1,720,963 research outputs found
Temporal Sequence Pattern Learning and Dynamic System Control (DSC)
No full textWe have investigated the role of temporal sequence learning, using an unsuper- vised artificial neural network (1), called Monoconnected Autoreflexive Neural Net- work, for better understanding the implicit learning process role, involved during elementary associative learning processes. Several neural network models have been proposed to describe implicit learning (IL), using unsupervised and self-organized models (2, 3). In our experiments we used a real biochemical data set consisting of 15 features, that deals with penicillin production (112 temporal sequence blocks with 11 sequence points per block (1232 patterns). The prediction task requires that the neural network can predict the correct sequence position, after a preliminary training (50% of all patterns). After training, the neural network learn to find the correct position in the temporal sequences with good accuracy. Our results seem to confirm that elementary associative learning, could be used in temporal sequence learning and that dynamic system control (DSC) tasks (for instance to know which features are more sensitive to a better penicillin production), could be derived from the implicit learning process, using the importance of different features, recovered from the weight matrix analysis
Pattern identification and classification in gene expression data using an autoassociative neural network model
No full textThe application of DNA microarray technology for analysis of gene expression creates enormous opportunities to accelerate the pace in understanding living systems and identification of target genes and pathways for drug development and therapeutic intervention. Parallel monitoring of the expression profiles of thousands of genes seems particularly promising for a deeper understanding of cancer biology and the identification of molecular signatures supporting the histological classification schemes of neoplastic specimens. However, the increasing volume of data generated by microarray experiments poses the challenge of developing equally efficient methods and analysis procedures to extract, interpret, and upgrade the information content of these databases. Herein, a computational procedure for pattern identification, feature extraction, and classification of gene expression data through the analysis of an autoassociative neural network model is described. The identified patterns and features contain critical information about gene-phenotype relationships observed during changes in cell physiology. They represent a rational and dimensionally reduced base for understanding the basic biology of the onset of diseases, defining targets of therapeutic intervention, and developing diagnostic tools for the identification and classification of pathological states. The proposed method has been tested on two different microarray clatasets-Golub's analysis of acute human leukemia [Golub et al. (1999) Science 286:531537], and the human colon adenocarcinoma study presented by Alon et al. [1999; Proc Natl Acad Sci USA 97:10101-10106]. The analysis of the neural network internal structure allows the identification of specific phenotype markers and the extraction of peculiar associations among genes and physiological states. At the same time, the neural network outputs provide assignment to multiple classes, such as different pathological conditions or tissue samples, for previously unseen instances
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 method for the final yield prediction and for the automatic control of solid-phase peptide synthesis
No full textMetodo per la predizione della resa finale ed il controllo automatico della sintesi di peptidi in fase solida
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
Metodo per la predizione della resa finale ed il controllo automatico della sintesi di peptidi in fase solida
No full textMetodo per la predizione della resa finale ed il controllo automatico della sintesi di peptidi in fase solida comprendente la rilevazione del seganle conduttimetrico generato dalla miscela di reazione nei primi 5 minuti di reazione, il campionamento di detto segnale attraverso un sistema di conversione analogico/digitale e l'elaborazione dell'andamento del segnale mediante rete neural
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
Extracting dynamic patterns from gene expression data by the analysis of an autoassociative neural network architecture
No full textAutoassociative neural networks are used to analyse gene expression profiles
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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