1,721,003 research outputs found
Going Beyond Counting First Authors in Author Co-citation Analysis
Get PDFThe 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
Data integration strategies for bioinformatics with applications in biomarker and network discovery
No full textNowadays, with large amounts of data becoming available, solving biological quests is becoming more and more a data-driven activity. To support this, there is a need for tools that enable the integration of the many sources of data. This thesis presents several avenues that can be taken, showing how integration can support research in the life sciences. Biological data can be integrated at several levels. We present a categorization of these different strategies within the context of the Data-Information-Knowledge-Wisdom paradigm. We argue that bioinformatics research should not only concentrate on the individual levels, but also on the transitions between these domains. Throughout the thesis we present possible solutions for a number of these different transformations. One of these transformations bridges the gap that exists between the two lowest levels of data integration: data representations (e.g. databases) and data analysis (e.g. pattern recognition, statistical analysis). The wealth of different data sources, each describing a different aspect of the molecular system (such as gene expressions, locations on genome, physical binding partners etc.), have driven data representation approaches towards flat and flexible formats. In contrast, data analysis prefers structured multi-dimensional array-based data formats. We argue that this gap cannot be closed, but rather needs to be bridged through the use of novel query systems. As a solution, we introduce the tool IBIDAS, which allows one to easily handle not only tables, but also more complexly structured data, making it a flexible tool for the exploration and analysis of data. Another question concerns at what point different data sources need to be integrated. One strategy (`late' integration) is to analyse each data source separately, after which the results are integrated. This strategy however prevents the discovery of connections that transcend the individual data sources. The alternative `early' integration strategy, in which the data is first concatenated (i.e. as feature vector) before analysis, is however not always feasible when complex data types, such as DNA sequences, need to be taken into account. We advocate an `intermediate' data integration approach, in which each data source is first transformed into a suitable “kernel space”. In this space, data can be integrated in a straightforward manner. This can even be done in a non-linear fashion, after which the data can be analyzed together. We show the strength of such “kernel method” when combining data sources to predict interacting proteins. When combining similar data, we emphasize that one should consider this as a data integration problem too, instead of just concatenating the data. As an example, batch-effects can seriously affect the data distributions of gene expression experiments. These effects need to be resolved when analyzing these experiments jointly. One way to solve this is to normalize data before it is joined. We show that it is necessary to take into account as much information as possible about the way in which the data is created into a normalization scheme. By modeling the effects that deteriorate your data, seemingly uninformative data sets can become again a rich source of information. As an example we applied this to data sets that study the relationship between the transcriptome of stem cells and the effectivity of these cells in bone regeneration. Instead of integrating data for a single problem, one can also integrate data for a class of problems. We show that the machine learning concept can elegantly solve such integration problems. By making use of the similarities between the problem domains, learning parameters can be restricted. This approach was applied in the analysis of 'materiomics' data for the new TopoChip platform. Measurements that characterized the reactions of cells to individual material surfaces were noisy, making it difficult to adequately compare these surface effects. However, by taking into account the similarities between surfaces, and by integrating data across these similar surfaces, results were improved significantly. As an encompassing example of data integration we finally show how a combination of integration methods can be put together to link two other integration levels: pattern recognition and causal model inference. In this example, numerous data sources are being used to predict cause-effect relationships between genes in perturbation experiments. The used data sources describe various aspects of proteins, protein-protein interactions and protein-DNA interactions. These descriptions of the physical components of a cell are related to cause-effect interactions between the genes, in such a way that data from perturbation experiments is explained. We combine kernel-based integration methods with a method that constructs a causal model, showing that cause-effect relationships can be accurately predicted. Taken together, this thesis explores several data integration levels and approaches. Given the complexity of biology, we believe that data integration will become more and more essential in bioinformatics and that this dissertation only has set the first steps on this road.Intelligent SystemsElectrical Engineering, Mathematics and Computer Scienc
Variations on the Author
Get PDF“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
Get PDFWe 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
Epigenetic data analysis on Alzheimer's disease
No full textMutations in ones DNA can influence the risk for developing Alzheimer's disease (Alzheimer). DNA consists of long strings of nucleotides, which together define the genome. However, next to these nucleotides, also the way in which DNA wraps in a cell affects the function of a cell. The information describing the DNA wrapping state is called the epigenome. A recent paper by Kundaje et al. [1] has provided, among other things, an epigenetic label (state) to each nucleotide. One type of mutations on the DNA affects a single nucleotide; these are called SNPs. Some SNPs are related to Alzheimer whereas others are related to other diseases or traits such as hair color. In this report, we have investigated if Alzheimer SNPs are related to specific epigenetic states. Specifically, we searched for patterns to distinguish Alzheimer SNPs from other SNPs. For this, we explored numerous similarity approaches, such as differences in state counts, clustering methods, and a classifier. Up till now, we did not find an obvious difference in epigenetic patterns between Alzheimer SNPs and control SNPs.Pattern Recognition and BioinformaticsIntelligent SystemsElectrical Engineering, Mathematics and Computer Scienc
Dispelling the Myths Behind First-author Citation Counts
Get PDFWe 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
koamabayili/VECTRON-author-checklist: VECTRON author checklist
No full textWe have done our best to complete the author checklist relating to the use of animals in the hut study. Note that the objective for the hut study was to evaluate the IRS treatment applications for residual efficacy against Anopheles mosquitoes, including the local An. coluzzii mosquito population. Cows were only used to attract mosquitoes into the huts and no tests were carried out directly on the cows. The author checklist is intended for use with studies where experiments are carried out on animals, which is why we have had such difficulty in completing this for the hut study, as many of the questions do not relate to how the cows were used
Author-wise bibliometric analysis based on entropy.
No full textAuthor-wise bibliometric analysis based on entropy.</p
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