bluuebunny/crossref_metadata_2025_split huggingface dataset:
A Hierarchical Approach to Protein Fold Prediction
Authors: Mohammad Tabrez Anwar Shamim , Nagarajaram Hampapathalu Adimurthy 
Published: December 2017

Summary Fold recognition, assigning novel proteins to known structures, forms an important component of the overall protein structure discovery process. The available methods for protein fold recognition are limited by the low fold-coverage and/or low prediction accuracies. We describe here a new Support Vector Machine (SVM)-based method for protein fold prediction with high prediction accuracy and high fold-coverage. The new method of fold prediction with high fold-coverage was developed by training and testing on a large number of folds in order to make the method suitable for large scale fold predictions. However, presence of large number of folds in the training set made the classification task difficult as a consequence of increased complexity involved in binary classifications of SVMs. In order to overcome this complexity we adopted a hierarchical approach where fold-prediction is made in two steps. At the first step structural class of the query is predicted and at the second step fold is predicted within the predicted structural class. This decreased the complexity of the classification problem and also improved the overall fold prediction accuracy. To the best of our knowledge this is the first taxonomic fold recognition method to cover over 700 protein-folds and gives prediction accuracy of around 70% on a benchmark dataset. Since the new method gives rise to state of the art prediction performance and hence can be very useful for structural characterization of proteins discovered in various genomes.
UBioLab: a web-LABoratory for Ubiquitous in-silico experiments
Authors: Bartocci E. , Di Berardini M. R. , Merelli E. , Vito L. 
Published: December 2017

Summary The huge and dynamic amount of bioinformatic resources (e.g., data and tools) available nowadays in Internet represents a big challenge for biologists -for what concerns their management and visualization- and for bioinformaticians -for what concerns the possibility of rapidly creating and executing in-silico experiments involving resources and activities spread over the WWW hyperspace. Any framework aiming at integrating such resources as in a physical laboratory has imperatively to tackle -and possibly to handle in a transparent and uniform way- aspects concerning physical distribution, semantic heterogeneity, co-existence of different computational paradigms and, as a consequence, of different invocation interfaces (i.e., OGSA for Grid nodes, SOAP for Web Services, Java RMI for Java objects, etc.). The framework UBioLab has been just designed and developed as a prototype following the above objective. Several architectural features -as those ones of being fully Web-based and of combining domain ontologies, Semantic Web and workflow techniques- give evidence of an effort in such a direction.The integration of a semantic knowledge management system for distributed (bioinformatic) resources, a semantic-driven graphic environment for defining and monitoring ubiquitous workflows and an intelligent agent-based technology for their distributed execution allows UBioLab to be a semantic guide for bioinformaticians and biologists providing (i) a flexible environment for visualizing, organizing and inferring any (semantics and computational) “type” of domain knowledge (e.g., resources and activities, expressed in a declarative form), (ii) a powerful engine for defining and storing semantic-driven ubiquitous in-silico experiments on the domain hyperspace, as well as (iii) a transparent, automatic and distributed environment for correct experiment executions.
Transcription Network Analysis by A Sparse Binary Factor Analysis Algorithm
Authors: Tu Shikui , Chen Runsheng , Xu Lei 
Published: December 2017

Summary Transcription factor activities (TFAs), rather than expression levels, control gene expres- sion and provide valuable information for investigating TF-gene regulations. The underly- ing bimodal or switch-like patterns of TFAs may play important roles in gene regulation. Network Component Analysis (NCA) is a popular method to deduce TFAs and TF-gene control strengths from microarray data. However, it does not directly examine the bimodal- ity of TFAs and it needs the TF-gene connection topology to be a priori known. In this paper, we modify NCA to model gene expression regulation by Binary Factor Analysis (BFA), which directly captures switch-like patterns of TFAs. Moreover, sparse technique is employed on the mixing matrix of BFA, and thus the proposed sparse BYY-BFA al- gorithm, developed under Bayesian Ying-Yang (BYY) learning framework, can not only uncover the latent TFA profile’s switch-like patterns, but also be capable of automatically shutting off the unnecessary connections. Simulation study demonstrates the effectiveness of BYY-BFA, and a preliminary application to Saccharomyces cerevisiae cell cycle data and Escherichia coli carbon source transition data shows that the reconstructed binary pat- terns of TFAs by BYY-BFA are consistent with the ups and downs of TFAs by NCA, and that BYY-BFA also works well when the network topology is unknown.
A Rational Framework for Production Decision Making in Blood Establishments
Authors: Ramoa Augusto , Maia Salomé , Lourenço Anália 
Published: December 2017

Summary SAD_BaSe is a blood bank data analysis software, created to assist in the management of blood donations and the blood production chain in blood establishments. In particular, the system keeps track of several collection and production indicators, enables the definition of collection and production strategies, and the measurement of quality indicators required by the Quality Management System regulating the general operation of blood establishments.This paper describes the general scenario of blood establishments and its main requirements in terms of data management and analysis. It presents the architecture of SAD_BaSe and identifies its main contributions. Specifically, it brings forward the generation of customized reports driven by decision making needs and the use of data mining techniques in the analysis of donor suspensions and donation discards.
Network Expansion and Pathway Enrichment Analysis towards Biologically Significant Findings from Microarrays
Authors: Wu Xiaogang , Reinhard Christoph , Li Shuyu D. , Huang Hui , Wei Tao , Pandey Ragini , Chen Jake Y. 
Published: December 2017

Summary In many cases, crucial genes show relatively slight changes between groups of samples (e.g. normal vs. disease), and many genes selected from microarray differential analysis by measuring the expression level statistically are also poorly annotated and lack of biological significance. In this paper, we present an innovative approach - network expansion and pathway enrichment analysis (NEPEA) for integrative microarray analysis. We assume that organized knowledge will help microarray data analysis in significant ways, and the organized knowledge could be represented as molecular interaction networks or biological pathways. Based on this hypothesis, we develop the NEPEA framework based on network expansion from the human annotated and predicted protein interaction (HAPPI) database, and pathway enrichment from the human pathway database (HPD). We use a recently-published microarray dataset (GSE24215) related to insulin resistance and type 2 diabetes (T2D) as case study, since this study provided a thorough experimental validation for both genes and pathways identified computationally from classical microarray analysis and pathway analysis. We perform our NEPEA analysis for this dataset based on the results from the classical microarray analysis to identify biologically significant genes and pathways. Our findings are not only consistent with the original findings mostly, but also obtained more supports from other literatures.
An Integrative Clinical Database and Diagnostics Platform for Biomarker Identification and Analysis in Ion Mobility Spectra of Human Exhaled Air
Authors: Schneider Till , Hauschild Anne-Christin , Baumbach Jörg Ingo , Baumbach Jan 
Published: December 2017

Summary Over the last decade the evaluation of odors and vapors in human breath has gained more and more attention, particularly in the diagnostics of pulmonary diseases. Ion mobility spectrometry coupled with multi-capillary columns (MCC/IMS), is a well known technology for detecting volatile organic compounds (VOCs) in air. It is a comparatively inexpensive, non-invasive, high-throughput method, which is able to handle the moisture that comes with human exhaled air, and allows for characterizing of VOCs in very low concentrations. To identify discriminating compounds as biomarkers, it is necessary to have a clear understanding of the detailed composition of human breath. Therefore, in addition to the clinical studies, there is a need for a flexible and comprehensive centralized data repository, which is capable of gathering all kinds of related information. Moreover, there is a demand for automated data integration and semi-automated data analysis, in particular with regard to the rapid data accumulation, emerging from the high-throughput nature of the MCC/IMS technology. Here, we present a comprehensive database application and analysis platform, which combines metabolic maps with heterogeneous biomedical data in a well-structured manner. The design of the database is based on a hybrid of the entity-attribute- value (EAV) model and the EAV-CR, which incorporates the concepts of classes and relationships. Additionally it offers an intuitive user interface that provides easy and quick access to the platform’s functionality: automated data integration and integrity validation, versioning and roll-back strategy, data retrieval as well as semi-automatic data mining and machine learning capabilities. The platform will support MCC/IMS-based biomarker identification and validation. The software, schemata, data sets and further information is publicly available at http://imsdb.mpi-inf.mpg.de.
A System for Extracting Study Design Parameters from Nutritional Genomics Abstracts
Authors: Kelly Cassidy , Yang Hui 
Published: December 2017

Summary The extraction of study design parameters from biomedical journal articles is an important problem in natural language processing (NLP). Such parameters define the characteristics of a study, such as the duration, the number of subjects, and their profile. Here we present a system for extracting study design parameters from sentences in article abstracts. This system will be used as a component of a larger system for creating nutrigenomics networks from articles in the nutritional genomics domain. The algorithms presented consist of manually designed rules expressed either as regular expressions or in terms of sentence parse structure. A number of filters and NLP tools are also utilized within a pipelined algorithmic framework. Using this novel approach, our system performs extraction at a finer level of granularity than comparable systems, while generating results that surpass the current state of the art.
An Integrative Bioinformatics Framework for Genome-scale Multiple Level Network Reconstruction of Rice
Authors: Liu Lili , Mei Qian , Yu Zhenning , Sun Tianhao , Zhang Zijun , Chen Ming 
Published: December 2017

Summary Understanding how metabolic reactions translate the genome of an organism into its phenotype is a grand challenge in biology. Genome-wide association studies (GWAS) statistically connect genotypes to phenotypes, without any recourse to known molecular interactions, whereas a molecular mechanistic description ties gene function to phenotype through gene regulatory networks (GRNs), protein-protein interactions (PPIs) and molecular pathways. Integration of different regulatory information levels of an organism is expected to provide a good way for mapping genotypes to phenotypes. However, the lack of curated metabolic model of rice is blocking the exploration of genome-scale multi-level network reconstruction. Here, we have merged GRNs, PPIs and genome-scale metabolic networks (GSMNs) approaches into a single framework for rice via omics’ regulatory information reconstruction and integration. Firstly, we reconstructed a genome-scale metabolic model, containing 4,462 function genes, 2,986 metabolites involved in 3,316 reactions, and compartmentalized into ten subcellular locations. Furthermore, 90,358 pairs of protein-protein interactions, 662,936 pairs of gene regulations and 1,763 microRNA-target interactions were integrated into the metabolic model. Eventually, a database was developped for systematically storing and retrieving the genome-scale multi-level network of rice. This provides a reference for understanding genotype-phenotype relationship of rice, and for analysis of its molecular regulatory network.
Assembling cell context-specific gene sets: a case in cardiomyopathy
Authors: Liu Mingming , King Vanessa , Lim Wei Keat 
Published: December 2017

Summary An increasing amount of evidence suggests that canonical pathways and standard molecular signature databases are incomplete and inadequate to model the complex behavior of cell physiology and pathology. Yet, many Gene Set Analysis (GSA) studies still rely on these databases to identify disease biomarkers and molecular mechanisms within a specific cell context. While tremendous effort has been invested in developing GSA tools, there is limited number of studies focusing on de novo assembly of context-specific gene sets as opposed to simply applying GSA using the standard gene set database.In this paper, we propose a pipeline to derive the entire collection of Cell context-Specific Gene Sets (CSGS) from a molecular interaction network, based on the hypothesis that molecular events linked to a specific phenotypic response should cluster within a subnet of interacting genes. Gene sets are assigned using both physical properties of the network and functional annotations of the neighboring nodes. The identified gene sets could provide a precise starting point such that the downstream GSA will cover all functional pathways in this particular cell context and, at the same time, avoid the noise and excessive multiple-hypothesis testing due to inclusion of irrelevant gene sets from the standard database. We applied the pipeline in the context of cardiomyopathy and demonstrated its superiority over MSigDB gene set collection in terms of: (i) reproducibility and robustness in GSA, (ii) effectiveness in uncovering molecular mechanisms associated with cardiomyopathy, and (iii) the performance in distinguishing diseased vs. normal states.
Efficient mapping of genomic sequences to optimize multiple pairwise alignment in hybrid cluster platforms
Authors: Montañola Alberto , Roig Concepció , Hernández Porfidio 
Published: December 2017

Summary Multiple sequence alignment (MSA), used in biocomputing to study similarities between different genomic sequences, is known to require important memory and computation resources. Nowadays, researchers are aligning thousands of these sequences, creating new challenges in order to solve the problem using the available resources efficiently. Determining the efficient amount of resources to allocate is important to avoid waste of them, thus reducing the economical costs required in running for example a specific cloud instance. The pairwise alignment is the initial key step of the MSA problem, which will compute all pair alignments needed. We present a method to determine the optimal amount of memory and computation resources to allocate by the pairwise alignment, and we will validate it through a set of experimental results for different possible inputs. These allow us to determine the best parameters to configure the applications in order to use effectively the available resources of a given system.
The CellML 1.1 Specification
Authors: Cuellar Autumn , Hedley Warren , Nelson Melanie , Lloyd Catherine , Halstead Matt , Bullivant David , Nickerson David , Hunter Peter , Nielsen Poul 
Published: December 2017

Summary This document specifies CellML 1.1, an XML-based language for describing and exchanging models of cellular and subcellular processes. MathML embedded in CellML documents is used to define the underlying mathematics of models. Models consist of a network of re- usable components, each with variables and equations manipulating those variables. Models may import other models to create systems of increasing complexity. Metadata may be embedded in CellML documents using RDF.
The CellML Metadata Framework 2.0 Specification
Authors: Cooling Michael T. , Hunter Peter 
Published: December 2017

Summary The CellML Metadata Framework 2.0 is a modular framework that describes how semantic annotations should be made about mathematical models encoded in the CellML (www.cellml.org) format, and their elements. In addition to the Core specification, there are several satellite specifications, each designed to cater for model annotation in a different context. Basic Model Information, Citation, License and Biological Annotation specifications are presented.
COMBINE Archive Specification Version 1
Authors: Bergmann Frank T. , Rodriguez Nicolas , Le Novère Nicolas 
Published: December 2017

Summary Several standard formats have been proposed that can be used to describe models, simulations, data or other essential information in a consistent fashion. These constitute various separate components required to reproduce a given published scientific result.The Open Modeling EXchange format (OMEX) supports the exchange of all the information necessary for a modeling and simulation experiment in biology. An OMEX file is a ZIP container that includes a manifest file, an optional metadata file, and the files describing the model. The manifest is an XML file listing all files included in the archive and their type. The metadata file provides additional information about the archive and its content. Although any format can be used, we recommend an XML serialization of the Resource Description Framework.Together with the other standard formats from the Computational Modeling in Biology Network (COMBINE), OMEX is the basis of the COMBINE Archive. The content of a COMBINE Archive consists of files encoded in COMBINE standards whenever possible, but may include additional files defined by an Internet Media Type. The COMBINE Archive facilitates the reproduction of modeling and simulation experiments in biology by embedding all the relevant information in one file. Having all the information stored and exchanged at once also helps in building activity logs and audit trails.
Extracting kinetic information from literature with KineticRE
Authors: Alão Freitas Ana , Costa Hugo , Rocha Isabel 
Published: December 2017

Summary To better understand the dynamic behavior of metabolic networks in a wide variety of conditions, the field of Systems Biology has increased its interest in the use of kinetic models. The different databases, available these days, do not contain enough data regarding this topic. Given that a significant part of the relevant information for the development of such models is still wide spread in the literature, it becomes essential to develop specific and powerful text mining tools to collect these data. In this context, this work has as main objective the development of a text mining tool to extract, from scientific literature, kinetic parameters, their respective values and their relations with enzymes and metabolites. The approach proposed integrates the development of a novel plug-in over the text mining framework @Note2. In the end, the pipeline developed was validated with a case study on Kluyveromyces lactis, spanning the analysis and results of 20 full text documents.
SBML Level 3 package: Groups, Version 1 Release 1
Authors: Hucka Michael , Smith Lucian P. 
Published: December 2017

SummaryBiological models often contain components that have relationships with each other, or that modelers want to treat as belonging to groups with common characteristics or shared metadata. The SBML Level 3 Version 1 Core specification does not provide a n explicit mechanism for expressing such relationships, but it does provide a mechanism for SBML packages to extend the Core specification and add additional syntactical constructs. The SBML Groups package for SBML Level 3 adds the necessary features to SBML to allow grouping of model components to be expressed. Such groups do not affect the mathematical interpretation of a model, but they do provide a way to add information that can be useful for modelers and software tools. The SBML Groups package enables a modeler to include definitions of groups and nested groups, each of which may be annotated to convey why that group was created, and what it represents.
Jllumina - A comprehensive Java-based API for statistical Illumina Infinium HumanMethylation450 and Infinium MethylationEPIC BeadChip data processing
Authors: Almeida Diogo , Skov Ida , Lund Jesper , Mohammadnejad Afsaneh , Silva Artur , Vandin Fabio , Tan Qihua , Baumbach Jan , Röttger Richard 
Published: December 2017

Summary
Measuring differential methylation of the DNA is the nowadays most common approach to linking epigenetic modifications to diseases (called epigenome-wide association studies, EWAS). For its low cost, its efficiency and easy handling, the Illumina HumanMethylation450 BeadChip and its successor, the Infinium MethylationEPIC BeadChip, is the by far most popular techniques for conduction EWAS in large patient cohorts. Despite the popularity of this chip technology, raw data processing and statistical analysis of the array data remains far from trivial and still lacks dedicated software libraries enabling high quality and statistically sound downstream analyses. As of yet, only R-based solutions are freely available for low-level processing of the Illumina chip data. However, the lack of alternative libraries poses a hurdle for the development of new bioinformatic tools, in particular when it comes to web services or applications where run time and memory consumption matter, or EWAS data analysis is an integrative part of a bigger framework or data analysis pipeline. We have therefore developed and implemented Jllumina, an open-source Java library for raw data manipulation of Illumina Infinium HumanMethylation450 and Infinium MethylationEPIC BeadChip data, supporting the developer with Java functions covering reading and preprocessing the raw data, down to statistical assessment, permutation tests, and identification of differentially methylated loci. Jllumina is fully parallelizable and publicly available at http://dimmer.compbio.sdu.dk/download.html
Omics Fusion – A Platform for Integrative Analysis of Omics Data
Authors: Brink Benedikt G. , Seidel Annica , Kleinbölting Nils , Nattkemper Tim W. , Albaum Stefan P. 
Published: December 2017

Summary
We present Omics Fusion, a new web-based platform for integrative analysis of omics data. Omics Fusion provides a collection of new and established tools and visualization methods to support researchers in exploring omics data, validating results or understanding how to adjust experiments in order to make new discoveries. It is easily extendible and new visualization methods are added continuously. It is available for free under: https://fusion.cebitec.uni-bielefeld.de/