Prototipo de semaforización

Consiente del creciente problema de tráfico vehicular que está surgiendo en nuestro país ya que con los nuevos horizontes de la globalización y demás factores económicos y tras observar que el sistema de trasporte en general, no funciona de la mejor manera, por razones simples como la falta de sincronización de los semáforos ó el funcionamiento bajo los mismos tiempos durante todo el día entre otras; además, el mal uso de las vías por parte de los ciudadanos, dada la constante indisciplina entre vehículos públicos, particulares livianos y de carga, este proyecto propone una solución a la mayor parte de los inconvenientes que se presentan a diario en las vías rápidas de transporte de nuestro país, ya que por la imprudencia de muchos conductores y de la misma ciudadanía es que ocurren trágicos accidentes dejando consigo mismo la desolación y la inseguridad. Por efecto de esta problemática nos llevo a elaborar un prototipo de semaforización el cual permitirá disminuir el alto índice de accidentalidad y no obstante colaborar con el medio ambiente ya que a la menor emanación de esmog menor será la contaminación que producirán los vehículos

Prototipo de semaforización

Consiente del creciente problema de tráfico vehicular que está surgiendo en nuestro país ya que con los nuevos horizontes de la globalización y demás factores económicos y tras observar que el sistema de trasporte en general, no funciona de la mejor manera, por razones simples como la falta de sincronización de los semáforos ó el funcionamiento bajo los mismos tiempos durante todo el día entre otras; además, el mal uso de las vías por parte de los ciudadanos, dada la constante indisciplina entre vehículos públicos, particulares livianos y de carga, este proyecto propone una solución a la mayor parte de los inconvenientes que se presentan a diario en las vías rápidas de transporte de nuestro país, ya que por la imprudencia de muchos conductores y de la misma ciudadanía es que ocurren trágicos accidentes dejando consigo mismo la desolación y la inseguridad. Por efecto de esta problemática nos llevo a elaborar un prototipo de semaforización el cual permitirá disminuir el alto índice de accidentalidad y no obstante colaborar con el medio ambiente ya que a la menor emanación de esmog menor será la contaminación que producirán los vehículos

Classification in biological networks with hypergraphlet kernels

Abstract Motivation Biological and cellular systems are often modeled as graphs in which vertices represent objects of interest (genes, proteins and drugs) and edges represent relational ties between these objects (binds-to, interacts-with and regulates). This approach has been highly successful owing to the theory, methodology and software that support analysis and learning on graphs. Graphs, however, suffer from information loss when modeling physical systems due to their inability to accurately represent multiobject relationships. Hypergraphs, a generalization of graphs, provide a framework to mitigate information loss and unify disparate graph-based methodologies. Results We present a hypergraph-based approach for modeling biological systems and formulate vertex classification, edge classification and link prediction problems on (hyper)graphs as instances of vertex classification on (extended, dual) hypergraphs. We then introduce a novel kernel method on vertex- and edge-labeled (colored) hypergraphs for analysis and learning. The method is based on exact and inexact (via hypergraph edit distances) enumeration of hypergraphlets; i.e. small hypergraphs rooted at a vertex of interest. We empirically evaluate this method on fifteen biological networks and show its potential use in a positive-unlabeled setting to estimate the interactome sizes in various species.This work was partially supported by the National Science Foundation (NSF) [DBI-1458477], National Institutes of Health (NIH) [R01 MH105524], the Indiana University Precision Health Initiative, the European Research Council (ERC) [Consolidator Grant 770827], UCL Computer Science, the Slovenian Research Agency project [J1-8155], the Serbian Ministry of Education and Science Project [III44006] and the Prostate Project.Peer ReviewedPostprint (author's final draft

Dynamic Bayesian networks for integrating multi-omics time-series microbiome data

. A key challenge in the analysis of longitudinal microbiomes data is to go beyond computing their compositional profiles and infer the complex web of interactions between the various microbial taxa, their genes, and the metabolites they consume and produce. To address this challenge, we developed a computational pipeline that first aligns multi-omics data and then uses dynamic Bayesian networks (DBNs) to integrate them into a unified model. We discuss how our approach handles the different sampling and progression rates between individuals, how we reduce the large number of different entities and parameters in the DBNs, and the construction and use of a validation set to model edges. Applying our method to data collected from Inflammatory Bowel Disease (IBD) patients, we show that it can accurately identify known and novel interactions between various entities and can improve on current methods for learning such interactions. Experimental validations support several predictions about novel metabolite-taxa interactions. The source code is freely available under the MIT Open Source license agreement and can be downloaded from https://github.com/DaniRuizPerez/longitudinal_multiomic_analysis_public

Dynamic bayesian networks for integrating multi-omics time series microbiome data

A key challenge in the analysis of longitudinal microbiome data is theinference of temporal interactions between microbial taxa, their genes, the metabolites that they consume and produce, and host genes. To address these challenges,we developed a computational pipeline, a pipeline for the analysis of longitudinalmulti-omics data (PALM), that first aligns multi-omics data and then uses dynamicBayesian networks (DBNs) to reconstruct a unified model. Our approach overcomesdifferences in sampling and progression rates, utilizes a biologically inspired multiomic framework, reduces the large number of entities and parameters in the DBNs,and validates the learned network. Applying PALM to data collected from inflammatory bowel disease patients, we show that it accurately identifies known and novelinteractions. Targeted experimental validations further support a number of the predicted novel metabolite-taxon interactionsFil: Ruiz Perez, Daniel. Florida International University; Estados UnidosFil: Lugo Martinez, Jose. University of Carnegie Mellon; Estados UnidosFil: Bourguignon, Natalia. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina. Florida International University; Estados Unidos. Universidad Tecnológica Nacional; ArgentinaFil: Mathee, Kalai. Florida International University; Estados UnidosFil: Lerner, Betiana. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina. Universidad Tecnológica Nacional; ArgentinaFil: Bar Joseph, Ziv. University of Carnegie Mellon; Estados UnidosFil: Narasimhan, Giri. Florida International University; Estados Unido

Dynamic bayesian networks for integrating multi-omics time series microbiome data

A key challenge in the analysis of longitudinal microbiome data is the inference of temporal interactions between microbial taxa, their genes, the metabolites that they consume and produce, and host genes. To address these challenges, we developed a computational pipeline, a pipeline for the analysis of longitudinal multi-omics data (PALM), that first aligns multi-omics data and then uses dynamic Bayesian networks (DBNs) to reconstruct a unified model. Our approach overcomes differences in sampling and progression rates, utilizes a biologically inspired multiomic framework, reduces the large number of entities and parameters in the DBNs, and validates the learned network. Applying PALM to data collected from inflammatory bowel disease patients, we show that it accurately identifies known and novel interactions. Targeted experimental validations further support a number of the predicted novel metabolite-taxon interactions

Extensive error in the number of genes inferred from draft genome assemblies

Current sequencing methods produce large amounts of data, but genome assemblies based on these data are often woefully incomplete. These incomplete and error-filled assemblies result in many annotation errors, especially in the number of genes present in a genome. In this paper we investigate the magnitude of the problem, both in terms of total gene number and the number of copies of genes in specific families. To do this, we compare multiple draft assemblies against higher-quality versions of the same genomes, using several new assemblies of the chicken genome based on both traditional and next-generation sequencing technologies, as well as published draft assemblies of chimpanzee. We find that upwards of 40% of all gene families are inferred to have the wrong number of genes in draft assemblies, and that these incorrect assemblies both add and subtract genes. Using simulated genome assemblies of Drosophila melanogaster, we find that the major cause of increased gene numbers in draft genomes is the fragmentation of genes onto multiple individual contigs. Finally, we demonstrate the usefulness of RNA-Seq in improving the gene annotation of draft assemblies, largely by connecting genes that have been fragmented in the assembly process

The loss and gain of functional amino acid residues is a common mechanism causing human inherited disease

Elucidating the precise molecular events altered by disease-causing genetic variants represents a major challenge in translational bioinformatics. To this end, many studies have investigated the structural and functional impact of amino acid substitutions. Most of these studies were however limited in scope to either individual molecular functions or were concerned with functional effects (e.g. deleterious vs. neutral) without specifically considering possible molecular alterations. The recent growth of structural, molecular and genetic data presents an opportunity for more comprehensive studies to consider the structural environment of a residue of interest, to hypothesize specific molecular effects of sequence variants and to statistically associate these effects with genetic disease. In this study, we analyzed data sets of disease-causing and putatively neutral human variants mapped to protein 3D structures as part of a systematic study of the loss and gain of various types of functional attribute potentially underlying pathogenic molecular alterations. We first propose a formal model to assess probabilistically function-impacting variants. We then develop an array of structure-based functional residue predictors, evaluate their performance, and use them to quantify the impact of disease-causing amino acid substitutions on catalytic activity, metal binding, macromolecular binding, ligand binding, allosteric regulation and post-translational modifications. We show that our methodology generates actionable biological hypotheses for up to 41% of disease-causing genetic variants mapped to protein structures suggesting that it can be reliably used to guide experimental validation. Our results suggest that a significant fraction of disease-causing human variants mapping to protein structures are function-altering both in the presence and absence of stability disruption

Inferring the molecular and phenotypic impact of amino acid variants with MutPred2

Identifying pathogenic variants and underlying functional alterations is challenging. To this end, we introduce MutPred2, a tool that improves the prioritization of pathogenic amino acid substitutions over existing methods, generates molecular mechanisms potentially causative of disease, and returns interpretable pathogenicity score distributions on individual genomes. Whilst its prioritization performance is state-of-the-art, a distinguishing feature of MutPred2 is the probabilistic modeling of variant impact on specific aspects of protein structure and function that can serve to guide experimental studies of phenotype-altering variants. We demonstrate the utility of MutPred2 in the identification of the structural and functional mutational signatures relevant to Mendelian disorders and the prioritization of de novo mutations associated with complex neurodevelopmental disorders. We then experimentally validate the functional impact of several variants identified in patients with such disorders. We argue that mechanism-driven studies of human inherited disease have the potential to significantly accelerate the discovery of clinically actionable variants