1,720,976 research outputs found
Algorithmic paradigms for stability-based cluster validity and model selection statistical methods, with applications to microarray data analysis.
No full textThe advent of high throughput technologies, in particular microarrays, for biological research has revived interest in clustering, resulting in a plethora of new clustering algorithms. However, model selection, i.e., the identification of the correct number of clusters in a dataset, has received relatively little attention. Indeed, although central for statistics, its difficulty is also well known. Fortunately, a few novel techniques for model selection, representing a sharp departure from previous ones in statistics, have been proposed and gained prominence for microarray data analysis. Among those, the stability-based methods are the most robust and best performing in terms of prediction, but the slowest in terms of time. It is very unfortunate that as fascinating and classic an area of statistics as model selection, with important practical applications, has received very little attention in terms of algorithmic design and engineering. In this paper, in order to partially fill this gap, we make the following contributions: (A) the first general algorithmic paradigm for stability-based methods for model selection; (B) reductions showing that all of the known methods in this class are an instance of the proposed paradigm; (C) a novel algorithmic paradigm for the class of stability-based methods for cluster validity, i.e., methods assessing how statistically significant is a given clustering solution; (D) a general algorithmic paradigm that describes heuristic and very effective speed-ups known in the literature for stability-based model selection methods.
Since the performance evaluation of model selection algorithms is mainly experimen- tal, we offer, for completeness and without even attempting to be exhaustive, a represen- tative synopsis of known experimental benchmarking results that highlight the ability of stability-based methods for model selection and the computational resources that they re- quire for the task. As a whole, the contributions of this paper generalize in several respects reference methodologies in statistics and show that algorithmic approaches can yield deep methodological insights into this area, in addition to practical computational procedures
Bayesian versus data driven model selection for microarray data
Full text linkClustering is one of the most well known activities in scientific investigation and the object of research in many disciplines, ranging from Statistics to Computer Science. In this beautiful area, one of the most difficult challenges is a particular instance of the model selection problem, i.e., the identification of the correct number of clusters in a dataset. In what follows, for ease of reference, we refer to that instance still as model selection. It is an important part of any statistical analysis. The techniques used for solving it are mainly either Bayesian or data-driven, and are both based on internal knowledge. That is, they use information obtained by processing the input data. Although both techniques have been evaluated in the realm of microarray data analysis, their merits (relative to each other) has not been assessed. Here we will fill this gap in the literature by comparing three Bayesians versus several state of the art data-driven model selection methods. Our results show that, although in some cases Bayesian methods guarantee good results, they are not able to compete in terms of ability to predict the correct number of clusters in a dataset with the data-driven method
Compressive biological sequence analysis and archival in the era of high-throughput sequencing technologies
No full textHigh-throughput sequencing technologies produce large collections of data, mainly DNA sequences with additional information, requiring the design of efficient and effective methodologies for both their compression and storage. In this context, we first provide a classification of the main techniques that have been proposed, according to three specific research directions that have emerged from the literature and, for each, we provide an overview of the current techniques. Finally, to make this review useful to researchers and technicians applying the existing software and tools, we include a synopsis of the main characteristics of the described approaches, including details on their implementation and availability. Performance of the various methods is also highlighted, although the state of the art does not lend itself to a consistent and coherent comparison among all the methods presented here
Textual data compression in computational biology: Algorithmic techniques.
No full textIn a recent review [R. Giancarlo, D. Scaturro, F. Utro, Textual data compression in computational biology: a synopsis, Bioinformatics 25 (2009) 1575–1586] the first systematic organization and presentation of the impact of textual data compression for the analysis of biological data has been given. Its main focus was on a systematic presentation of the key areas of bioinformatics and computational biology where compression has been used together with a technical presentation of how well-known notions from information theory have been adapted to successfully work on biological data. Rather surprisingly, the use of data compression is pervasive in computational biology. Starting from that one, the focus of this companion review is on the computational methods involved in the use of data compression in computational biology. Indeed, although one would expect ad hoc adaptation of compression techniques to work on biological data, unifying and homogeneous algorithmic approaches are emerging. Moreover, given that experiments based on parallel sequencing are the future for biological research, data compression techniques are among a handful of candidates that seem able, successfully, to deal with the deluge of sequence data they produce; although, until now, only in terms of storage and indexing, with the analysis still being a challenge. Therefore, the two reviews, complementing each other, are perceived to be a useful starting point for computer scientists to get acquainted with many of the computational challenges coming from computational biology in which core ideas of the information sciences are already having a substantial impact
Stability-Based Model Selection for High Throughput Genomic Data: An Algorithmic Paradigm
No full textClustering is one of the most well known activities in scien- tific investigation and the object of research in many disciplines, ranging from Statistics to Computer Science. In this beautiful area, one of the most difficult challenges is the model selection problem, i.e., the identifi- cation of the correct number of clusters in a dataset. In the last decade, a few novel techniques for model selection, representing a sharp departure from previous ones in statistics, have been proposed and gained promi- nence for microarray data analysis. Among those, the stability-based methods are the most robust and best performing in terms of predic- tion, but the slowest in terms of time. Unfortunately, this fascinating and classic area of statistics as model selection, with important practical applications, has received very little attention in terms of algorithmic design and engineering. In this paper, in order to partially fill this gap, we highlight: (A) the first general algorithmic paradigm for stability- based methods for model selection; (B) a novel algorithmic paradigm for the class of stability-based methods for cluster validity, i.e., methods assessing how statistically significant is a given clustering solution; (C) a general algorithmic paradigm that describes heuristic and very effective speed-ups known in the Literature for stability-based model selection methods
The intrinsic combinatorial organization and information theoretic content of a sequence are correlated to the DNA encoded nucleosome organization of eukaryotic genomes
Full text linkMotivation: Thanks to research spanning nearly 30 years, two major models have emerged that account for nucleosome organization in chromatin: statistical and sequence specific. The first is based on elegant, easy to compute, closed-form mathematical formulas that make no assumptions of the physical and chemical properties of the underlying DNA sequence. Moreover, they need no training on the data for their computation. The latter is based on some sequence regularities but, as opposed to the statistical model, it lacks the same type of closed-form formulas that, in this case, should be based on the DNA sequence only. Results: We contribute to close this important methodological gap between the two models by providing three very simple formulas for the sequence specific one. They are all based on well-known formulas in Computer Science and Bioinformatics, and they give different quantifications of how complex a sequence is. In view of how remarkably well they perform, it is very surprising that measures of sequence complexity have not even been considered as candidates to close the mentioned gap. We provide experimental evidence that the intrinsic level of combinatorial organization and information-theoretic content of subsequences within a genome are strongly correlated to the level of DNA encoded nucleosome organization discovered by Kaplan et al. Our results establish an important connection between the intrinsic complexity of subsequences in a genome and the intrinsic, i.e. DNA encoded, nucleosome organization of eukaryotic genomes. It is a first step towards a mathematical characterization of this latter 'encoding'. Supplementary information: Supplementary data are available at Bioinformatics onlin
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
A methodology to assess the intrinsic discriminative ability of a distance function and its interplay with clustering algorithms for microarray data analysis
Full text linkBackground: Clustering is one of the most well known activities in scientific investigation and the object of research in many disciplines, ranging from statistics to computer science. Following Handl et al., it can be summarized as a three step process: (1) choice of a distance function; (2) choice of a clustering algorithm; (3) choice of a validation method. Although such a purist approach to clustering is hardly seen in many areas of science, genomic data require that level of attention, if inferences made from cluster analysis have to be of some relevance to biomedical research. Results: A procedure is proposed for the assessment of the discriminative ability of a distance function. That is, the evaluation of the ability of a distance function to capture structure in a dataset. It is based on the introduction of a new external validation index, referred to as Balanced Misclassification Index (BMI, for short) and of a nontrivial modification of the well known Receiver Operating Curve (ROC, for short), which we refer to as Corrected ROC (CROC, for short). The main results are: (a) a quantitative and qualitative method to describe the intrinsic separation ability of a distance; (b) a quantitative method to assess the performance of a clustering algorithm in conjunction with the intrinsic separation ability of a distance function. The proposed procedure is more informative than the ones available in the literature due to the adopted tools. Indeed, the first one allows to map distances and clustering solutions as graphical objects on a plane, and gives information about the bias of the clustering algorithm with respect to a distance. The second tool is a new external validity index which shows similar performances with respect to the state of the art, but with more flexibility, allowing for a broader spectrum of applications. In fact, it allows not only to quantify the merit of each clustering solution but also to quantify the agglomerative or divisive errors due to the algorithm. Conclusions: The new methodology has been used to experimentally study three popular distance functions, namely, Euclidean distance d2, Pearson correlation dr and mutual information dMI. Based on the results of the experiments, we have that the Euclidean and Pearson correlation distances have a good intrinsic discrimination ability. Conversely, the mutual information distance does not seem to offer the same flexibility and versatility as the other two distances. Apparently, that is due to well known problems in its estimation. since it requires that a dataset must have a substantial number of features to be reliable. Nevertheless, taking into account such a fact, together with results presented in Priness et al., one receives an indication that dMI may be superior to the other distances considered in this study only in conjunction with clustering algorithms specifically designed for its use. In addition, it results that K-means, Average Link, and Complete link clustering algorithms are in most cases able to improve the discriminative ability of the distances considered in this study with respect to clustering. The methodology has a range of applicability that goes well beyond microarray data since it is independent of the nature of the input data. The only requirement is that the input data must have the same format of a "feature matrix". In particular it can be used to cluster ChIP-seq data.Version of Recor
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
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