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Journal of Integrative Bioinformatics

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https://read.qxmd.com/read/30768424/selected-extended-papers-of-the-12th-international-conference-on-practical-applications-of-computational-biology-and-bioinformatics-pacbb
#1
EDITORIAL
Florentino Fdez-Riverola, Miguel Rocha
No abstract text is available yet for this article.
February 15, 2019: Journal of Integrative Bioinformatics
https://read.qxmd.com/read/30763265/dynamic-load-balancing-strategy-for-parallel-tumor-growth-simulations
#2
Alberto G Salguero, Antonio J Tomeu-Hardasmal, Manuel I Capel
In this paper, we propose a parallel cellular automaton tumor growth model that includes load balancing of cells distribution among computational threads with the introduction of adjusting parameters. The obtained results show a fair reduction in execution time and improved speedup compared with the sequential tumor growth simulation program currently referenced in tumoral biology. The dynamic data structures of the model can be extended to address additional tumor growth characteristics such as angiogenesis and nutrient intake dependencies...
February 14, 2019: Journal of Integrative Bioinformatics
https://read.qxmd.com/read/30763264/qsar-modelling-to-identify-lrrk2-inhibitors-for-parkinson-s-disease
#3
Víctor Sebastián-Pérez, María Jimena Martínez, Carmen Gil, Nuria Eugenia Campillo, Ana Martínez, Ignacio Ponzoni
Parkinson's disease is one of the most common neurodegenerative illnesses in older persons and the leucine-rich repeat kinase 2 (LRRK2) is an auspicious target for its pharmacological treatment. In this work, quantitative structure-activity relationship (QSAR) models for identification of putative inhibitors of LRRK2 protein are developed by using an in-house chemical library and several machine learning techniques. The methodology applied in this paper has two steps: first, alternative subsets of molecular descriptors useful for characterizing LRRK2 inhibitors are chosen by a multi-objective feature selection method; secondly, QSAR models are learned by using these subsets and three different strategies for supervised learning...
February 14, 2019: Journal of Integrative Bioinformatics
https://read.qxmd.com/read/30785707/integrative-gene-selection-on-gene-expression-data-providing-biological-context-to-traditional-approaches
#4
Cindy Perscheid, Bastien Grasnick, Matthias Uflacker
The advance of high-throughput RNA-Sequencing techniques enables researchers to analyze the complete gene activity in particular cells. From the insights of such analyses, researchers can identify disease-specific expression profiles, thus understand complex diseases like cancer, and eventually develop effective measures for diagnosis and treatment. The high dimensionality of gene expression data poses challenges to its computational analysis, which is addressed with measures of gene selection. Traditional gene selection approaches base their findings on statistical analyses of the actual expression levels, which implies several drawbacks when it comes to accurately identifying the underlying biological processes...
December 22, 2018: Journal of Integrative Bioinformatics
https://read.qxmd.com/read/30864422/a-model-integration-pipeline-for-the-improvement-of-human-genome-scale-metabolic-reconstructions
#5
Vítor Vieira, Jorge Ferreira, Rúben Rodrigues, Filipe Liu, Miguel Rocha
Metabolism has been a major field of study in the last years, mainly due to its importance in understanding cell physiology and certain disease phenotypes due to its deregulation. Genome-scale metabolic models (GSMMs) have been established as important tools to help achieve a better understanding of human metabolism. Towards this aim, advances in systems biology and bioinformatics have allowed the reconstruction of several human GSMMs, although some limitations and challenges remain, such as the lack of external identifiers for both metabolites and reactions...
December 21, 2018: Journal of Integrative Bioinformatics
https://read.qxmd.com/read/30808158/selected-extended-papers-of-the-12th-international-conference-on-practical-applications-of-computational-biology-and-bioinformatics-pacbb
#6
EDITORIAL
Florentino Fdez-Riverola, Miguel Rocha
No abstract text is available yet for this article.
February 15, 2019: Journal of Integrative Bioinformatics
https://read.qxmd.com/read/30864352/genconet-a-graph-database-for-the-analysis-of-comorbidities-by-gene-networks
#7
Alban Shoshi, Ralf Hofestädt, Olga Zolotareva, Marcel Friedrichs, Alex Maier, Vladimir A Ivanisenko, Victor E Dosenko, Elena Yu Bragina
The prevalence of comorbid diseases poses a major health issue for millions of people worldwide and an enormous socio-economic burden for society. The molecular mechanisms for the development of comorbidities need to be investigated. For this purpose, a workflow system was developed to aggregate data on biomedical entities from heterogeneous data sources. The process of integrating and merging all data sources of the workflow system was implemented as a semi-automatic pipeline that provides the import, fusion, and analysis of the highly connected biomedical data in a Neo4j database GenCoNet...
December 25, 2018: Journal of Integrative Bioinformatics
https://read.qxmd.com/read/30864351/search-for-new-candidate-genes-involved-in-the-comorbidity-of-asthma-and-hypertension-based-on-automatic-analysis-of-scientific-literature
#8
Olga V Saik, Pavel S Demenkov, Timofey V Ivanisenko, Elena Yu Bragina, Maxim B Freidin, Victor E Dosenko, Olga I Zolotareva, Evgeniy L Choynzonov, Ralf Hofestaedt, Vladimir A Ivanisenko
Comorbid states of diseases significantly complicate diagnosis and treatment. Molecular mechanisms of comorbid states of asthma and hypertension are still poorly understood. Prioritization is a way for identifying genes involved in complex phenotypic traits. Existing methods of prioritization consider genetic, expression and evolutionary data, molecular-genetic networks and other. In the case of molecular-genetic networks, as a rule, protein-protein interactions and KEGG networks are used. ANDSystem allows reconstructing associative gene networks, which include more than 20 types of interactions, including protein-protein interactions, expression regulation, transport, catalysis, etc...
December 25, 2018: Journal of Integrative Bioinformatics
https://read.qxmd.com/read/30808160/a-model-integration-pipeline-for-the-improvement-of-human-genome-scale-metabolic-reconstructions
#9
Vítor Vieira, Jorge Ferreira, Rúben Rodrigues, Filipe Liu, Miguel Rocha
Metabolism has been a major field of study in the last years, mainly due to its importance in understanding cell physiology and certain disease phenotypes due to its deregulation. Genome-scale metabolic models (GSMMs) have been established as important tools to help achieve a better understanding of human metabolism. Towards this aim, advances in systems biology and bioinformatics have allowed the reconstruction of several human GSMMs, although some limitations and challenges remain, such as the lack of external identifiers for both metabolites and reactions...
December 21, 2018: Journal of Integrative Bioinformatics
https://read.qxmd.com/read/30864353/integrative-analysis-of-co-morbid-multifactorial-diseases
#10
EDITORIAL
Ralf Hofestädt, Vladimir Ivanisenko
No abstract text is available yet for this article.
December 15, 2018: Journal of Integrative Bioinformatics
https://read.qxmd.com/read/30530896/molecular-relationships-between-bronchial-asthma-and-hypertension-as-comorbid-diseases
#11
Elena Yu Bragina, Irina A Goncharova, Anna F Garaeva, Evgeniy V Nemerov, Anastasija A Babovskaya, Andrey B Karpov, Yulia V Semenova, Irina Z Zhalsanova, Densema E Gomboeva, Olga V Saik, Olga I Zolotareva, Vladimir A Ivanisenko, Victor E Dosenko, Ralf Hofestaedt, Maxim B Freidin
Comorbidity, a co-incidence of several disorders in an individual, is a common phenomenon. Their development is governed by multiple factors, including genetic variation. The current study was set up to look at associations between isolated and comorbid diseases of bronchial asthma and hypertension, on one hand, and single nucleotide polymorphisms associated with regulation of gene expression (eQTL), on the other hand. A total of 96 eQTL SNPs were genotyped in 587 Russian individuals. Bronchial asthma alone was found to be associated with rs1927914 (TLR4), rs1928298 (intergenic variant), and rs1980616 (SERPINA1); hypertension alone was found to be associated with rs11065987 (intergenic variant); rs2284033 (IL2RB), rs11191582 (NT5C2), and rs11669386 (CARD8); comorbidity between asthma and hypertension was found to be associated with rs1010461 (ANG/RNASE4), rs7038716, rs7026297 (LOC105376244), rs7025144 (intergenic variant), and rs2022318 (intergenic variant)...
December 10, 2018: Journal of Integrative Bioinformatics
https://read.qxmd.com/read/30530891/shrna-induced-knockdown-of-a-bioinformatically-predicted-target-il10-influences-functional-parameters-in-spontaneously-hypertensive-rats-with-asthma
#12
Tatiana Drevytska, Roman Morhachov, Lesya Tumanovska, Georgiy Portnichenko, Vasyl Nagibin, Oleksiy Boldyriev, Tatiana Lapikova-Bryhinska, Veronika Gurianova, Borys Dons'koi, Maxim Freidin, Vladimir Ivanisenko, Elena Yu Bragina, Ralf Hofestädt, Victor Dosenko
One of the most common comorbid pathology is asthma and arterial hypertension. For experimental modeling of comorbidity we have used spontaneously hypertensive rats with ovalbumin (OVA)-induced asthma. Rats were randomly divided into three groups: control group, OVA-induced asthma group; OVA-induced asthma + IL10 shRNA interference group. Target gene (IL10) was predicted by ANDSystem. We have demonstrated that RNA-interference of IL10 affected cardiovascular (tested using Millar microcatheter system) as well as respiratory functions (tested using force-oscillation technique, Flexivent) in rats...
December 10, 2018: Journal of Integrative Bioinformatics
https://read.qxmd.com/read/30517077/deep-convolutional-neural-networks-for-the-prediction-of-molecular-properties-challenges-and-opportunities-connected-to-the-data
#13
Niclas Ståhl, Göran Falkman, Alexander Karlsson, Gunnar Mathiason, Jonas Boström
We present a flexible deep convolutional neural network method for the analysis of arbitrary sized graph structures representing molecules. This method, which makes use of the Lipinski RDKit module, an open-source cheminformatics software, enables the incorporation of any global molecular (such as molecular charge and molecular weight) and local (such as atom hybridization and bond orders) information. In this paper, we show that this method significantly outperforms another recently proposed method based on deep convolutional neural networks on several datasets that are studied...
December 5, 2018: Journal of Integrative Bioinformatics
https://read.qxmd.com/read/30367805/a-content-based-retrieval-framework-for-whole-metagenome-sequencing-samples
#14
Duygu Dede Şener, Daniele Santoni, Giovanni Felici, Hasan Oğul
Finding similarities and differences between metagenomic samples within large repositories has been rather a significant issue for researchers. Over the recent years, content-based retrieval has been suggested by various studies from different perspectives. In this study, a content-based retrieval framework for identifying relevant metagenomic samples is developed. The framework consists of feature extraction, selection methods and similarity measures for whole metagenome sequencing samples. Performance of the developed framework was evaluated on given samples...
October 26, 2018: Journal of Integrative Bioinformatics
https://read.qxmd.com/read/30218605/modeling-and-simulating-the-aerobic-carbon-metabolism-of-a-green-microalga-using-petri-nets-and-new-concepts-of-vanesa
#15
Christoph Brinkrolf, Nadja A Henke, Lennart Ochel, Boas Pucker, Olaf Kruse, Petra Lutter
In this work we present new concepts of VANESA, a tool for modeling and simulation in systems biology. We provide a convenient way to handle mathematical expressions and take physical units into account. Simulation and result management has been improved, and syntax and consistency checks, based on physical units, reduce modeling errors. As a proof of concept, essential components of the aerobic carbon metabolism of the green microalga Chlamydomonas reinhardtii are modeled and simulated. The modeling process is based on xHPN Petri net formalism and simulation is performed with OpenModelica, a powerful environment and compiler for Modelica...
September 15, 2018: Journal of Integrative Bioinformatics
https://read.qxmd.com/read/30205646/biomodelkit-an-integrative-framework-for-multi-scale-biomodel-engineering
#16
Mary-Ann Blätke
While high-throughput technology, advanced techniques in biochemistry and molecular biology have become increasingly powerful, the coherent interpretation of experimental results in an integrative context is still a challenge. BioModelKit (BMK) approaches this challenge by offering an integrative and versatile framework for biomodel-engineering based on a modular modelling concept with the purpose: (i) to represent knowledge about molecular mechanisms by consistent executable sub-models (modules) given as Petri nets equipped with defined interfaces facilitating their reuse and recombination; (ii) to compose complex and integrative models from an ad hoc chosen set of modules including different omic and abstraction levels with the option to integrate spatial aspects; (iii) to promote the construction of alternative models by either the exchange of competing module versions or the algorithmic mutation of the composed model; and (iv) to offer concepts for (omic) data integration and integration of existing resources, and thus facilitate their reuse...
September 6, 2018: Journal of Integrative Bioinformatics
https://read.qxmd.com/read/30085931/towards-fairer-biological-knowledge-networks-using-a-hybrid-linked-data-and-graph-database-approach
#17
Marco Brandizi, Ajit Singh, Christopher Rawlings, Keywan Hassani-Pak
The speed and accuracy of new scientific discoveries - be it by humans or artificial intelligence - depends on the quality of the underlying data and on the technology to connect, search and share the data efficiently. In recent years, we have seen the rise of graph databases and semi-formal data models such as knowledge graphs to facilitate software approaches to scientific discovery. These approaches extend work based on formalised models, such as the Semantic Web. In this paper, we present our developments to connect, search and share data about genome-scale knowledge networks (GSKN)...
August 7, 2018: Journal of Integrative Bioinformatics
https://read.qxmd.com/read/30001212/heuristic-modeling-and-3d-stereoscopic-visualization-of-a-chlamydomonas-reinhardtii-cell
#18
Niklas Biere, Mehmood Ghaffar, Anja Doebbe, Daniel Jäger, Nils Rothe, Benjamin M Friedrich, Ralf Hofestädt, Falk Schreiber, Olaf Kruse, Björn Sommer
The structural modeling and representation of cells is a complex task as different microscopic, spectroscopic and other information resources have to be combined to achieve a three-dimensional representation with high accuracy. Moreover, to provide an appropriate spatial representation of the cell, a stereoscopic 3D (S3D) visualization is favorable. In this work, a structural cell model is created by combining information from various light microscopic and electron microscopic images as well as from publication-related data...
July 11, 2018: Journal of Integrative Bioinformatics
https://read.qxmd.com/read/29982237/from-virtual-reality-to-immersive-analytics-in-bioinformatics
#19
Björn Sommer, Marc Baaden, Michael Krone, Andrew Woods
Bioinformatics-related research produces huge heterogeneous amounts of data. This wealth of information includes data describing metabolic mechanisms and pathways, proteomics, transcriptomics, and metabolomics. Often, the visualization and exploration of related structural - usually molecular - data plays an important role in the aforementioned contexts. For decades, virtual reality (VR)-related technologies were developed and applied to Bioinformatics problems. Often, these approaches provide "just" visual support of the analysis, e...
July 9, 2018: Journal of Integrative Bioinformatics
https://read.qxmd.com/read/29982236/semantics-for-an-integrative-and-immersive-pipeline-combining-visualization-and-analysis-of-molecular-data
#20
Mikael Trellet, Nicolas Férey, Jakub Flotyński, Marc Baaden, Patrick Bourdot
The advances made in recent years in the field of structural biology significantly increased the throughput and complexity of data that scientists have to deal with. Combining and analyzing such heterogeneous amounts of data became a crucial time consumer in the daily tasks of scientists. However, only few efforts have been made to offer scientists an alternative to the standard compartmentalized tools they use to explore their data and that involve a regular back and forth between them. We propose here an integrated pipeline especially designed for immersive environments, promoting direct interactions on semantically linked 2D and 3D heterogeneous data, displayed in a common working space...
July 9, 2018: Journal of Integrative Bioinformatics
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