Journal Article10.1080/02664763.2011.578620
Clustering time-course microarray data using functional Bayesian infinite mixture model
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TL;DR: This paper presents a new Bayesian, infinite mixture model based, clustering approach, specifically designed for time-course microarray data, which is studied using synthetic and real micro array data and is compared with the performances of competitive techniques.
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Abstract: This paper presents a new Bayesian, infinite mixture model based, clustering approach, specifically designed for time-course microarray data. The problem is to group together genes which have “similar” expression profiles, given the set of noisy measurements of their expression levels over a specific time interval. In order to capture temporal variations of each curve, a non-parametric regression approach is used. Each expression profile is expanded over a set of basis functions and the sets of coefficients of each curve are subsequently modeled through a Bayesian infinite mixture of Gaussian distributions. Therefore, the task of finding clusters of genes with similar expression profiles is then reduced to the problem of grouping together genes whose coefficients are sampled from the same distribution in the mixture. Dirichlet processes prior is naturally employed in such kinds of models, since it allows one to deal automatically with the uncertainty about the number of clusters. The posterior inference i...
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Functional Data Analysis
TL;DR: In this article, the authors provide an overview of FDA, starting with simple statistical notions such as mean and covariance functions, then covering some core techniques, the most popular of which is functional principal component analysis (FPCA).
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Review of Functional Data Analysis
TL;DR: An overview of FDA is provided, starting with simple statistical notions such as mean and covariance functions, then covering some core techniques, the most popular of which is Functional Principal Component Analysis (FPCA), an important dimension reduction tool and in sparse data situations can be used to impute functional data that are sparsely observed.
Learning the evolution of disciplines from scientific literature: A functional clustering approach to normalized keyword count trajectories
Matilde Trevisani,Arjuna Tuzzi +1 more
TL;DR: This study proposes a methodology consisting of a stepwise information retrieval procedure for keywords’ selection and a functional clustering two-stage approach for statistical learning, showing that the different concept of curve similarity induced in clustering by the type of transformation heavily affects groups’ composition and size.
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Parameter clustering in Bayesian functional principal component analysis of neuroscientific data.
TL;DR: A new model called parameter clustering functional principal component analysis (PCl‐fPCA) is proposed that merges ideas from functional data analysis and Bayesian nonparametrics to obtain a flexible and computationally feasible signal reconstruction and exploration of spatiotemporal neuroscientific data.
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Introduction: Tracing the History of a Discipline Through Quantitative and Qualitative Analyses of Scientific Literature
Arjuna Tuzzi
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TL;DR: In this article, the authors studied the temporal evolution of word occurrences in papers published by scientific journals and identified the main subject matters that were considered relevant by mainstream journals and offered new viewpoints to read and understand the evolution of a discipline.
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