentino 
Published: December 2017

Summary In this paper we present two case studies of Proteomics applications development using the AIBench framework, a Java desktop application framework mainly focused in scientific software development. The applications presented in this work are Decision Peptide- Driven, for rapid and accurate protein quantification, and Bacterial Identification, for Tuberculosis biomarker search and diagnosis. Both tools work with mass spectrometry data, specifically with MALDI-TOF spectra, minimizing the time required to process and analyze the experimental data.
Model Reference Adaptive Scheme for Multi-drug Infusion for Blood Pressure Control
Authors: Enbiya S. , Mahieddine F. , Hossain A. 
Published: December 2017

Summary Using multiple interacting drugs to control both the mean arterial pressure (MAP) and cardiac output (CO) of patients with different sensitivity to drugs is a challenging task which this paper attempts to address. A multivariable model reference adaptive control (MRAC) algorithm is developed using a two-input, two-output patient model. The control objective is to maintain the homodynamic variables MAP and CO at the normal values by simultaneously administering two drugs; sodium nitroprusside (SNP) and dopamine (DPM). Computer simulations were carried out to investigate the performance of this controller. The results show that the proposed adaptive scheme is robust with respect to disturbances and variations in model parameters.
Parallel Niche Pareto AlineaGA – an Evolutionary Multiobjective approach on Multiple Sequence Alignment
Authors: Silva Fernando José Mateus da , Pérez Juan Manuel Sánchez , Pulido Juan Antonio Gómez , Rodríguez Miguel A. Vega 
Published: December 2017

Summary Multiple sequence alignment is one of the most recurrent assignments in Bioinformatics. This method allows organizing a set of molecular sequences in order to expose their similarities and their differences. Although exact methods exist for solving this problem, their use is limited by the computing demands which are necessary for exploring such a large and complex search space. Genetic Algorithms are adaptive search methods which perform well in large and complex spaces. Parallel Genetic Algorithms, not only increase the speed up of the search, but also improve its efficiency, presenting results that are better than those provided by the sum of several sequential Genetic Algorithms. Although these methods are often used to optimize a single objective, they can also be used in multidimensional domains, finding all possible tradeoffs among multiple conflicting objectives. Parallel AlineaGA is an Evolutionary Algorithm which uses a Parallel Genetic Algorithm for performing multiple sequence alignment. We now present the Parallel Niche Pareto AlineaGA, a multiobjective version of Parallel AlineaGA.We compare the performance of both versions using eight BAliBASE datasets. We also measure up the quality of the obtained solutions with the ones achieved by T-Coffee and ClustalW2, allowing us to observe that our algorithm reaches for better solutions in the majority of the datasets.
Evaluating the effect of unbalanced data in biomedical document classification
Authors: Laza Rosalía , Pavón Reyes , Reboiro-Jato Miguel , Fdez-Riverola Florentino 
Published: December 2017

Summary Nowadays, document classification has become an interesting research field. Partly, this is due to the increasing availability of biomedical information in digital form which is necessary to catalogue and organize. In this context, machine learning techniques are usually applied to text classification by using a general inductive process that automatically builds a text classifier from a set of pre-classified documents. Related with this domain, imbalanced data is a well-known problem in many practical applications of knowledge discovery and its effects on the performance of standard classifiers are remarkable. In this paper, we investigate the application of a Bayesian Network (BN) model for the triage of documents, which are represented by the association of different MeSH terms. Our results show that BNs are adequate for describing conditional independencies between MeSH terms and that MeSH ontology is a valuable resource for representing Medline documents at different abstraction levels. Moreover, we perform an extensive experimental evaluation to investigate if the classification of Medline documents using a BN classifier poses additional challenges when dealing with class-imbalanced prediction. The evaluation involves two methods, under-sampling and cost-sensitive learning. We conclude that BN classifier is sensitive to both balancing strategies and existing techniques can improve its overall performance.
Predicting protein distance maps according to physicochemical properties
Authors: Cortés Gualberto Asencio , Aguilar-Ruiz Jesús A. 
Published: December 2017

SummaryThe prediction of protein structures is a current issue of great significance in structural bioinformatics. More specifically, the prediction of the tertiary structure of a protein con- sists in determining its three-dimensional conformation based solely on its amino acid sequence. This study proposes a method in which protein fragments are assembled according to their physicochemical similarities, using information extracted from known protein structures. Many approaches cited in the literature use the physicochemical properties of amino acids, generally hydrophobicity, polarity and charge, to predict structure. In our method, implemented with parallel multithreading, we used a set of 30 physicochemical amino acid properties selected from the AAindex database. Several protein tertiary structure prediction methods produce a contact map. Our proposed method produces a distance map, which provides more information about the structure of a protein than a contact map. We performed several preliminary analysis of the protein physicochemical data distributions using 3D surfaces. Three main pattern types were found in 3D surfaces, thus it is possible to extract rules in order to predict distances between amino acids according to their physicochemical properties. We performed an experimental validation of our method using five non-homologous protein sets and we showed the generality of this method and its prediction quality using the amino acid properties considered. Finally, we included a study of the algorithm efficiency according to the number of most similar fragments considered and we notably improved the precision with the studied proteins sets.