1,720,973 research outputs found
Mathematical problems for miscible, incompressible fluids with Korteweg stresses
No full textGaldi, P.; Joseph, D.D.; Preziosi, L.; Rionero, S.. (1990). Mathematical problems for miscible, incompressible fluids with Korteweg stresses. Retrieved from the University Digital Conservancy, https://hdl.handle.net/11299/1435
Multiview learning in biomedical applications
No full textMotivation In the era of big data, the richness and variety of available datasets have opened new horizons for investigators in the biomedical field. The ultimate challenge consists in building an integrated base of knowledge derived from heterogeneous sources. Multiview learning is the branch of machine learning concerned with the analysis of multimodal data, that is, patterns represented by different sets of features extracted from multiple data sources. In recent years, multiview learning methodologies have become increasingly popular, and a high number of biomedical applications based on multiview data have been recorded in literature. For example, in bioinformatics, analyses can be based on multiple experiments investigating different facets of the same phenomena, such as gene expression, microRNA expression, protein-protein interactions, genome-wide association, and so on, in order to capture information regarding different aspects of biological systems. In the same way, neuroscience data analysis can benefit from different imaging modalities that allow to study different features of the nervous system (e.g., structural vs. functional organization). Compared to the limited perspective offered by single-view analyses, the integration of multiple views can provide a deeper understanding of the underlying principles governing complex systems. Results In this work, we review the existing multiview methodologies to discuss their operation modes and principles, with the goal of increasing their further development in the biomedical field. We organize the described methods in three categories, according to the type of data, the statistical problem, and the type of integration. This discussion, which highlights the advantages and disadvantages of different schools of thought, is intended to be a reference for those who want to start working with the integration of biomedical data. We selected a number of representative examples in bioinformatics and neuroinformatics to show the potential of multiview learning applications to cutting-edge research problems. First, we explain how multiview clustering can be used to perform patient subtyping in order to identify groups of patients who share similar molecular characteristics and possibly similar reactions to treatment. Then, the drug-repositioning problem is introduced and a discussion of the multiview classification methods used in literature is provided. We then describe an example of how both clustering and classification can be combined in a multiview setting for the automated diagnosis of neurodegenerative disorders, and we explain how multiple noninvasive imaging modalities can be exploited together to obtain more accurate brain parcellations. Finally, we discuss how deep learning techniques that are getting more and more recognition in various fields can be applied to multimodal data to learn complex representations, and we present few examples of this application
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
Multiview learning in biomedical applications
No full textMotivation: In the era of big data, the richness and variety of available datasets have opened new horizons for investigators in the biomedical field. The ultimate challenge consists in building an integrated base of knowledge derived from heterogeneous sources. Multiview learning is the branch of machine learning concerned with the analysis of multimodal data, i.e., patterns represented by different sets of features extracted from multiple data sources. In recent years, multiview learning methodologies have become increasingly popular, and a high number of biomedical applications based on multiview data have been recorded in the literature. For example, in bioinformatics, analyses can be based on multiple experiments investigating different facets of the same phenomena, such as gene expression, microRNA expression, protein-protein interactions, genome-wide association, and so on, to capture information regarding different aspects of biological systems. In the same way, neuroscience data analysis can benefit from different imaging modalities that allow to study different features of the nervous system (e.g., structural vs functional organization). Compared to the limited perspective offered by single-view analyses, the integration of multiple views can provide a deeper understanding of the underlying principles governing complex systems. Results: In this work, we review the existing multiview methodologies to discuss their operation modes and principles, with the goal of increasing their further development in the biomedical field. We organized the described methods in three categories, according to the type of data, the statistical problem, and the type of integration. This discussion, which highlights the advantages and disadvantages of different schools of thought, is intended to be a reference for those who want to start working with the integration of biomedical data. We selected a number of representative examples in bioinformatics and neuroinformatics to show the potential of multiview learning applications for cutting-edge research problems. First, we explain how multiview clustering can be used to perform patient subtyping to identify groups of patients that share similar molecular characteristics and possibly similar reactions to treatment. Then, the drug-repositioning problem is introduced and a discussion of the multiview classification methods used in the literature is provided. We then describe an example of how both clustering and classification can be combined in a multiview setting for the automated diagnosis of neurodegenerative disorders and we explain how multiple noninvasive imaging modalities can be exploited together to obtain more accurate brain parcellations. We additionally introduce the emerging fields of single-cell multiomics data analysis and brain imaging genomics. Finally, we discuss how deep learning techniques, which are getting more and more recognition in various fields, can be applied to multimodal data to learn complex representations, and we present a few examples of application
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
Data Mining: Accuracy and Error Measures for Classification and Prediction
No full textA variety of measures exist to assess the accuracy of predictive models in data mining and several aspects should be considered when evaluating the performance of learning algorithms. In this article, the most common accuracy and error scores for classification and regression are reviewed and compared. Moreover, the standard approaches to model selection and assessment are presented, together with an introduction to ensemble methods for improving the accuracy of single classifiers
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