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  • Artikel  (136)
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  • 1
    Publikationsdatum: 2014-01-22
    Beschreibung: Good accessibility of publicly funded research data is essential to secure an open scientific system and eventually becomes mandatory [Wellcome Trust will Penalise Scientists Who Don’t Embrace Open Access . The Guardian 2012]. By the use of high-throughput methods in many research areas from physics to systems biology, large data collections are increasingly important as raw material for research. Here, we present strategies worked out by international and national institutions targeting open access to publicly funded research data via incentives or obligations to share data. Funding organizations such as the British Wellcome Trust therefore have developed data sharing policies and request commitment to data management and sharing in grant applications. Increased citation rates are a profound argument for sharing publication data. Pre-publication sharing might be rewarded by a data citation credit system via digital object identifiers (DOIs) which have initially been in use for data objects. Besides policies and incentives, good practice in data management is indispensable. However, appropriate systems for data management of large-scale projects for example in systems biology are hard to find. Here, we give an overview of a selection of open-source data management systems proved to be employed successfully in large-scale projects.
    Print ISSN: 1467-5463
    Digitale ISSN: 1477-4054
    Thema: Biologie , Informatik
    Publiziert von Oxford University Press
    Standort Signatur Erwartet Verfügbarkeit
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  • 2
    Publikationsdatum: 2014-01-22
    Beschreibung: Genome-scale metabolic network reconstructions are now routinely used in the study of metabolic pathways, their evolution and design. The development of such reconstructions involves the integration of information on reactions and metabolites from the scientific literature as well as public databases and existing genome-scale metabolic models. The reconciliation of discrepancies between data from these sources generally requires significant manual curation, which constitutes a major obstacle in efforts to develop and apply genome-scale metabolic network reconstructions. In this work, we discuss some of the major difficulties encountered in the mapping and reconciliation of metabolic resources and review three recent initiatives that aim to accelerate this process, namely BKM-react, MetRxn and MNXref (presented in this article). Each of these resources provides a pre-compiled reconciliation of many of the most commonly used metabolic resources. By reducing the time required for manual curation of metabolite and reaction discrepancies, these resources aim to accelerate the development and application of high-quality genome-scale metabolic network reconstructions and models.
    Print ISSN: 1467-5463
    Digitale ISSN: 1477-4054
    Thema: Biologie , Informatik
    Publiziert von Oxford University Press
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  • 3
    facet.materialart.
    Unbekannt
    Oxford University Press
    Publikationsdatum: 2014-01-22
    Beschreibung: microRNAs (miRNAs) are small endogenous non-coding RNAs that function as the universal specificity factors in post-transcriptional gene silencing. Discovering miRNAs, identifying their targets and further inferring miRNA functions have been a critical strategy for understanding normal biological processes of miRNAs and their roles in the development of disease. In this review, we focus on computational methods of inferring miRNA functions, including miRNA functional annotation and inferring miRNA regulatory modules, by integrating heterogeneous data sources. We also briefly introduce the research in miRNA discovery and miRNA-target identification with an emphasis on the challenges to computational biology.
    Print ISSN: 1467-5463
    Digitale ISSN: 1477-4054
    Thema: Biologie , Informatik
    Publiziert von Oxford University Press
    Standort Signatur Erwartet Verfügbarkeit
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  • 4
    Publikationsdatum: 2014-01-22
    Beschreibung: Supermatrix and supertree analyses are frequently used to more accurately recover vertical evolutionary history but debate still exists over which method provides greater reliability. Traditional methods that resolve relationships among organisms from single genes are often unreliable because of the frequent lack of strong phylogenetic signal and the presence of systematic artifacts. Methods developed to reconstruct organismal history from multiple genes can be divided into supermatrix and supertree approaches. A supermatrix analysis consists of the concatenation of multiple genes into a single, possibly partitioned alignment, from which phylogenies are reconstructed using a variety of approaches. Supertrees build consensus trees from the topological information contained within individual gene trees. Both methods are now widely used and have been demonstrated to solve previously ambiguous or unresolved phylogenies with high statistical support. However, the amount of misleading signal needed to induce erroneous phylogenies for both strategies is still unknown. Using genome simulations, we test the accuracy of supertree and supermatrix approaches in recovering the true organismal phylogeny under increased amounts of horizontally transferred genes and changes in substitution rates. Our results show that overall, supermatrix approaches are preferable when a low amount of gene transfer is suspected to be present in the dataset, while supertrees have greater reliability in the presence of a moderate amount of misleading gene transfers. In the face of very high or very low substitution rates without horizontal gene transfers, supermatrix approaches outperform supertrees as individual gene trees remain unresolved and additional sequences contribute to a congruent phylogenetic signal.
    Print ISSN: 1467-5463
    Digitale ISSN: 1477-4054
    Thema: Biologie , Informatik
    Publiziert von Oxford University Press
    Standort Signatur Erwartet Verfügbarkeit
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  • 5
    Publikationsdatum: 2014-01-22
    Beschreibung: The iteratively reweighted least square (IRLS) method is mostly identical to maximum likelihood (ML) method in terms of parameter estimation and power of quantitative trait locus (QTL) detection. But the IRLS is greatly superior to ML in terms of computing speed and the robustness of parameter estimation. In conjunction with the priors of parameters, ML can analyze multiple QTL model based on Bayesian theory, whereas under a single QTL model, IRLS has very limited statistical power to detect multiple QTLs. In this study, we proposed the iteratively reweighted least absolute shrinkage and selection operator (IRLASSO) for extending IRLS to simultaneously map multiple QTLs. The LASSO with coordinate descent step is employed to efficiently estimate non-zero genetic effect of each locus scanned over entire genome. Simulations demonstrate that IRLASSO has a higher precision of parameter estimation and power to detect QTL than IRLS, and is able to estimate residual variance more accurately than the unweighted LASSO based on LS. Especially, IRLASSO is very fast, usually taking less than five iterations to converge. The barley dataset from the North American Barley Genome Mapping Project is reanalyzed by our proposed method.
    Print ISSN: 1467-5463
    Digitale ISSN: 1477-4054
    Thema: Biologie , Informatik
    Publiziert von Oxford University Press
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  • 6
    Publikationsdatum: 2014-01-22
    Beschreibung: The formation of phenotypic traits, such as biomass production, tumor volume and viral abundance, undergoes a complex process in which interactions between genes and developmental stimuli take place at each level of biological organization from cells to organisms. Traditional studies emphasize the impact of genes by directly linking DNA-based markers with static phenotypic values. Functional mapping, derived to detect genes that control developmental processes using growth equations, has proven powerful for addressing questions about the roles of genes in development. By treating phenotypic formation as a cohesive system using differential equations, a different approach—systems mapping—dissects the system into interconnected elements and then map genes that determine a web of interactions among these elements, facilitating our understanding of the genetic machineries for phenotypic development. Here, we argue that genetic mapping can play a more important role in studying the genotype–phenotype relationship by filling the gaps in the biochemical and regulatory process from DNA to end-point phenotype. We describe a new framework, named network mapping, to study the genetic architecture of complex traits by integrating the regulatory networks that cause a high-order phenotype. Network mapping makes use of a system of differential equations to quantify the rule by which transcriptional, proteomic and metabolomic components interact with each other to organize into a functional whole. The synthesis of functional mapping, systems mapping and network mapping provides a novel avenue to decipher a comprehensive picture of the genetic landscape of complex phenotypes that underlie economically and biomedically important traits.
    Print ISSN: 1467-5463
    Digitale ISSN: 1477-4054
    Thema: Biologie , Informatik
    Publiziert von Oxford University Press
    Standort Signatur Erwartet Verfügbarkeit
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  • 7
    Publikationsdatum: 2014-01-22
    Beschreibung: Traditional approaches for genetic mapping are to simply associate the genotypes of a quantitative trait locus (QTL) with the phenotypic variation of a complex trait. A more mechanistic strategy has emerged to dissect the trait phenotype into its structural components and map specific QTLs that control the mechanistic and structural formation of a complex trait. We describe and assess such a strategy, called structural mapping, by integrating the internal structural basis of trait formation into a QTL mapping framework. Electrical impedance spectroscopy (EIS) has been instrumental for describing the structural components of a phenotypic trait and their interactions. By building robust mathematical models on circuit EIS data and embedding these models within a mixture model-based likelihood for QTL mapping, structural mapping implements the EM algorithm to obtain maximum likelihood estimates of QTL genotype-specific EIS parameters. The uniqueness of structural mapping is to make it possible to test a number of hypotheses about the pattern of the genetic control of structural components. We validated structural mapping by analyzing an EIS data collected for QTL mapping of frost hardiness in a controlled cross of jujube trees. The statistical properties of parameter estimates were examined by simulation studies. Structural mapping can be a powerful alternative for genetic mapping of complex traits by taking account into the biological and physical mechanisms underlying their formation.
    Print ISSN: 1467-5463
    Digitale ISSN: 1477-4054
    Thema: Biologie , Informatik
    Publiziert von Oxford University Press
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  • 8
    Publikationsdatum: 2014-09-24
    Beschreibung: The stochastic modelling of biological systems, coupled with Monte Carlo simulation of models, is an increasingly popular technique in bioinformatics. The simulation-analysis workflow may result computationally expensive reducing the interactivity required in the model tuning. In this work, we advocate the high-level software design as a vehicle for building efficient and portable parallel simulators for the cloud. In particular, the Calculus of Wrapped Components (CWC) simulator for systems biology, which is designed according to the FastFlow pattern-based approach, is presented and discussed. Thanks to the FastFlow framework, the CWC simulator is designed as a high-level workflow that can simulate CWC models, merge simulation results and statistically analyse them in a single parallel workflow in the cloud. To improve interactivity, successive phases are pipelined in such a way that the workflow begins to output a stream of analysis results immediately after simulation is started. Performance and effectiveness of the CWC simulator are validated on the Amazon Elastic Compute Cloud.
    Print ISSN: 1467-5463
    Digitale ISSN: 1477-4054
    Thema: Biologie , Informatik
    Publiziert von Oxford University Press
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  • 9
    facet.materialart.
    Unbekannt
    Oxford University Press
    Publikationsdatum: 2014-09-24
    Beschreibung: Protein phosphorylation is one of the most pervasive post-translational modifications, regulating diverse cellular processes in various organisms. As mass spectrometry-based experimental approaches for identifying phosphorylation events are resource-intensive, many computational methods have been proposed, in which phosphorylation site prediction is formulated as a classification problem. They differ in several ways, and one crucial issue is the construction of training data and test data for unbiased performance evaluation. In this article, we categorize the existing data construction methods and try to answer three questions: (i) Is it equivalent to use different data construction methods in the assessment of phosphorylation site prediction algorithms? (ii) What kind of test data set is unbiased for assessing the prediction performance of a trained algorithm in different real world scenarios? (iii) Among the summarized training data construction methods, which one(s) has better generalization performance for most scenarios? To answer these questions, we conduct comprehensive experimental studies for both non-kinase-specific and kinase-specific prediction tasks. The experimental results show that: (i) different data construction methods can lead to significantly different prediction performance; (ii) there can be different test data construction methods that are unbiased with respect to different real world scenarios; and (iii) different data construction methods have different generalization performance in different real world scenarios. Therefore, when developing new algorithms in future research, people should concentrate on what kind of scenario their algorithm will work for, what the corresponding unbiased test data are and which training data construction method can generate best generalization performance.
    Print ISSN: 1467-5463
    Digitale ISSN: 1477-4054
    Thema: Biologie , Informatik
    Publiziert von Oxford University Press
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  • 10
    Publikationsdatum: 2014-09-24
    Beschreibung: With the rapid development of biotechnologies, many types of biological data including molecular networks are now available. However, to obtain a more complete understanding of a biological system, the integration of molecular networks with other data, such as molecular sequences, protein domains and gene expression profiles, is needed. A key to the use of networks in biological studies is the definition of similarity among proteins over the networks. Here, we review applications of similarity measures over networks with a special focus on the following four problems: (i) predicting protein functions, (ii) prioritizing genes related to a phenotype given a set of seed genes that have been shown to be related to the phenotype, (iii) prioritizing genes related to a phenotype by integrating gene expression profiles and networks and (iv) identification of false positives and false negatives from RNAi experiments. Diffusion kernels are demonstrated to give superior performance in all these tasks, leading to the suggestion that diffusion kernels should be the primary choice for a network similarity metric over other similarity measures such as direct neighbors and shortest path distance.
    Print ISSN: 1467-5463
    Digitale ISSN: 1477-4054
    Thema: Biologie , Informatik
    Publiziert von Oxford University Press
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