1,720,976 research outputs found
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
Automatic analysis of flow cytometry data and its application to lymphoma diagnosis
No full textFlow cytometry has many applications in clinical medicine and biological research. For many modern applications, traditional methods of manual data interpretation are not efficient due to the large amount of complex, high dimensional data.
In this thesis, I discuss some of the important challenges towards automatic analysis of flow cytometry data and propose my solutions. To validate my approach on addressing real life problems, I developed an automatic pipeline for analyzing flow cytometry data and applied it to clinical data. My pipeline can potentially be useful for improving quality check on diagnosis, assisting discovery of novel phenotypes, and making clinical recommendations.
Furthermore, some of the challenges that I studied are rooted in more general areas of computer science, and therefore, the tools and techniques that I developed can be applied to a wider range of problems in data mining and machine learning. Enhancement to spectral clustering algorithm and proposing a novel scheme for scoring features are two examples of my contributions to computer science that were developed as part of this thesis.Science, Faculty ofComputer Science, Department ofGraduat
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
Combining Deep Learning with Traditional Machine Learning to Improve Classification Accuracy on Small Datasets
No full textFeature extraction and selection are essential phases in building machine learning classification models, and they have a great impact on the accuracy and the performance of the model. However, these phases are expensive, and there is no guarantee that manually extracted features will generalize well in different data modalities. Deep learning models integrate the phases of feature extraction, selection, and classification into a single optimization process. However, they are very computationally expensive compared to traditional machine learning algorithms, and they require large training datasets to achieve good classification performance.
This work explores ways of combining the advantages of deep learning and traditional machine learning models by building a hybrid classification scheme. The first few layers of a convolutional neural network are utilized for feature extraction and selection. Subsequently, the extracted features are fed to a traditional supervised learning algorithm to perform classification. We evaluate our method on sensor data coming from human physiological biosignal measurements and motion tracking data coming from accelerometers. Our experimental results show that our hybrid approach outperforms deep learning and traditional machine learning algorithms when those are used in isolation on small dataset.Computer Scienc
Identification of Gene Sets that Predict Acute Myeloid Leukemia prognosis using integrative gene network analysis
No full textOrthogonal data types can potentially provide new opportunities to pinpoint the underlying molecular mechanisms of diseases. However, currently-available techniques to capitalize on information from different data types suffer from a substantial loss of statistical power. Therefore, there is urgent need to develop algorithms to integrate data types. In this thesis, I have developed a data integration approach based on multi-view clustering. I demonstrate the usefulness of my approach in prognostication of Acute Myeloid Leukemia (AML), a particular type of blood cancer. AML accounts for 1.2% of cancer deaths per year in the USA. AML patients are categorized into low, medium and high-risk groups. The variable survival rate for medium-risk patients leads to difficulties in deciding on the appropriate treatment for these patients. Current methods of prognostication of AML use only gene expression, mutations and molecular cytogenetic abnormalities. However, the DNA methylation data, which have valuable information that would be useful for prognostication, have not yet been effectively used in the existing clinical tests. In this project, I have used The Cancer Genome Atlas (TCGA) dataset and developed a method that analyzes both gene expression and DNA methylation data in a single model using network analysis. The model based on this methodology correctly classified 13 out of 90 patients as high-risk, whereas they were previously labeled as medium-risk using current clinical methods. All 13 of these cases died within two years after diagnosis. To validate these results, I tested the method using an independent dataset. The model labeled 11 out of 228 patients as high-risk, whereas they were previously labeled as medium-risk based on the European Leukemia Net (ELN) 2010 criteria. All 11 patients died within two years of diagnosis, and their risk group is not predictable with other currently used methods.Computer Scienc
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
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