bluuebunny/crossref_metadata_2025_split huggingface dataset:
The Topology of the Growing Human Interactome Data
Authors: Janjić Vuk , Pržulj Nataša 
Published: December 2017

Summary We have long moved past the one-gene-one-function concept originally proposed by Beadle and Tatum back in 1941; but the full understanding of genotype-phenotype relations still largely relies on the analysis of static, snapshot-like, interaction data sets. Here, we look at what global patterns can be uncovered if we simply trace back the human interactome network over the last decade of protein-protein interaction (PPI) screening. We take a purely topological approach and find that as the human interactome is getting denser, it is not only gaining in structure (in terms of now being better fit by structured network models than before), but also there are patterns in the way in which it is growing: (a) newly added proteins tend to get linked to existing proteins in the interactome that are not know to interact; and (b) new proteins tend to link to already well connected proteins. Moreover, the alignment between human and yeast interactomes spanning over 40% of yeast’s proteins - that are involved in regulation of transcription, RNA splicing and other cellcycle- related processes-suggests the existence of a part of the interactome which remains topologically and functionally unaffected through evolution. Furthermore, we find a small sub-network, specific to the “core” of the human interactome and involved in regulation of transcription and cancer development, whose wiring has not changed within the human interactome over the last 10 years of interacome data acquisition. Finally, we introduce a generalisation of the clustering coefficient of a network as a new measure called the cycle coefficient, and use it to show that PPI networks of human and model organisms are wired in a tight way which forbids the occurrence large cycles.
Profiling of Genetic Switches using Boolean Implications in Expression Data
Authors: Çakır M. Volkan , Binder Hans , Wirth Henry 
Published: December 2017

Summary Correlation analysis assuming coexpression of the genes is a widely used method for gene expression analysis in molecular biology. Yet growing extent, quality and dimensionality of the molecular biological data permits emerging, more sophisticated approaches like Boolean implications. We present an approach which is a combination of the SOM (self organizing maps) machine learning method and Boolean implication analysis to identify relations between genes, metagenes and similarly behaving metagene groups (spots). Our method provides a way to assign Boolean states to genes/metagenes/spots and offers a functional view over significantly variant elements of gene expression data on these three different levels. While being able to cover relations between weakly correlated entities Boolean implication method also decomposes these relations into six implication classes. Our method allows one to validate or identify potential relationships between genes and functional modules of interest and to assess their switching behaviour. Furthermore the output of the method renders it possible to construct and study the network of genes. By providing logical implications as updating rules for the network it can also serve to aid modelling approaches.
Identifying interactions between chemical entities in biomedical text
Authors: Lamurias Andre , Ferreira João D. , Couto Francisco M. 
Published: December 2017

Summary Interactions between chemical compounds described in biomedical text can be of great importance to drug discovery and design, as well as pharmacovigilance. We developed a novel system, “Identifying Interactions between Chemical Entities” (IICE), to identify chemical interactions described in text. Kernel-based Support Vector Machines first identify the interactions and then an ensemble classifier validates and classifies the type of each interaction. This relation extraction module was evaluated with the corpus released for the DDI Extraction task of SemEval 2013, obtaining results comparable to stateof- the-art methods for this type of task. We integrated this module with our chemical named entity recognition module and made the whole system available as a web tool at www.lasige.di.fc.ul.pt/webtools/iice.
Systems Biology Graphical Notation: Process Description language Level 1 Version 1.3
Authors: Moodie Stuart , Le Novère Nicolas , Demir Emek , Mi Huaiyu , Villéger Alice 
Published: December 2017

Summary The Systems Biological Graphical Notation (SBGN) is an international community effort for standardized graphical representations of biological pathways and networks. The goal of SBGN is to provide unambiguous pathway and network maps for readers with different scientific backgrounds as well as to support efficient and accurate exchange of biological knowledge between different research communities, industry, and other players in systems biology. Three SBGN languages, Process Description (PD), Entity Relationship (ER) and Activity Flow (AF), allow for the representation of different aspects of biological and biochemical systems at different levels of detail.The SBGN Process Description language represents biological entities and processes between these entities within a network. SBGN PD focuses on the mechanistic description and temporal dependencies of biological interactions and transformations. The nodes (elements) are split into entity nodes describing, e.g., metabolites, proteins, genes and complexes, and process nodes describing, e.g., reactions and associations. The edges (connections) provide descriptions of relationships (or influences) between the nodes, such as consumption, production, stimulation and inhibition. Among all three languages of SBGN, PD is the closest to metabolic and regulatory pathways in biological literature and textbooks, but its well-defined semantics offer a superior precision in expressing biological knowledge.
The Systems Biology Markup Language (SBML): Language Specification for Level 3 Version 1 Core
Authors: Hucka Michael , Bergmann Frank T. , Hoops Stefan , Keating Sarah M. , Sahle Sven , Schaff James C. , Smith Lucian P. , Wilkinson Darren J. 
Published: December 2017

Summary Computational models can help researchers to interpret data, understand biological function, and make quantitative predictions. The Systems Biology Markup Language (SBML) is a file format for representing computational models in a declarative form that can be exchanged between different software systems. SBML is oriented towards describing biological processes of the sort common in research on a number of topics, including metabolic pathways, cell signaling pathways, and many others. By supporting SBML as an input/output format, different tools can all operate on an identical representation of a model, removing opportunities for translation errors and assuring a common starting point for analyses and simulations. This document provides the specification for Version 1 of SBML Level 3 Core. The specification defines the data structures prescribed by SBML as well as their encoding in XML, the eXtensible Markup Language. This specification also defines validation rules that determine the validity of an SBML document, and provides many examples of models in SBML form. Other materials and software are available from the SBML project web site, http://sbml.org/.
The Systems Biology Markup Language (SBML) Level 3 Package: Layout, Version 1 Core
Authors: Gauges Ralph , Rost Ursula , Sahle Sven , Wengler Katja , Bergmann Frank T. 
Published: December 2017

SummaryMany software tools provide facilities for depicting reaction network diagrams in a visual form. Two aspects of such a visual diagram can be distinguished: the layout (i.e.: the positioning and connections) of the elements in the diagram, and the graphical form of the elements (for example, the glyphs used for symbols, the properties of the lines connecting them, and so on). For software tools that also read and write models in SBML (Systems Biology Markup Language) format, a common need is to store the network diagram together with the SBML representation of the model. This in turn raises the question of how to encode the layout and the rendering of these diagrams. The SBML Level 3 Version 1 Core specification does not provide a mechanism for explicitly encoding diagrams, but it does provide a mechanism for SBML packages to extend the Core specification and add additional syntactical constructs. The Layout package for SBML Level 3 adds the necessary features to SBML so that diagram layouts can be encoded in SBML files, and a companion package called SBML Rendering specifies how the graphical rendering of elements can be encoded.The SBML Layout package is based on the principle that reaction network diagrams should be described as representations of entities such as species and reactions (with direct links to the underlying SBML elements), and not as arbitrary drawings or graphs; for this reason, existing languages for the description of vector drawings (such as SVG) or general graphs (such as GraphML) cannot be used.
The Systems Biology Markup Language (SBML) Level 3 Package: Flux Balance Constraints
Authors: Olivier Brett G. , Bergmann Frank T. 
Published: December 2017

Summary Constraint-based modeling is a well established modelling methodology used to analyze and study biological networks on both a medium and genome scale. Due to their large size, genome scale models are typically analysed using constraint-based optimization techniques. One widely used method is Flux Balance Analysis (FBA) which, for example, requires a modelling description to include: the definition of a stoichiometric matrix, an objective function and bounds on the values that fluxes can obtain at steady state.The Flux Balance Constraints (FBC) Package extends SBML Level 3 and provides a standardized format for the encoding, exchange and annotation of constraint-based models. It includes support for modelling concepts such as objective functions, flux bounds and model component annotation that facilitates reaction balancing. The FBC package establishes a base level for the unambiguous exchange of genome-scale, constraint-based models, that can be built upon by the community to meet future needs (e. g. by extending it to cover dynamic FBC models).
Managing and Documenting Legacy Scientific Workflows
Authors: Acuña Ruben , Chomilier Jacques , Lacroix Zoé 
Published: December 2017

Summary Scientific legacy workflows are often developed over many years, poorly documented and implemented with scripting languages. In the context of our cross-disciplinary projects we face the problem of maintaining such scientific workflows. This paper presents the Workflow Instrumentation for Structure Extraction (WISE) method used to process several ad-hoc legacy workflows written in Python and automatically produce their workflow structural skeleton. Unlike many existing methods, WISE does not assume input workflows to be preprocessed in a known workflow formalism. It is also able to identify and analyze calls to external tools. We present the method and report its results on several scientific workflows.
Integrating data from heterogeneous DNA microarray platforms
Authors: Valente Eduardo , Rocha Miguel 
Published: December 2017

Summary DNA microarrays are one of the most used technologies for gene expression measurement. However, there are several distinct microarray platforms, from different manufacturers, each with its own measurement protocol, resulting in data that can hardly be compared or directly integrated. Data integration from multiple sources aims to improve the assertiveness of statistical tests, reducing the data dimensionality problem. The integration of heterogeneous DNA microarray platforms comprehends a set of tasks that range from the re-annotation of the features used on gene expression, to data normalization and batch effect elimination. In this work, a complete methodology for gene expression data integration and application is proposed, which comprehends a transcript-based re-annotation process and several methods for batch effect attenuation. The integrated data will be used to select the best feature set and learning algorithm for a brain tumor classification case study. The integration will consider data from heterogeneous Agilent and Affymetrix platforms, collected from public gene expression databases, such as The Cancer Genome Atlas and Gene Expression Omnibus.
Estimation of biomass composition from genomic and transcriptomic information
Authors: Santos Sophia , Rocha Isabel 
Published: December 2017

SummaryGiven the great potential impact of the growing number of complete genome-scale metabolic network reconstructions of microorganisms, bioinformatics tools are needed to simplify and accelerate the course of knowledge in this field. One essential component of a genomescale metabolic model is its biomass equation, whose maximization is one of the most common objective functions used in Flux Balance Analysis formulations. Some components of biomass, such as amino acids and nucleotides, can be estimated from genome information, providing reliable data without the need of performing lab experiments. In this work a java tool is proposed that estimates microbial biomass composition in amino acids and nucleotides, from genome and transcriptomic information, using as input files sequences in FASTA format and files with transcriptomic data in the csv format. This application allows to obtain the results rapidly and is also a user-friendly tool for users with any or little background in informatics (http://darwin.di.uminho.pt/biomass/). The results obtained using this tool are fairly close to experimental data, showing that the estimation of amino acid and nucleotide compositions from genome information and from transcriptomic data is a good alternative when no experimental data is available.
Specifications of Standards in Systems and Synthetic Biology: Status and Developments in 2016
Authors: Schreiber Falk , Bader Gary D. , Gleeson Padraig , Golebiewski Martin , Hucka Michael , Novère Nicolas Le , Myers Chris , Nickerson David , Sommer Björn , Waltemath Dagmar 
Published: December 2017

SummaryStandards are essential to the advancement of science and technology. In systems and synthetic biology, numerous standards and associated tools have been developed over the last 16 years. This special issue of the Journal of Integrative Bioinformatics aims to support the exchange, distribution and archiving of these standards, as well as to provide centralised and easily citable access to them.
New insights into genotype–phenotype correlation in individuals with different level of general non-specific reactivity of an organism
Authors: Mulik Alexander , Novochadov Valery , Bondarev Alexander , Lipnitskaya Sofya , Ulesikova Irina , Shatyr Yulia 
Published: December 2017

Summary
The objective of the study was to investigate the genetic basis of general non-specific reactivity of an organism. Systematic search in PubMedCentral, PDB, KEGG and SNP databases identified a set of genes and their polymorphisms that can determine pain sensitivity and therefore the level of general non-specific reactivity of the human organism. Six SNPs were selected for genotyping kit design; 230 healthy volunteers were enrolled in the study. It was revealed that very high pain threshold was associated with allele A in rs1851048 and allele C in rs6777055. High level of general non-specific reactivity of an organism was associated with allele G in rs2562456 (OR=1.804, CI=1.139-2.857, p=0.011) and allele C in rs6923492 (OR=1.582, CI=1.071-2.335, p=0.021). Low level of general non-specific reactivity of an organism was associated with allele T in rs6923492 (OR=0.351, CI=0.154-0.799, p=0.010). A set of genes and single-nucleotide polymorphisms associated with the pain sensitivity and indirectly with the level of general non-specific reactivity of human organism were determined. The identified correlations reveal some molecular mechanisms of general non-specific reactivity of an organism variability and can guide further research in this area.
Genomic Islands: an overview of current software tools and future improvements
Authors: Soares Siomar de Castro , Oliveira Letícia de Castro , Jaiswal Arun Kumar , Azevedo Vasco 
Published: December 2017

Summary Microbes are highly diverse and widely distributed organisms. They account for ~60% of Earth’s biomass and new predictions point for the existence of 1011 to 1012 species, which are constantly sharing genes through several different mechanisms. Genomic Islands (GI) are critical in this context, as they are large regions acquired through horizontal gene transfer. Also, they present common features like genomic signature deviation, transposase genes, flanking tRNAs and insertion sequences. GIs carry large numbers of genes related to specific lifestyle and are commonly classified in Pathogenicity, Resistance, Metabolic or Symbiotic Islands. With the advent of the next-generation sequencing technologies and the deluge of genomic data, many software tools have been developed that aim to tackle the problem of GI prediction and they are all based on the prediction of GI common features. However, there is still room for the development of new software tools that implements new approaches, such as, machine learning and pangenomics based analyses. Finally, GIs will always hold a potential application in every newly invented genomic approach as they are directly responsible for much of the genomic plasticity of bacteria.
A Machine Learning Approach for MicroRNA Precursor Prediction in Retro-transcribing Virus Genomes
Authors: Demirci Müşerref Duygu Saçar , Toprak Mustafa , Allmer Jens 
Published: December 2017

SummaryIdentification of microRNA (miRNA) precursors has seen increased efforts in recent years. The difficulty in experimental detection of pre-miRNAs increased the usage of computational approaches. Most of these approaches rely on machine learning especially classification. In order to achieve successful classification, many parameters need to be considered such as data quality, choice of classifier settings, and feature selection. For the latter one, we developed a distributed genetic algorithm on HTCondor to perform feature selection. Moreover, we employed two widely used classification algorithms libSVM and random forest with different settings to analyze the influence on the overall classification performance. In this study we analyzed 5 human retro virus genomes; Human endogenous retrovirus K113, Hepatitis B virus (strain ayw), Human T lymphotropic virus 1, Human T lymphotropic virus 2, Human immunodeficiency virus 2, and Human immunodeficiency virus 1. We then predicted pre-miRNAs by using the information from known virus and human pre-miRNAs. Our results indicate that these viruses produce novel unknown miRNA precursors which warrant further experimental validation.
miRquant 2.0: an Expanded Tool for Accurate Annotation and Quantification of MicroRNAs and their isomiRs from Small RNA-Sequencing Data
Authors: Kanke Matthew , Baran-Gale Jeanette , Villanueva Jonathan , Sethupathy Praveen 
Published: December 2017

SummarySmall non-coding RNAs, in particular microRNAs, are critical for normal physiology and are candidate biomarkers, regulators, and therapeutic targets for a wide variety of diseases. There is an ever-growing interest in the comprehensive and accurate annotation of microRNAs across diverse cell types, conditions, species, and disease states. Highthroughput sequencing technology has emerged as the method of choice for profiling microRNAs. Specialized bioinformatic strategies are required to mine as much meaningful information as possible from the sequencing data to provide a comprehensive view of the microRNA landscape. Here we present miRquant 2.0, an expanded bioinformatics tool for accurate annotation and quantification of microRNAs and their isoforms (termed isomiRs) from small RNA-sequencing data. We anticipate that miRquant 2.0 will be useful for researchers interested not only in quantifying known microRNAs but also mining the rich well of additional information embedded in small RNA-sequencing data.
Performance of GPS sidereal filters during a satellite outage
Authors: Atkins C. , Ziebart M. K. 
Published: December 2017

Abstract
Sidereal filtering is the name of a technique used to reduce the effect of multipath interference on a GPS position time series associated with a static or quasi-static antenna. This article assesses the impact of a GPS satellite outage on the performance of a sidereal filter. Two different types of sidereal filter are tested: a position-domain sidereal filter (PDSF) and an observation-domain sidereal filter (ODSF). A satellite outage is simulated at two static receivers with contrasting antenna types and multipath environments. At both stations, the ODSF is more effective than a PDSF at removing multipath error over averaging intervals under around 200 seconds in length whether there is an outage or not. However, difference in the performance of the two types of sidereal filter was much more significant at the station more prone to multipath interference. The results are particularly relevant for applications where high-rate precise point positioning (PPP) is used for monitoring: If a PDSF is applied, then errors due to highfrequency multipath interference may still alias into the resulting position time series if a satellite outage occurs and possibly increasing the false alarm rate. In contrast, an ODSF is likely to perform better in such circumstances.
The System of Means for Overcoming the Crisis – Based on an Empirical Examination at Companies in Budapest and Békés County, Hungary
Authors: Chovan Brigitta , Poór József , Juhász Tímea 
Published: December 2017

Abstract
The economic crisis started in 2008 had a significant negative impact on employment in most sectors. During the research authors sought the answer to the question which employment systems were applied for overcoming the crisis at companies in Budapest and Békés County. To examine this, the authors compiled the questionnaire in co-operation with the Hungarian Chamber of Commerce and Industry and the Management and HR Research Centre of Szent István University.
The authors’ questionnaire findings also provide a proof for the fact that by creating and implementing the suitable human resource management models and by promoting them widely, the changes due to the current economic circumstances will be manageable and the increase in the competitiveness of organizations will be ensured.
One of the ways to decrease the personnel costs is to apply atypical employment forms. This form of employment is becoming more and more popular among employers; yet, despite the achievements, those questioned still prefer traditional employment forms.
Succesfull International Negotiations
Authors: Cichosz Izabela 
Published: December 2017

Abstract
Today’s concept of economic globalization is predicated and centered around a sustainable development various economic systems which focus on their ability to achieve the best possible results today without jeopardizing the ability of national economies to meet their needs in the future. Beginning with the fact that globalization has become a pre-requisite for most companies today, regardless of its size or scope of operations, I will, in this paper talk about the theoretical issues that surround globalization of an economy, different types and stages of international transactions and the business communications that go on within these internationalization stages.