The Pad\'e iterations for the matrix sign function and their reciprocals are optimal

It is proved that among the rational iterations locally converging with order s>1 to the sign function, the Pad\'e iterations and their reciprocals are the unique rationals with the lowest sum of the degrees of numerator and denominator

CpGmotifs : a tool to discover DNA motifs associated to CpG methylation events

BackgroundThe investigation of molecular alterations associated with the conservation and variation of DNA methylation in eukaryotes is gaining interest in the biomedical research community. Among the different determinants of methylation stability, the DNA composition of the CpG surrounding regions has been shown to have a crucial role in the maintenance and establishment of methylation statuses. This aspect has been previously characterized in a quantitative manner by inspecting the nucleotidic composition in the region. Research in this field still lacks a qualitative perspective, linked to the identification of certain sequences (or DNA motifs) related to particular DNA methylation phenomena.ResultsHere we present a novel computational strategy based on short DNA motif discovery in order to characterize sequence patterns related to aberrant CpG methylation events. We provide our framework as a user-friendly, shiny-based application, CpGmotifs, to easily retrieve and characterize DNA patterns related to CpG methylation in the human genome. Our tool supports the functional interpretation of deregulated methylation events by predicting transcription factors binding sites (TFBS) encompassing the identified motifs.ConclusionsCpGmotifs is an open source software. Its source code is available on GitHub https://github.com/Greco-Lab/CpGmotifs and a ready-to-use docker image is provided on DockerHub at https://hub.docker.com/r/grecolab/cpgmotifs.Peer reviewe

Reingeniería organizacional del restaurante El Coirón

A partir de la hipótesis: la realización de una reingeniería de la organización mediante la aplicación de un plan de negocios y la ampliación del establecimiento, mejorará la gestión del restaurante y atención al público”, se realizó un asesoramiento profesional a los dueños del restaurante El Coirón, ubicado en la ruta 82 del departamento de Lujan de Cuyo, provincia de Mendoza, República Argentina. El mismo consistió en un plan de negocios con el fin de permitirles consolidar una gestión formal y más eficiente de su organización. El presente trabajo es un estudio modificativo y se planteó como objetivo general un incremento en la utilidades del 20% para el próximo año y como específicos: invertir en la ampliación de las instalaciones y capacidad del restaurante en un 60% en los próximos cuatro meses; acortar los tiempos de entrega de comida en un 40% e invertir en publicidad y promoción un 15% de las ganancias semestrales. La evaluación del proyecto de ampliación, dio como resultado la viabilidad del mismo. Lo expuesto se vio reflejado en indicadores como el VAN (Valor Actual Neto) $ 211.258, el cual indica que si los dueños llevaran a cabo el proyecto su riqueza se incrementaría en dicho monto. Una TIR (tasa interna de retorno) de 75% refleja una estimación de la rentabilidad del proyecto. Otro indicador positivo que confirma la viabilidad, es la Relación de Beneficio Costo de 1,46 por ser mayor a 1.Fil: Greco, Federico Jos

Knowledge Generation with Rule Induction in Cancer Omics

The explosion of omics data availability in cancer research has boosted the knowledge of the molecular basis of cancer, although the strategies for its definitive resolution are still not well established. The complexity of cancer biology, given by the high heterogeneity of cancer cells, leads to the development of pharmacoresistance for many patients, hampering the efficacy of therapeutic approaches. Machine learning techniques have been implemented to extract knowledge from cancer omics data in order to address fundamental issues in cancer research, as well as the classification of clinically relevant sub-groups of patients and for the identification of biomarkers for disease risk and prognosis. Rule induction algorithms are a group of pattern discovery approaches that represents discovered relationships in the form of human readable associative rules. The application of such techniques to the modern plethora of collected cancer omics data can effectively boost our understanding of cancer-related mechanisms. In fact, the capability of these methods to extract a huge amount of human readable knowledge will eventually help to uncover unknown relationships between molecular attributes and the malignant phenotype. In this review, we describe applications and strategies for the usage of rule induction approaches in cancer omics data analysis. In particular, we explore the canonical applications and the future challenges and opportunities posed by multi-omics integration problems.Peer reviewe

Network Analysis of Microarray Data

DNA microarrays are widely used to investigate gene expression. Even though the classical analysis of microarray data is based on the study of differentially expressed genes, it is well known that genes do not act individually. Network analysis can be applied to study association patterns of the genes in a biological system. Moreover, it finds wide application in differential coexpression analysis between different systems. Network based coexpression studies have for example been used in (complex) disease gene prioritization, disease subtyping, and patient stratification.Peer reviewe

VOLTA : adVanced mOLecular neTwork Analysis

Motivation: Network analysis is a powerful approach to investigate biological systems. It is often applied to study gene co-expression patterns derived from transcriptomics experiments. Even though co-expression analysis is widely used, there is still a lack of tools that are open and customizable on the basis of different network types and analysis scenarios (e.g. through function accessibility), but are also suitable for novice users by providing complete analysis pipelines. Results: We developed VOLTA, a Python package suited for complex co-expression network analysis. VOLTA is designed to allow users direct access to the individual functions, while they are also provided with complete analysis pipelines. Moreover, VOLTA offers when possible multiple algorithms applicable to each analytical step (e.g. multiple community detection or clustering algorithms are provided), hence providing the user with the possibility to perform analysis tailored to their needs. This makes VOLTA highly suitable for experienced users who wish to build their own analysis pipelines for a wide range of networks as well as for novice users for which a 'plug and play' system is provided.Peer reviewe

A systematic comparison of data- and knowledge-driven approaches to disease subtype discovery

bbab314Typical clustering analysis for large-scale genomics data combines two unsupervised learning techniques: dimensionality reduction and clustering (DR-CL) methods. It has been demonstrated that transforming gene expression to pathway-level information can improve the robustness and interpretability of disease grouping results. This approach, referred to as biological knowledge-driven clustering (BK-CL) approach, is often neglected, due to a lack of tools enabling systematic comparisons with more established DR-based methods. Moreover, classic clustering metrics based on group separability tend to favor the DR-CL paradigm, which may increase the risk of identifying less actionable disease subtypes that have ambiguous biological and clinical explanations. Hence, there is a need for developing metrics that assess biological and clinical relevance. To facilitate the systematic analysis of BK-CL methods, we propose a computational protocol for quantitative analysis of clustering results derived from both DR-CL and BK-CL methods. Moreover, we propose a new BK-CL method that combines prior knowledge of disease relevant genes, network diffusion algorithms and gene set enrichment analysis to generate robust pathway-level information. Benchmarking studies were conducted to compare the grouping results from different DR-CL and BK-CL approaches with respect to standard clustering evaluation metrics, concordance with known subtypes, association with clinical outcomes and disease modules in co-expression networks of genes. No single approach dominated every metric, showing the importance multi-objective evaluation in clustering analysis. However, we demonstrated that, on gene expression data sets derived from TCGA samples, the BK-CL approach can find groupings that provide significant prognostic value in both breast and prostate cancers.Peer reviewe

Manually curated and harmonised transcriptomics datasets of psoriasis and atopic dermatitis patients

We present manually curated transcriptomics data of psoriasis and atopic dermatitis patients retrieved from the NCBI Gene Expression Omnibus and EBI ArrayExpress repositories. We collected 39 transcriptomics datasets, deriving from DNA microarrays and RNA-Sequencing technologies, for a total of 1677 samples. We provide quality-checked, homogenised and preprocessed gene expression matrices and their corresponding metadata tables along with the estimated surrogate variables. These data represent a ready-made valuable source of knowledge for translational researchers in the dermatology field.Peer reviewe