bluuebunny/crossref_metadata_2025_split huggingface dataset:
Enhancing Data Integration with Text Analysis to Find Proteins Implicated in Plant Stress Response
Authors: Hassani-Pak Keywan , Legaie Roxane , Canevet Catherine , Berg Hugo A. van den , Moore Jonathan D. , Rawlings Christopher J. 
Published: December 2017

SummaryHigh throughput genomic studies can identify large numbers of potential candidate genes, which must be interpreted and filtered by investigators to select the best ones for further analysis. Prioritization is generally based on evidence that supports the role of a gene product in the biological process being investigated. The two most important bodies of information providing such evidence are bioinformatics databases and the scientific literature. In this paper we present an extension to the Ondex data integration framework that uses text mining techniques over Medline abstracts as a method for accessing both these bodies of evidence in a consistent way. In an example use case, we apply our method to create a knowledge base of Arabidopsis proteins implicated in plant stress response and use various scoring metrics to identify key protein-stress associations. In conclusion, we show that the additional text mining features are able to highlight proteins using the scientific literature that would not have been seen using data integration alone. Ondex is an open-source software project and can be downloaded, together with the text mining features described here, from www.ondex.org.
A Model of Phospholipid Biosynthesis in Tumor in Response to an Anticancer Agent in Vivo
Authors: Behzadi Mahsa , Demidem Aicha , Morvan Daniel , Schwartz Laurent , Stepien Georges , Steyaert Jean-Marc 
Published: December 2017

SummaryWe study, in this paper, a model for the core of the system of the Glycerophospholipid metabolism in the murine cells. It comprises the simple and enzymatic reactions of PhosphatidylEthanolamine and the PhosphatidylCholine . The model’s general structure is taken from a number of books and articles. We translate this model into a set of ordinary differential equations (ODEs), to propose a quantitative explanation of the experimental experiences and the observed results. In order to make it usable as a basis for simulations and mathematical analysis we need to make precise the various constants present in the equations but which are usually not directly accessible in the literature. In a first step we considered experimental data of rat’s liver cells obtained by NMR spectroscopy: given the values of metabolite concentrations we find appropriate parameter values which allow us to describe the system with ODEs. We have then performed several analyses using the developed model such as stability analysis. A first interesting result is the global stability of the system which was observed by simulation and then proved by mathematical arguments. A second important result is that we observe on the diagrams that the steady state for normal cells is precisely a singular point of order two, whereas tumoral cells present different characteristics; this fact has been proved for PhosphatidylEthanolamine N-Methyl transferase (PEMT), an enzyme which seems to be identified for the first time as a crucial element in the tumoral process. In a second step we applied our model to experimental data of proton HRMAS NMR spectroscopy for solid B16 melanoma and Lewis lung (3LL) 3LL carcinoma cells treated by Chloroethyl Nitrosourea (CENU). We performed a complete comparative analysis of parameters in order to learn the predictive statements to explain increases and decreases which one can observe in concentrations.
Clustering of gene expression profiles: creating initialization-independent clusterings by eliminating unstable genes
Authors: De Mulder Wim , Kuiper Martin , Boel René 
Published: December 2017

SummaryClustering is an important approach in the analysis of biological data, and often a first step to identify interesting patterns of coexpression in gene expression data. Because of the high complexity and diversity of gene expression data, many genes cannot be easily assigned to a cluster, but even if the dissimilarity of these genes with all other gene groups is large, they will finally be forced to become member of a cluster. In this paper we show how to detect such elements, called unstable elements. We have developed an approach for iterative clustering algorithms in which unstable elements are deleted, making the iterative algorithm less dependent on initial centers. Although the approach is unsupervised, it is less likely that the clusters into which the reduced data set is subdivided contain false positives. This clustering yields a more differentiated approach for biological data, since the cluster analysis is divided into two parts: the pruned data set is divided into highly consistent clusters in an unsupervised way and the removed, unstable elements for which no meaningful cluster exists in unsupervised terms can be given a cluster with the use of biological knowledge and information about the likelihood of cluster membership. We illustrate our framework on both an artificial and real biological data set.
A flexible statistics web processing service - Added value for information systems for experiment data
Authors: Heimann Dennis , Nieschulze Jens , König-Ries Birgitta 
Published: December 2017

SummaryData management in the life sciences has evolved from simple storage of data to complex information systems providing additional functionalities like analysis and visualization capabilities, demanding the integration of statistical tools.In many cases the used statistical tools are hard-coded within the system. That leads to an expensive integration, substitution, or extension of tools because all changes have to be done in program code.Other systems are using generic solutions for tool integration but adapting them to another system is mostly rather extensive work.This paper shows a way to provide statistical functionality over a statistics web service, which can be easily integrated in any information system and set up using XML configuration files. The statistical functionality is extendable by simply adding the description of a new application to a configuration file. The service architecture as well as the data exchange process between client and service and the adding of analysis applications to the underlying service provider are described. Furthermore a practical example demonstrates the functionality of the service.
PathJam: a new service for integrating biological pathway information
Authors: Glez-Peña Daniel , Reboiro-Jato Miguel , Domínguez Rubén , Gómez-López Gonzalo , Pisano David G. , Fdez-Riverola Florentino 
Published: December 2017

SummaryBiological pathways are crucial to much of the scientific research today including the study of specific biological processes related with human diseases. PathJam is a new comprehensive and freely accessible web-server application integrating scattered human pathway annotation from several public sources. The tool has been designed for both (i) being intuitive for wet-lab users providing statistical enrichment analysis of pathway annotations and (ii) giving support to the development of new integrative pathway applications. PathJam’s unique features and advantages include interactive graphs linking pathways and genes of interest, downloadable results in fully compatible formats, GSEA compatible output files and a standardized RESTful API.
Visualization and Analysis of a Cardio Vascular Diseaseand MUPP1-related Biological Network combining Text Mining and Data Warehouse Approaches
Authors: Sommer Björn , Tiys Evgeny S. , Kormeier Benjamin , Hippe Klaus , Janowski Sebastian J. , Ivanisenko Timofey V. , Bragin Anatoly O. , Arrigo Patrizio , Demenkov Pavel S. , Kochetov Alexey V. , Ivanisenko Vladimir A. , Kolchanov Nikolay A. , Hofestädt Ralf 
Published: December 2017

SummaryDetailed investigation of socially important diseases with modern experimental methods has resulted in the generation of large volume of valuable data. However, analysis and interpretation of this data needs application of efficient computational techniques and systems biology approaches. In particular, the techniques allowing the reconstruction of associative networks of various biological objects and events can be useful. In this publication, the combination of different techniques to create such a network associated with an abstract cell environment is discussed in order to gain insights into the functional as well as spatial interrelationships. It is shown that experimentally gained knowledge enriched with data warehouse content and text mining data can be used for the reconstruction and localization of a cardiovascular disease developing network beginning with MUPP1/MPDZ (multi-PDZ domain protein).
Web2.0 paves new ways for collaborative and exploratory analysis of Chemical Compounds in Spectrometry Data
Authors: Loyek Christian , Bunkowski Alexander , Vautz Wolfgang , Nattkemper Tim W. 
Published: December 2017

SummaryIn nowadays life science projects, sharing data and data interpretation is becoming increasingly important. This considerably calls for novel information technology approaches, which enable the integration of expert knowledge from different disciplines in combination with advanced data analysis facilities in a collaborative manner. Since the recent development of web technologies offers scientific communities new ways for cooperation and communication, we propose a fully web-based software approach for the collaborative analysis of bioimage data and demonstrate the applicability of Web2.0 techniques to ion mobility spectrometry image data. Our approach allows collaborating experts to easily share, explore and discuss complex image data without any installation of software packages. Scientists only need a username and a password to get access to our system and can directly start exploring and analyzing their data.
Life Sciences Data and Application Integration with B-Fabric
Authors: Türker Can , Akal Fuat , Schlapbach Ralph 
Published: December 2017

Summary In this demo paper, we sketch B-Fabric, an all-in-one solution for management of life sciences data. B-Fabric has two major purposes. First, it is a system for the integrated management of experimental data and scientific annotations. Second, it is a system infrastructure supporting on-the fly coupling of user applications, and thus serving as extensible platform for fast-paced, cutting-edge, collaborative research.
GMB: An Efficient Query Processor for Biological Data
Authors: Taha Kamal , Elmasri Ramez 
Published: December 2017

Summary Bioinformatics applications manage complex biological data stored into distributed and often heterogeneous databases and require large computing power. These databases are too big and complicated to be rapidly queried every time a user submits a query, due to the overhead involved in decomposing the queries, sending the decomposed queries to remote databases, and composing the results. There is also considerable communication costs involved. This study addresses the mentioned problems in Grid-based environment for bioinformatics. We propose a Grid middleware called GMB that alleviates these problems by caching the results of Frequently Used Queries (FUQ). Queries are classified based on their types and frequencies. FUQ are answered from the middleware, which improves their response time. GMB acts as a gateway to TeraGrid Grid: it resides between users’ applications and TeraGrid Grid. We evaluate GMB experimentally.
Modelling Proteolytic Enzymes With Support Vector Machines
Authors: Morgado Lionel , Pereira Carlos , Veríssimo Paula , Dourado António 
Published: December 2017

Summary The strong activity felt in proteomics during the last decade created huge amounts of data, for which the knowledge is limited. Retrieving information from these proteins is the next step. For that, computational techniques are indispensable. Although there is not yet a silver bullet approach to solve the problem of enzyme detection and classification, machine learning formulations such as the state-of-the-art Support Vector Machine (SVM) appear among the most reliable options. A SVM based framework for peptidase analysis, that recognizes the hierarchies demarked in the MEROPS database is presented. Feature selection with SVM-RFE is used to improve the discriminative models and build classifiers computationally more efficient than alignment based techniques.
Prognostic Prediction through Biclustering-Based Classification of Clinical Gene Expression Time Series
Authors: Carreiro André V. , Anunciação Orlando , Carriço João A. , Madeira Sara C. 
Published: December 2017

Summary The constant drive towards a more personalized medicine led to an increasing interest in temporal gene expression analyzes. It is now broadly accepted that considering a temporal perspective represents a great advantage to better understand disease progression and treatment results at a molecular level. In this context, biclustering algorithms emerged as an important tool to discover local expression patterns in biomedical applications, and CCC-Biclustering arose as an efficient algorithm relying on the temporal nature of data to identify all maximal temporal patterns in gene expression time series. In this work, CCC-Biclustering was integrated in new biclustering-based classifiers for prognostic prediction. As case study we analyzed multiple gene expression time series in order to classify the response of Multiple Sclerosis patients to the standard treatment with Interferon-β, to which nearly half of the patients reveal a negative response. In this scenario, using an effective predictive model of a patient’s response would avoid useless and possibly harmful therapies for the non-responder group. The results revealed interesting potentialities to be further explored in classification problems involving other (clinical) time series.
Classification methods for finding articles describing protein-protein interactions in PubMed
Authors: Matos Sérgio , Oliveira José Luis 
Published: December 2017

Summary With the rapid expansion in the number of published papers in the biomedical field, finding relevant articles has become a demanding task for researchers. This has led to increasing interest in the use of text mining tools that help search the literature and identify the most relevant documents or information. One specific topic of interest is related to the identification of articles that might be used for extracting protein-protein interactions. Using the BioCreative III Article Classification Task dataset, composed of PubMed abstracts classified as relevant or non-relevant for describing protein-protein interactions, we compare different classification methods with different sets of features. The best results - area under the interpolated precision-recall curve of 0.654 - indicate that the proposed classification strategy could be incorporated in the database curation workflows in order to prioritize articles for extraction of protein-protein interactions. Furthermore, we also analysed the use of this method for ranking documents resulting from general PubMed queries, and propose that this approach could be useful for general researchers looking for publications describing protein-protein interactions within a particular topic of interest.
Tracking T and B cells from two-photon microscopy imaging using constrained SMC clusters
Authors: Olivieri D. , Faro J. , Gomez-Conde I. , Tadokoro C. E. 
Published: December 2017

Summary This paper describes a novel software algorithm, called constrained Sequential Monte Carlo (SMC) clusters, for tracking a large collection of individual cells from intra-vital two-photon microscopy image sequences. We show how our method and software tool, implemented in python, is useful for quantifying the motility of T and B lymphocytes involved in an immune response vs lymphocytes under non immune conditions. We describe the theory behind our algorithm and briefly discuss the architecture of our software. Finally, we demonstrate both the functionality and utility of software by applying it to two practical examples from videos displaying lymphocyte motility in B cell zones (follicles) and T cell zones of lymph nodes.
A Study of the Short and Long-term Regulation of E. coli Metabolic Pathways
Authors: Lourenço Anália , Carneiro Sónia , Pinto José P. , Rocha Miguel , Ferreira Eugénio C. , Rocha Isabel 
Published: December 2017

Summary The present study addresses the regulatory network of Escherichia coli and offers a global view of the short- and long-term regulation of its metabolic pathways. The regulatory mechanisms responsible for key metabolic activities and the structure behind such mechanisms are detailed. Most metabolic functions are dependent on the activity of transcriptional regulators over gene expression - the so-called long-term regulation. However, enzymatic regulation - the so-called short-term regulation - often overlays transcriptional regulation and even, in particular metabolic pathways, enzymatic regulation may prevail. As such, understanding the balance between these two types of regulation is necessary to be able to predict and control cell responses, specifically cell responses to the various environmental stresses.
IDPredictor: predict database links in biomedical database
Authors: Mehlhorn Hendrik , Lange Matthias , Scholz Uwe , Schreiber Falk 
Published: December 2017

Summary Knowledge found in biomedical databases, in particular in Web information systems, is a major bioinformatics resource. In general, this biological knowledge is worldwide represented in a network of databases. These data is spread among thousands of databases, which overlap in content, but differ substantially with respect to content detail, interface, formats and data structure. To support a functional annotation of lab data, such as protein sequences, metabolites or DNA sequences as well as a semi-automated data exploration in information retrieval environments, an integrated view to databases is essential. Search engines have the potential of assisting in data retrieval from these structured sources, but fall short of providing a comprehensive knowledge excerpt out of the interlinked databases. A prerequisit of supporting the concept of an integrated data view is to acquire insights into cross-references among database entities. This issue is being hampered by the fact, that only a fraction of all possible cross-references are explicitely tagged in the particular biomedical informations systems. In this work, we investigate to what extend an automated construction of an integrated data network is possible. We propose a method that predicts and extracts cross-references from multiple life science databases and possible referenced data targets. We study the retrieval quality of our method and report on first, promising results. The method is implemented as the tool IDPredictor, which is published under the DOI 10.5447/IPK/2012/4 and is freely available using the URL: http://dx.doi.org/10.5447/IPK/2012/4.
Integrated simultaneous analysis of different biomedical data types with exact weighted bi-cluster editing
Authors: Sun Peng , Guo Jiong , Baumbach Jan 
Published: December 2017

Summary The explosion of biological data has largely influenced the focus of today’s biology research. Integrating and analysing large quantity of data to provide meaningful insights has become the main challenge to biologists and bioinformaticians. One major problem is the combined data analysis of data from different types, such as phenotypes and genotypes. This data is modelled as bi-partite graphs where nodes correspond to the different data points, mutations and diseases for instance, and weighted edges relate to associations between them. Bi-clustering is a special case of clustering designed for partitioning two different types of data simultaneously. We present a bi-clustering approach that solves the NP-hard weighted bi-cluster editing problem by transforming a given bi-partite graph into a disjoint union of bi-cliques. Here we contribute with an exact algorithm that is based on fixed-parameter tractability. We evaluated its performance on artificial graphs first. Afterwards we exemplarily applied our Java implementation to data of genome-wide association studies (GWAS) data aiming for discovering new, previously unobserved geno-to-pheno associations. We believe that our results will serve as guidelines for further wet lab investigations. Generally our software can be applied to any kind of data that can be modelled as bi-partite graphs. To our knowledge it is the fastest exact method for weighted bi-cluster editing problem.
ADOPS - Automatic Detection Of Positively Selected Sites
Authors: Reboiro-Jato David , Reboiro-Jato Miguel , Fdez-Riverola Florentino , Vieira Cristina P. , Fonseca Nuno A. , Vieira Jorge 
Published: December 2017

SummaryMaximum-likelihood methods based on models of codon substitution have been widely used to infer positively selected amino acid sites that are responsible for adaptive changes. Nevertheless, in order to use such an approach, software applications are required to align protein and DNA sequences, infer a phylogenetic tree and run the maximum-likelihood models. Therefore, a significant effort is made in order to prepare input files for the different software applications and in the analysis of the output of every analysis. In this paper we present the ADOPS (Automatic Detection Of Positively Selected Sites) software. It was developed with the goal of providing an automatic and flexible tool for detecting positively selected sites given a set of unaligned nucleotide sequence data. An example of the usefulness of such a pipeline is given by showing, under different conditions, positively selected amino acid sites in a set of 54 Coffea putative S-RNase sequences. ADOPS software is freely available and can be downloaded from http://sing.ei.uvigo.es/ADOPS.
In silico strain optimization by adding reactions to metabolic models
Authors: Correia Sara , Rocha Miguel 
Published: December 2017

Summary Nowadays, the concerns about the environment and the needs to increase the productivity at low costs, demand for the search of new ways to produce compounds with industrial interest. Based on the increasing knowledge of biological processes, through genome sequencing projects, and high-throughput experimental techniques as well as the available computational tools, the use of microorganisms has been considered as an approach to produce desirable compounds. However, this usually requires to manipulate these organisms by genetic engineering and/ or changing the enviromental conditions to make the production of these compounds possible. In many cases, it is necessary to enrich the genetic material of those microbes with hereologous pathways from other species and consequently adding the potential to produce novel compounds.This paper introduces a new plug-in for the OptFlux Metabolic Engineering platform, aimed at finding suitable sets of reactions to add to the genomes of selected microbes (wild type strain), as well as finding complementary sets of deletions, so that the mutant becomes able to overproduce compounds with industrial interest, while preserving their viability. The necessity of adding reactions to the metabolic model arises from existing gaps in the original model or motivated by the productions of new compounds by the organism. The optimization methods used are metaheuristics such as Evolutionary Algorithms and Simulated Annealing. The usefulness of this plug-in is demonstrated by a case study, regarding the production of vanillin by the bacterium E. coli.