Métricas de rendimiento para evaluar el aprendizaje automático en la clasificación de imágenes petroleras utilizando redes neuronales convolucionales

Neural networks enter an extensive field of artificial intelligence, their functions are: image-related learning, speech recognition, text detection, object detection, language detection, facial recognition, etc. A neural network learns tasks by analyzing large amounts of data, learning can be supervised, reinforced or unsupervised. This work focuses on the development of intelligent software based on a convolutional neural network, whose objective is to classify images, patterns and text from photos taken of oil wells, which are evaluated through performance metrics, which help detect possible errors, false predictions, underfitting and overfitting that may arise in machine learning. The objective of this work is to show how each of the performance metrics is applied to obtain better precision and accuracy for image classification using convolutional neural networks.Las redes neuronales entran en un campo extenso de la inteligencia artificial, sus funciones son: el aprendizaje relacionado con imágenes, reconocimiento de voz, detección de texto, detección de objetos, detección de idioma, reconocimiento facial, etc. Una red neuronal aprende tareas analizando grandes cantidades de datos, el aprendizaje puede ser supervisado, reforzado o no supervisado. La red neuronal convolucional (CNN) utilizan una jerarquía de características combinando las de bajo nivel con una estructura de capas para formar características de alto nivel. (Rashka & Mirjalili, 2019)Este trabajo se centra en el desarrollo de un software inteligente basado en una CNN, que clasifica imágenes, patrones y texto de fotos tomadas a pozos petroleros, las cuales son evaluadas por medio de métricas de rendimiento, que ayudan a detectar posibles errores, falsas predicciones, suba justes y sobreajustes que pueden llegar a surgir en el aprendizaje automático. El objetivo de este trabajo es mostrar cómo se aplica cada una de las métricas de rendimiento para obtener una mejor precisión y exactitud para la clasificación de imágenes utilizando redes neuronales convolucionales

Mutational dynamics in the mouse mitochondrial genome.

In the cell there are from hundreds to thousands of mitochondria. Mitochondrial mutant genomes can coexist with wild-type genomes. Mutations in the mitochondrial genome have been associated to several diseases, such as aging, Alzheimerâ s disease, Parkinsonâ s disease, some forms of cancer, infertility, neuromuscular disorders, etc. In this work, we address the following questions: what is the mutation load in the mitochondrial genome does the mutation load change in the mouse brain in different stages of life does the frequency of individual mutations change in different stages of life how are mutations distributed in the mitochondrial genome I will show preliminary results of this study in the mouse mitochondrial genome that will give us insights about the mutational dynamics in the human mitochondrial genome.Non UBCUnreviewedAuthor affiliation: UNAMResearche

Graph Theory in Orthology Detection

During evolution genes go throw many events, such as duplication, speciation, loss, horizontal gene transfer, among others. Two genes are said to be paralogs if they are the product of a duplication event, and orthologs if they are the product of a speciation event. The distinction of paralogs and orthologs is an important problem in comparative and evolutionary genomics. Moreover, orthology detection is a first step towards any functional annotation study. The evolutionary history of a set of genes can be represented as a phylogenetic tree where leaves represent genes and internal nodes evolutionary events. In this work, we investigate a graph theory-based method for the prediction of large-scale orthologous genes and the reconstruction of their evolutionary history [1,2,3]. We represent genes as vertices of a graph and place an edge between two genes if their sequence similarity is high. We characterize mathematically the topological properties of this graph in order to have only valid orthology relations. Surprisingly, graphs that represent valid orthology relations are P4-free, i.e. graphs which do not contain induced paths of length four. These graphs have been studied earlier and have been characterized as cographs [4]. We further investigate a set of induced subgraphs that give us evidence of noise in the data set or of wrong orthology predictions. In order to remove those induced subgraphs, we need to come up with a solution for the cograph editing problem, which has been found to be NP-complete [5]. Here we also present a work-in-process heuristic for the cograph edting problem that will help us to induce valid orthology relations. Acknowledgments: This research was supported by Conacyt Mexico and DAAD Germany. [1] Marcus Lechner, Maribel Hernandez-Rosales, Daniel Doerr, Nicolas Wieseke, An- nelyse Thevenin, Jens Stoye, Sonja J. Prohaska and Peter F. Stadler. Orthology Detection Combining Clustering and Synteny for Very Large Data Sets. PlosONE, 9(8):e105015, (2014). [2] Marc Hellmuth, Maribel Hernandez-Rosales, Katharina T. Huber, Vincent Moul- ton, Peter F. Stadler, and Nicolas Wieseke. Orthology relations, symbolic ul- trametrics, and cographs. J. Math. Biol. 66(1-2):399-420, (2013). [3] Maribel Hernandez-Rosales, Marc Hellmuth, Nicolas Wieseke, Katharina Huber, Vincent Moulton, and Peter F. Stadler. From event-labeled gene trees to species trees. BMC Bioinformatics 13(Suppl. 19):S6, (2012) [4] Corneil DG, Lerchs H, Stewart Burlingham LK. Complement reducible graphs. Discrete Appl Math 3:163â 174, (1981). [5] Liu Y, Wang J, Guo J, Chen J. Cographs editing: complexity and parametrized algorithms. In: Fu B, Du DZ (eds) COCOON 2011. Lecture notes computer science, vol 6842. Springer, Berlin, pp 110â 121, (2011).Non UBCUnreviewedAuthor affiliation: UNAMResearche

Intermunicipal travel networks of Mexico during the COVID-19 pandemic

Abstract Human mobility networks are widely used for diverse studies in geography, sociology, and economics. In these networks, nodes usually represent places or regions and links refer to movement between them. They become essential when studying the spread of a virus, the planning of transit, or society’s local and global structures. Therefore, the construction and analysis of human mobility networks are crucial for a vast number of real-life applications. This work presents a collection of networks that describe the human travel patterns between municipalities in Mexico in the 2020–2021 period. Using anonymized mobile location data, we constructed directed, weighted networks representing the volume of travels between municipalities. We analysed changes in global, local, and mesoscale network features. We observe that changes in these features are associated with factors such as COVID-19 restrictions and population size. In general, the implementation of restrictions at the start of the COVID-19 pandemic in early 2020, induced more intense changes in network features than later events, which had a less notable impact in network features. These networks will result very useful for researchers and decision-makers in the areas of transportation, infrastructure planning, epidemic control and network science at large

Insights into the Transcriptional Reprogramming in Tomato Response to PSTVd Variants Using Network Approaches

Viroids are the smallest pathogens of angiosperms, consisting of non-coding RNAs that cause severe diseases in agronomic crops. Symptoms associated with viroid infection are linked to developmental alterations due to genetic regulation. To understand the global mechanisms of host viroid response, we implemented network approaches to identify master transcription regulators and their differentially expressed targets in tomato infected with mild and severe variants of PSTVd. Our approach integrates root and leaf transcriptomic data, gene regulatory network analysis, and identification of affected biological processes. Our results reveal that specific bHLH, MYB, and ERF transcription factors regulate genes involved in molecular mechanisms underlying critical signaling pathways. Functional enrichment of regulons shows that bHLH-MTRs are linked to metabolism and plant defense, while MYB-MTRs are involved in signaling and hormone-related processes. Strikingly, a member of the bHLH-TF family has a specific potential role as a microprotein involved in the post-translational regulation of hormone signaling events. We found that ERF-MTRs are characteristic of severe symptoms, while ZNF-TF, tf3a-TF, BZIP-TFs, and NAC-TF act as unique MTRs. Altogether, our results lay a foundation for further research on the PSTVd and host genome interaction, providing evidence for identifying potential key genes that influence symptom development in tomato plants