The Experts below are selected from a list of 11583 Experts worldwide ranked by ideXlab platform
Liwen Vaughan - One of the best experts on this subject based on the ideXlab platform.
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co occurrence matrices and their applications in information science extending aca to the web environment
arXiv: Information Retrieval, 2009Co-Authors: Loet Leydesdorff, Liwen VaughanAbstract:Co-occurrence matrices, such as co-citation, co-word, and co-link matrices, have been used widely in the information sciences. However, confusion and controversy have hindered the proper statistical analysis of this data. The underlying problem, in our opinion, involved understanding the nature of various types of matrices. This paper discusses the difference between a symmetrical co-citation Matrix and an asymmetrical citation Matrix as well as the appropriate statistical techniques that can be applied to each of these matrices, respectively. Similarity measures (like the Pearson correlation coefficient or the cosine) should not be applied to the symmetrical co-citation Matrix, but can be applied to the asymmetrical citation Matrix to derive the Proximity Matrix. The argument is illustrated with examples. The study then extends the application of co-occurrence matrices to the Web environment where the nature of the available data and thus data collection methods are different from those of traditional databases such as the Science Citation Index. A set of data collected with the Google Scholar search engine is analyzed using both the traditional methods of multivariate analysis and the new visualization software Pajek that is based on social network analysis and graph theory.
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co occurrence matrices and their applications in information science extending aca to the web environment
Journal of the Association for Information Science and Technology, 2006Co-Authors: Loet Leydesdorff, Liwen VaughanAbstract:Co-occurrence matrices, such as cocitation, coword, and colink matrices, have been used widely in the information sciences. However, confusion and controversy have hindered the proper statistical analysis of these data. The underlying problem, in our opinion, involved understanding the nature of various types of matrices. This article discusses the difference between a symmetrical cocitation Matrix and an asymmetrical citation Matrix as well as the appropriate statistical techniques that can be applied to each of these matrices, respectively. Similarity measures (such as the Pearson correlation coefficient or the cosine) should not be applied to the symmetrical cocitation Matrix but can be applied to the asymmetrical citation Matrix to derive the Proximity Matrix. The argument is illustrated with examples. The study then extends the application of co-occurrence matrices to the Web environment, in which the nature of the available data and thus data collection methods are different from those of traditional databases such as the Science Citation Index. A set of data collected with the Google Scholar search engine is analyzed by using both the traditional methods of multivariate analysis and the new visualization software Pajek, which is based on social network analysis and graph theory. © 2006 Wiley Periodicals, Inc.
Kamaldeep Bhui - One of the best experts on this subject based on the ideXlab platform.
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the barts explanatory model inventory for dementia an item reduction approach based on responses from south asian communities
International Journal of Geriatric Psychiatry, 2020Co-Authors: Eleni Kampanellou, Mark Wilberforce, Angela Worden, Clarissa M Giebel, David Challis, Kamaldeep BhuiAbstract:BACKGROUND: Cultural differences in how the symptoms, causes, consequences, and treatments of dementia are understood and interpreted by South Asian people are a commonly expressed reason for late- or nonuse of mental health and care services. However, systematic collection of information on South Asian perceptions of dementia is hindered by a lack of appropriate instrumentation. OBJECTIVES: To produce a shortened version of the Barts Explanatory Model Inventory for Dementia (BEMI-D) schedule. METHODS: A two stage item reduction approach was employed first using multidimensional scaling categorizing items as core, intermediate, or outlier. Then, item review was undertaken using three criteria: literature importance, clinical face validity, and sub-group prevalence. The analysis followed a nonmetric multidimensional scaling method based on a two-way Proximity Matrix. RESULTS: The original BEMI-D had 197 items allocated to four checklists: symptoms, causes, consequences and treatments. The two stage item reduction approach resulted in the removal of 75 items. These reductions were achieved across all four checklists in relatively equal proportions. There was no evidence of substantive content loss in the revised schedule. The reduced version of the schedule comprises 122 items. CONCLUSIONS: A condensed version of the BEMI-D is more efficient as an assessment schedule that captures the culturally diverse perceptions of memory problems for South Asians offering a balanced trade-off between feasibility of use and content validity. This article is protected by copyright. All rights reserved.
Loet Leydesdorff - One of the best experts on this subject based on the ideXlab platform.
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co occurrence matrices and their applications in information science extending aca to the web environment
arXiv: Information Retrieval, 2009Co-Authors: Loet Leydesdorff, Liwen VaughanAbstract:Co-occurrence matrices, such as co-citation, co-word, and co-link matrices, have been used widely in the information sciences. However, confusion and controversy have hindered the proper statistical analysis of this data. The underlying problem, in our opinion, involved understanding the nature of various types of matrices. This paper discusses the difference between a symmetrical co-citation Matrix and an asymmetrical citation Matrix as well as the appropriate statistical techniques that can be applied to each of these matrices, respectively. Similarity measures (like the Pearson correlation coefficient or the cosine) should not be applied to the symmetrical co-citation Matrix, but can be applied to the asymmetrical citation Matrix to derive the Proximity Matrix. The argument is illustrated with examples. The study then extends the application of co-occurrence matrices to the Web environment where the nature of the available data and thus data collection methods are different from those of traditional databases such as the Science Citation Index. A set of data collected with the Google Scholar search engine is analyzed using both the traditional methods of multivariate analysis and the new visualization software Pajek that is based on social network analysis and graph theory.
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co occurrence matrices and their applications in information science extending aca to the web environment
Journal of the Association for Information Science and Technology, 2006Co-Authors: Loet Leydesdorff, Liwen VaughanAbstract:Co-occurrence matrices, such as cocitation, coword, and colink matrices, have been used widely in the information sciences. However, confusion and controversy have hindered the proper statistical analysis of these data. The underlying problem, in our opinion, involved understanding the nature of various types of matrices. This article discusses the difference between a symmetrical cocitation Matrix and an asymmetrical citation Matrix as well as the appropriate statistical techniques that can be applied to each of these matrices, respectively. Similarity measures (such as the Pearson correlation coefficient or the cosine) should not be applied to the symmetrical cocitation Matrix but can be applied to the asymmetrical citation Matrix to derive the Proximity Matrix. The argument is illustrated with examples. The study then extends the application of co-occurrence matrices to the Web environment, in which the nature of the available data and thus data collection methods are different from those of traditional databases such as the Science Citation Index. A set of data collected with the Google Scholar search engine is analyzed by using both the traditional methods of multivariate analysis and the new visualization software Pajek, which is based on social network analysis and graph theory. © 2006 Wiley Periodicals, Inc.
Tom R. Gaunt - One of the best experts on this subject based on the ideXlab platform.
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using a random forest Proximity measure for variable importance stratification in genotypic data
IWBBIO, 2014Co-Authors: Jose Antonio Seoane, Colin Campbell, Juan P Casas, Tom R. GauntAbstract:In this work we study variable-significance in classification using the Random Forest Proximity Matrix and local Importance Matrix. We use the prox- imity m atrix t o g roup t he s amples acr oss a num ber of c lusters a nd use t hese clusters to s tratify th e importance of a v ariable. W e apply t his a pproach t o a cardiovascular g enotype d ataset f or sample classification b ased o n coronary heart disease and we found a number of variations related with cardiovascular disease phenotypes. We also used a set of phenotypes related with this genotype data to match the obtained clusters with coronary heart diseases phenotypes.
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a random forest Proximity Matrix as a new measure for gene annotation
The European Symposium on Artificial Neural Networks, 2014Co-Authors: Jose Antonio Seoane, Colin Campbell, Juan P Casas, Tom R. GauntAbstract:In this paper we present a new score for gene annotation. This new score is based on the Proximity Matrix obtained from a trained Random Forest (RF) model. As an example application, we built this model using the association p- values of genotype with blood phenotype as input and the association of genotype data with coronary heart disease as output. This new score has been validated by comparing the Gene Ontology (GO) annotation using this score versus the score obtained from the gene annotation "String" tool. Using the new Proximity based measure results in more accurate annotation, especially in the GO categories Molecular Function and Biological Process.
Eleni Kampanellou - One of the best experts on this subject based on the ideXlab platform.
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the barts explanatory model inventory for dementia an item reduction approach based on responses from south asian communities
International Journal of Geriatric Psychiatry, 2020Co-Authors: Eleni Kampanellou, Mark Wilberforce, Angela Worden, Clarissa M Giebel, David Challis, Kamaldeep BhuiAbstract:BACKGROUND: Cultural differences in how the symptoms, causes, consequences, and treatments of dementia are understood and interpreted by South Asian people are a commonly expressed reason for late- or nonuse of mental health and care services. However, systematic collection of information on South Asian perceptions of dementia is hindered by a lack of appropriate instrumentation. OBJECTIVES: To produce a shortened version of the Barts Explanatory Model Inventory for Dementia (BEMI-D) schedule. METHODS: A two stage item reduction approach was employed first using multidimensional scaling categorizing items as core, intermediate, or outlier. Then, item review was undertaken using three criteria: literature importance, clinical face validity, and sub-group prevalence. The analysis followed a nonmetric multidimensional scaling method based on a two-way Proximity Matrix. RESULTS: The original BEMI-D had 197 items allocated to four checklists: symptoms, causes, consequences and treatments. The two stage item reduction approach resulted in the removal of 75 items. These reductions were achieved across all four checklists in relatively equal proportions. There was no evidence of substantive content loss in the revised schedule. The reduced version of the schedule comprises 122 items. CONCLUSIONS: A condensed version of the BEMI-D is more efficient as an assessment schedule that captures the culturally diverse perceptions of memory problems for South Asians offering a balanced trade-off between feasibility of use and content validity. This article is protected by copyright. All rights reserved.
