1,721,043 research outputs found
Correlation analysis
Full text linkCorrelation analysis is a statistical method used to evaluate the strength of relationship between two quantitative variables. A high correlation means that two or more variables have a strong relationship with each other, while a weak correlation means that the variables are hardly related. In other words, it is the process of studying the strength of that relationship with available statistical data. This technique is strictly connected to the linear regression analysis that is a statistical approach for modeling the association between a dependent variable, called response, and one or more explanatory or independent variables. The aim of this work is to provide a general overview of correlation analysis in order to apply it to biomedical applications
Hidden markov models
Full text linkWe introduce the theory of Hidden Markov Models, with a brief historical description, and we describe some computational biology. In particular, we describe the theoretical basics of these methods with particular attention to the three fundamental statistical problems and summarize, striking applications of hidden Markov models to computational biological studies. HMMs is a probabilistic framework for modelling and analyzing epigenetic studies; they are frequently used for modelling biological sequences, for example, in gene finding, profile searches, multiple sequence alignment and regulatory site identification. For this purpose, in particular, we contribute to give a general understanding of the nature and relevance of these probabilistic methods, describing some simple examples, with particular focus on the problem of CpG islands finding
Descriptive statistics
Full text linkWe describe a set of descriptive procedures and statistical measures to show how carry out an accurate statistical investigation. We detect two different types of approaches: graphical and numerical. The first approach contains information about the distribution of the data including tables, used to organize the collected information, and graphs, drawn to visualize the trend of the data. The second one consists of several statistical measures that give a summary of the data for the statistical units in a specific group. Finally, an example of clinical data is presented to provide an accurate understanding of the nature and relevance of descriptive statistical methods into the biomedical investigations
Introduction to biostatistics
Full text linkBiostatistics can be defined as the application of the mathematical tools used in statistics to the fields of biological sciences and medicine. Here, we provide a brief introduction to selected important topics in biostatistics. Specific applications include tools for describing statistical measures to summarize data and methods for performing inference on population parameters to reach decisions about population when only a part of the data is observed (sample). In particular, we introduce estimation and hypothesis testing for parametric and non-parametric tests. Finally, illustrative examples are given to show how biostatistics can improve our biological knowledge
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
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
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