1,721,071 research outputs found
miRNA Alterations Elicit Pathways Involved in Memory Decline and Synaptic Function in the Hippocampus of Aged Tg4-42 Mice
Full text linkThe transcriptome of non-coding RNA (ncRNA) species is increasingly focused in Alzheimer’s disease (AD) research. NcRNAs comprise, among others, transfer RNAs, long non-coding RNAs and microRNAs (miRs), each with their own specific biological function. We used smallRNASeq to assess miR expression in the hippocampus of young (3 month old) and aged (8 month old) Tg4-42 mice, a model system for sporadic AD, as well as age-matched wildtype controls. Tg4-42 mice express N-truncated Aβ4–42, develop age-related neuron loss, reduced neurogenesis and behavioral deficits. Our results do not only confirm known miR-AD associations in Tg4-42 mice, but more importantly pinpoint 22 additional miRs associated to the disease. Twenty-five miRs were differentially expressed in both aged Tg4-42 and aged wildtype mice while eight miRs were differentially expressed only in aged wildtype mice, and 33 only in aged Tg4-42 mice. No significant alteration in the miRNome was detected in young mice, which indicates that the changes observed in aged mice are down-stream effects of Aβ-induced pathology in the Tg4-42 mouse model for AD. Targets of those miRs were predicted using miRWalk. For miRs that were differentially expressed only in the Tg4-42 model, 128 targets could be identified, whereas 18 genes were targeted by miRs only differentially expressed in wildtype mice and 85 genes were targeted by miRs differentially expressed in both mouse models. Genes targeted by differentially expressed miRs in the Tg4-42 model were enriched for negative regulation of long-term synaptic potentiation, learning or memory, regulation of trans-synaptic signaling and modulation of chemical synaptic transmission obtained. This untargeted miR sequencing approach supports previous reports on the Tg4-42 mice as a valuable model for AD. Furthermore, it revealed miRs involved in AD, which can serve as biomarkers or therapeutic targets
Netzwerkbasierte Ansätze zum Verständnis der transkriptionellen Regulation bei Krankheiten
Full text linkGene regulatory networks (GRNs) comprise the collection of regulatory relationships between molecular regulators such as transcription factors and their target genes. These networks are important for controlling fundamental biological processes, including development metabolism, and response to environmental stimuli. The inference and analysis of GRNs can benefit both basic biological research and practical applications, including precision medicine and drug repurposing. Given the complexity and context specificity of regulatory relationships across different disease conditions, new computational methods for analyzing high-dimensional omics data are warranted. This thesis presents two new approaches for the inference and analysis of GRNs applied to gene expression data. Diseases can induce perturbations in normal gene co-expression patterns. Thus, detecting differentially co-expressed or rewired edges between disease and healthy biological states can be useful for investigating the link between specific molecular alterations and disease phenotypes. The first study presents BoostDiff (Boosted Differential trees), a tree-based ensemble algorithm for inferring differential GRNs from two conditions. BoostDiff modifies standard regression trees by building an adaptively boosted (AdaBoost) ensemble of differential trees with respect to a target condition. Each differential tree serves as the base learner and is built using a new splitting criterion called differential variance improvement. The performance of BoostDiff is demonstrated using both simulated and real-world transcriptomics datasets. BoostDiff identifies context-specific networks that are enriched with genes of known disease-relevant pathways and can complement standard differential expression analyses. The second study explores the utility of combining active module identification on protein-protein interaction networks and the prize-collecting Steiner tree algorithm on GRNs. The proposed workflow, named Transcriptional Regulator Identification using Prize-collecting Steiner trees (TRIPS), is designed to identify candidate regulators influencing disease using results from differential expression analysis and prior knowledge from biomedical databases. Systematic analyses using multiple disease-associated datasets demonstrate that TRIPS can recover relevant regulators with high precision. Furthermore, our findings reinforce the importance of performing network perturbation analyses to characterize the reliability of network-based tools. In particular, the prize-collecting Steiner tree approach implemented in TRIPS was found to better utilize the knowledge encoded in the structures of molecular networks. TRIPS can be used as a post-processing step in analyzing gene expression data to provide network-based context for prioritizing transcriptional regulators in downstream experiments. Collectively, these two studies expand the computational toolbox for deriving biologically meaningful insights from omics data and can find practical application in the field of systems medicine.Genregulatorische Netzwerke (GRN) umfassen die Gesamtheit der regulatorischen Beziehungen zwischen molekularen Regulatoren wie Transkriptionsfaktoren und ihren Zielgenen. Diese Netze sind wichtig für die Steuerung grundlegender biologischer Prozesse, einschließlich Entwicklung, Stoffwechsel und Reaktion auf Umweltreize. Die Inferenz und Analyse von GRN kann sowohl der biologischen Grundlagenforschung als auch der praktischen Anwendungen wie der Präzisionsmedizin und dem Drug-Repurposing, also der Verwendung bekannter Wirkstoffe für neue Indikationen, zugutekommen. Angesichts der Komplexität und Kontextspezifität der regulatorischen Beziehungen in verschiedenen Krankheitszuständen sind neue computergestützte Methoden zur Analyse hochdimensionaler Omics-Daten erforderlich. In dieser Arbeit werden zwei neue Ansätze für die Inferenz und Analyse von GRN vorgestellt, die auf Genexpressionsdaten basieren. Krankheiten können Störungen in normalen Koexpressionsmustern von Genen hervorrufen. Daher kann die Erkennung von differentiell ko-exprimierten Genen, oder die Änderung regulatorischer Beziehungen an sich zwischen kranken und gesunden biologischen Zuständen nützlich sein, um die Rolle spezifischer molekularer Veränderungen für Krankheitsphänotypen zu untersuchen. In der ersten Studie wird BoostDiff (Boosted Differential trees) vorgestellt, ein nicht-parametrischer baumbasierter Algorithmus zur Ableitung differentieller GRN aus zwei Zuständen. BoostDiff modifiziert Standard-Regressionsbäume, indem es ein adaptiv geboostetes (AdaBoost) Ensemble von differentiellen Bäumen in Bezug auf eine Zielbedingung (z.B. krank vs. gesund) erstellt. Jeder differentielle Baum dient als Basis-Lerner und wird unter Verwendung eines neuen Spaltungskriteriums namens differentielle Varianzverbesserung erstellt. Die Leistung von BoostDiff wird anhand von simulierten und realen Transkriptomik-Datensätzen demonstriert. BoostDiff identifiziert kontextspezifische Netzwerke, die mit Genen bekannter krankheitsrelevanter Pfade angereichert sind und bringt somit einen deutlichen Mehrwert gegenüber differentiellen Expressionsanalysen. In der zweiten Studie wird der Nutzen einer Kombination aus der Identifizierung sog. aktiver Module in Protein-Protein-Interaktionsnetzwerken und dem Price-Collecting-Steiner-Tree-Algorithmus in GRN untersucht. Der vorgeschlagene Arbeitsablauf mit dem Namen „Transcriptional Regulator Identification using Prize-collecting Steiner trees“ (TRIPS) wurde entwickelt, um Regulatoren zu identifizieren die potentiell Krankheiten beeinflussen, indem Ergebnisse aus differentiellen Expressionsanalysen und Vorwissen aus biomedizinischen Datenbanken verwendet werden. Systematische Analysen mit mehreren krankheits-spezifischen Datensätzen zeigen, dass TRIPS relevante Regulatoren mit hoher Präzision ermitteln kann. Darüber hinaus unterstreichen unsere Ergebnisse die Bedeutung der Durchführung von Netzwerk-Permutationsanalysen, um die Zuverlässigkeit netzwerkbasierter Werkzeuge bewerten zu können. Insbesondere hat sich herausgestellt, dass der in TRIPS implementierte Steiner-Baum-Ansatz das in den Strukturen der molekularen Netzwerke kodierte Wissen besser nutzt als bisherige Verfahren. TRIPS kann als Nachbearbeitungsschritt bei der Analyse von Genexpressionsdaten verwendet werden, um netzwerkbasierten Kontext für die Priorisierung von Transkriptionsregulatoren in nachgelagerten Experimenten zu liefern. In Kombination erweitern diese beiden Studien das bioinformatische Instrumentarium zur Gewinnung biologisch aussagekräftiger Erkenntnisse aus Omics-Daten. Sie können im Bereich der Systemmedizin praktische Anwendung finden
Going Beyond Counting First Authors in Author Co-citation Analysis
Full text linkThe present study examines one of the fundamental aspects of author co-citation analysis (ACA) - the way co-citation
counts are defined. Co-citation counting provides the data on which all subsequent statistical analyses and mappings
are based, and we compare ACA results based on two different types of co-citation counting - the traditional type that
only counts the first one among a cited work's authors on the one hand and a non-traditional type that takes into
account the first 5 authors of a cited work on the other hand. Results indicate that the picture produced through this non-traditional author co-citation counting contains more coherent author groups and is therefore considerably clearer. However, this picture represents fewer specialties in the research field being studied than that produced through the traditional first-author co-citation counting when the same number of top-ranked authors is selected and analyzed. Reasons for these effects are discussed
Variations on the Author
Full text link“Variations on the Author” discusses two of Eduardo Coutinho’s recent films (Um Dia na Vida, from 2010, and Últimas Conversas, posthumously released in 2015) and their contribution to the general question of documentary authorship. The director’s filmography is characterized by a consistent yet self-effacing form of authorial self-inscription: Coutinho often features as an interviewer that rather than express opinions propels discourses; an interviewer that is good at listening. This mode of self-inscription characterizes him as an author who is not expressive but who is nonetheless markedly present on the screen. In Um Dia na Vida, however, Coutinho is completely absent form the image, while Últimas Conversas, on the contrary, includes a confessional prologue that moves the director from the margins to the center of his films. This article examines the ways in which these works stand out in the filmography of a director who offers new insights into the notion of cinematic authorship
Appropriate Similarity Measures for Author Cocitation Analysis
Full text linkWe provide a number of new insights into the methodological discussion about author cocitation analysis. We first argue that the use of the Pearson correlation for measuring the similarity between authors’ cocitation profiles is not very satisfactory. We then discuss what kind of similarity measures may be used as an alternative to the Pearson correlation. We consider three similarity measures in particular. One is the well-known cosine. The other two similarity measures have not been used before in the bibliometric literature. Finally, we show by means of an example that our findings have a high practical relevance.information science;Pearson correlation;cosine;similarity measure;author cocitation analysis
Dispelling the Myths Behind First-author Citation Counts
Full text linkWe conducted a full-scale evaluative citation analysis study of scholars in the XML research field to explore just how different from each other author rankings resulting from different citation counting methods actually are, and to demonstrate the capability of emerging data and tools on the Web in supporting more realistic citation counting methods. Our results contest some common arguments for the continued
use of first-author citation counts in the evaluation of scholars, such as high correlations between author rankings by first-author citation counts and other citation
counting methods, and high costs of using more realistic citation counting methods that are not well-supported by the ISI databases. It is argued that increasingly available digital full text research papers make it possible for citation analysis studies to go beyond what the ISI databases have directly supported and to employ more
sophisticated methods
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