| 1. |
- Brinkrolf, Christoph, et al.
(författare)
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Modeling and Simulating the Aerobic Carbon Metabolism of a Green Microalga Using Petri Nets and New Concepts of VANESA
- 2018
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Ingår i: Journal of integrative bioinformatics. - : De Gruyter Open. - 1613-4516. ; 15:3
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Tidskriftsartikel (refereegranskat)abstract
- 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. VANESA, as well as OpenModelica, is open source, free-of-charge for non-commercial use, and is available at: http://agbi.techfak.uni-bielefeld.de/vanesa.
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| 2. |
- Brunak, S, et al.
(författare)
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Towards standardization guidelines for in silico approaches in personalized medicine
- 2020
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Ingår i: Journal of integrative bioinformatics. - : Walter de Gruyter GmbH. - 1613-4516. ; 17:2-3
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Tidskriftsartikel (refereegranskat)abstract
- Despite the ever-progressing technological advances in producing data in health and clinical research, the generation of new knowledge for medical benefits through advanced analytics still lags behind its full potential. Reasons for this obstacle are the inherent heterogeneity of data sources and the lack of broadly accepted standards. Further hurdles are associated with legal and ethical issues surrounding the use of personal/patient data across disciplines and borders. Consequently, there is a need for broadly applicable standards compliant with legal and ethical regulations that allow interpretation of heterogeneous health data through in silico methodologies to advance personalized medicine. To tackle these standardization challenges, the Horizon2020 Coordinating and Support Action EU-STANDS4PM initiated an EU-wide mapping process to evaluate strategies for data integration and data-driven in silico modelling approaches to develop standards, recommendations and guidelines for personalized medicine. A first step towards this goal is a broad stakeholder consultation process initiated by an EU-STANDS4PM workshop at the annual COMBINE meeting (COMBINE 2019 workshop report in same issue). This forum analysed the status quo of data and model standards and reflected on possibilities as well as challenges for cross-domain data integration to facilitate in silico modelling approaches for personalized medicine.
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| 3. |
- Gubanova, N. V., et al.
(författare)
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Glioblastoma gene network reconstruction and ontology analysis by online bioinformatics tools
- 2021
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Ingår i: Journal of Integrative Bioinformatics. - : Walter de Gruyter GmbH. - 1613-4516. ; 18:4
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Tidskriftsartikel (refereegranskat)abstract
- Glioblastoma is the most aggressive type of brain tumors resistant to a number of antitumor drugs. The problem of therapy and drug treatment course is complicated by extremely high heterogeneity in the benign cell populations, the random arrangement of tumor cells, and polymorphism of their nuclei. The pathogenesis of gliomas needs to be studied using modern cellular technologies, genome- and transcriptome-wide technologies of high-throughput sequencing, analysis of gene expression on microarrays, and methods of modern bioinformatics to find new therapy targets. Functional annotation of genes related to the disease could be retrieved based on genetic databases and cross-validated by integrating complementary experimental data. Gene network reconstruction for a set of genes (proteins) proved to be effective approach to study mechanisms underlying disease progression. We used online bioinformatics tools for annotation of gene list for glioma, reconstruction of gene network and comparative analysis of gene ontology categories. The available tools and the databases for glioblastoma gene analysis are discussed together with the recent progress in this field.
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| 4. |
- Huss, Mikael, et al.
(författare)
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Prediction of transcription factor binding to DNA using rule induction methods
- 2006
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Ingår i: Journal of Integrative Bioinformatics - JIB. - 1613-4516. ; 3:2, s. 42-
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Tidskriftsartikel (refereegranskat)abstract
- In this study, we seek to develop a predictive model for finding the strength of bindingbetween a particular transcription factor (TF) variant and a particular DNA target variant.The DNA binding paired domains of the Pax transcription factors, which are our mainfocus, show seemingly fuzzy and degenerate binding to various DNA targets, and paireddomain-DNA binding is not a problem well suited for previously proposed algorithms.Here, we introduce a simple way to use rule induction for predicting the strength of TFDNAbinding. We have created a dataset consisting of 597 example cases for paireddomain-DNA binding by collecting information about all published and quantifiedinteractions between TF and DNA sequence variants. Application of the rule inductionbased method on this dataset yields a high, although far from ideal accuracy of 69.7%(based on cross-validation), but perhaps more importantly, several useful rules forpredicting the binding strength have been found. Although the primary motivation forintroducing the rule induction based methods is the lack of efficient algorithms for paireddomain-DNA binding prediction, we also show that the method can be applied with somesuccess to a more well-studied TF-DNA binding prediction task involving the earlygrowth response (EGR) TF family.
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| 5. |
- Jakoniené, Vaida, et al.
(författare)
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Tool for Evaluating Strategies for Grouping of Biological Data
- 2007
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Ingår i: Journal of Integrative Bioinformatics. - 1613-4516. ; 4:3
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Tidskriftsartikel (refereegranskat)abstract
- During the last decade an enormous amount of biological data has been generated and techniques and tools to analyze this data have been developed. Many of these tools use some form of grouping and are used in, for instance, data integration, data cleaning, prediction of protein functionality, and correlation of genes based on microarray data. A number of aspects influence the quality of the grouping results: the data sources, the grouping attributes and the algorithms implementing the grouping procedure. Many methods exist, but it is often not clear which methods perform best for which grouping tasks. The study of the properties, and the evaluation and the comparison of the different aspects that influence the quality of the grouping results, would give us valuable insight in how the grouping procedures could be used in the best way. It would also lead to recommendations on how to improve the current procedures and develop new procedures. To be able to perform such studies and evaluations we need environments that allow us to compare and evaluate different grouping strategies. In this paper we present a framework, KitEGA, for such an environment, and present its current prototype implementation. We illustrate its use by comparing grouping strategies for classifying proteins regarding biological function and isozymes.
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| 6. |
- Selpi, Selpi, 1977, et al.
(författare)
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A First Step towards Learning which uORFs Regulate Gene Expression
- 2006
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Ingår i: Journal of integrative bioinformatics. - 1613-4516. ; 3:2, s. 31-
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Tidskriftsartikel (refereegranskat)abstract
- We have taken a first step towards learning which upstream Open Reading Frames (uORFs) regulate gene expression (i.e., which uORFs are functional) in the yeast Saccharomyces cerevisiae. We do this by integrating data from several resources and combining a bioinformatics tool, ORF Finder, with a machine learning technique, inductive logic programming (ILP). Here, we report the challenge of using ILP as part of this integrative system, in order to automatically generate a model that identifies functional uORFs. Our method makes searching for novel functional uORFs more efficient than random sampling. An attempt has been made to predict novel functional uORFs using our method. Some preliminary evidence that our model may be biologically meaningful is presented.
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| 7. |
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| 8. |
- Ståhl, Niclas, 1990-, et al.
(författare)
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Deep Convolutional Neural Networks for the Prediction of Molecular Properties : Challenges and Opportunities Connected to the Data
- 2018
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Ingår i: Journal of Integrative Bioinformatics. - : De Gruyter Open. - 1613-4516. ; 16:1
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Tidskriftsartikel (refereegranskat)abstract
- 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. Several best practices for training deep convolutional neural networks on chemical datasets are also highlighted within the article, such as how to select the information to be included in the model, how to prevent overfitting and how unbalanced classes in the data can be handled.
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