1,720,956 research outputs found
Mining of Biological Data II: Assessing Data Structure and Class Homogeneity by Cluster Analysis
An important step in data analysis is class assignment which isusually done on the basis of a macroscopic phenotypic or bioprocesscharacteristic, such as high vs low growth, healthy vs diseased state,or high vs low productivity. Unfortunately, such an assignment maylump together samples, which when derived from a more detailedphenotypic or bioprocess description are dissimilar, giving rise tomodels of lower quality and predictive power. In this paper we pre-sent a clustering algorithm for data preprocessing which involves theidentification of fundamentally similar lots on the basis of the extentof similarity among the system variables. The algorithm combinesaspects of cluster analysis and principal component analysis byapplying agglomerative clustering methods to the first principalcomponent of the system data matrix. As part of a rational strategyfor developing empirical models, this technique selects lots (sam-ples) which are most appropriate for inclusion in a training set byanalyzing multivariate data homogeneity. Samples with similar datastructures are identified and grouped together into distinct clusters.This knowledge is used in the formation of potential training sets.Additionally, this technique can identify atypical lots, i.e., samplesthat are not simply outliers but exhibit the general properties of oneclass but have been given the assignment of the other. The method ispresented along with examples from its application to fermentationdata sets
Mining genomic expression data
Data mining techniques for tha analysis of cellular behavior are presented
Mining of Biological Data I: Identifying Discriminating Features via Mean Hypothesis Testing
Large volumes of data are routinely collected during bioprocessoperations and, more recently, in basic biological research usinggenomics-based technologies. While these data often lack sufficientdetail to be used for mechanism identification, it is possible that theunderlying mechanisms affecting cell phenotype or process outcomeare reflected as specific patterns in the overall or temporal sensorlogs. This raises the possibility of identifying outcome-specificfingerprints that can be used for process or phenotype classificationand the identification of discriminating characteristics, such asspecific genes or process variables. The aim of this work is to providea systematic approach to identifying and modeling patterns inhistorical records and using this information for process classifica-tion. This approach differs from others in that emphasis is placed onanalyzing the data structure first and thereby extracting potentiallyrelevant features prior to model creation. The initial step in this over-all approach is to first identify the discriminating features of the rele-vant measurements and time windows, which can then be subse-quently used to discriminate among different classes of processbehavior. This is achieved via a mean hypothesis testing algorithm.Next, the homogeneity of the multivariate data in each class isexplored via a novel cluster analysis technique called PC1 TimeSeries Clustering to ensure that the data subsets used accuratelyreflect the variability displayed in the historical records. This will bethe topic of the second paper in this series. We present here themethod for identifying discriminating features in data via meanhypothesis testing along with results from the analysis of case studiesfrom industrial fermentations
Going Beyond Counting First Authors in Author Co-citation Analysis
The 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
“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
We 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
We 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
We 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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