1,721,048 research outputs found
diffuStats: an R package to compute diffusion-based scores on biological networks
No full textThis is a pre-copyedited, author-produced version of an article accepted for publication in Bioinformatics following peer review. The version of record Sergio Picart-Armada, Wesley K Thompson, Alfonso Buil, Alexandre Perera-Lluna; diffuStats: an R package to compute diffusion-based scores on biological networks, Bioinformatics, Volume 34, Issue 3, 1 February 2018, Pages 533–534 is available online at: https://doi.org/10.1093/bioinformatics/btx632.Label propagation and diffusion over biological networks are a common mathematical formalism in computational biology for giving context to molecular entities and prioritising novel candidates in the area of study.
There are several choices in conceiving the diffusion process -involving the graph kernel, the score definitions and the presence of a posterior statistical normalisation- which have an impact on the results.
This manuscript describes diffuStats, an R package that provides a collection of graph kernels and diffusion scores, as well as a parallel permutation analysis for the normalised scores, that eases the computation of the scores and their benchmarking for an optimal choice.Peer ReviewedPostprint (author's final draft
Additional File 2.tab.gz
No full textThis is the dataset used in the paper Effect of Sequence Padding on the Performance of Protein-Based Deep Learning Models by Angela Lopez-del Rio, Maria Martin, Alexandre Perera-Lluna and Rabie Saidi.The UniprotKB/Swiss-Prot database (version 2019_05) protein entries analysed during the current study can be accessed and downloaded through the following link: http://ftp.ebi.ac.uk/pub/databases/uniprot/previous_releases/release-2019_05/knowledgebase/uniprot_sprot-only2019_05.tar.gz. Since this data needs further filtering to get only taxonomy Archaea, we have uploaded here the data analysed in this article.The code is publicly available at https://github.com/b2slab/padding_benchmark. </div
Going Beyond Counting First Authors in Author Co-citation Analysis
Get PDFThe 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
Boxplots for array of six sensors of types 1, 2, 3, 13, 14 and 17 show the distribution of sensor signals in response to analyte A at concentrations 0.01, 0.02, 0.05 and 0.1%.
No full text<p>The concentration values were selected to cover the dynamic range of analyte A and to include the value in the saturation region. All the sensors show a non-linear response to analyte A at the selected concentration range. The three sensors of types 13, 14 and 17 show rather noisy responses. The plot is produced by the plotBoxplot method applied to the sensor array under drift-free conditions.</p
Performance on prediction of concentration of gas C under drift-free conditions.
No full text<p>Two methods, linear PLS and non-linear SVR, were tested on the regression task of analyte C given at concentration 0.1, 0.4, 1 and 2 vol.%. Three arrays composed of 24 sensors, different in the types of sensor, were compared in terms of the root-mean-square error in prediction (RMSEP). For each array, the non-linear models outperform the linear models. All three arrays show similar performance with the SVR method, and it is hard to pick the best array.</p
Variations on the Author
Get PDF“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
Organization of simulation models in the <i>chemosensors</i> package.
No full text<p>Simulation models, their classes and associated data sets of parameters computed for the seventeen UNIMAN sensors.</p
Scoreplot corresponding to the Principal Component Analysis of the sensor array data gathered from the array consisting of 12 sensors of types 13, 14 and 17.
No full text<p>The array was exposed to six gas classes: pure analyte A at concentrations 0.01 and 0.05 (labels A 0.01 and A 0.05), pure analyte C at concentrations 0.1 and 1 (C 0.1 and C 1), and two binary mixtures of A and C (A 0.01, C 0.1 and A 0.05, C 1). The concentrations were given at volume fraction units <i>vol.%</i>, and the measurement of each gas class was repeated 10 times. The distribution of the scores shows that the sensors in the array have more affinity to analyte A than to analyte C. The plot is produced by the plotPCA method applied to the sensor array.</p
Basic slots of SensorArray class in <i>chemosensors</i> package.
No full text<p>Description of basic slots of SensorArray class necessary to parameterize a virtual sensor array.</p
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