Igor Lorenzato Almeida

Gene Expression Analysis using Markov Chains extracted from RNNs

This paper present a new approach for the analysis of gene exp res- sion, by extracting a Markov Chain from trained Recurrent Ne ural Networks (RNNs). A lot of microarray data is being generated, since ar ray technologies have been widely used to monitor simultaneously the express ion pattern of thou- sands of genes. Microarray data is highly specialized, invo lves several variables in which are complex to express and analyze. The challenge is to discover how to extract useful information from these data sets. So this w ork proposes the use of RNNs for data modeling, due to their ability to learn co mplex temporal non-linear data. Once a model is obtained for the data, it is p to ex- tract the acquired knowledge and to represent it through Mar kov Chains model. Markov Chains are easily visualized in the form of states gra phs, which show the influences among the gene expression levels and their cha nges in time.

Aplicativo Lúdico-Pedagógico para Ensino de Programação e Robótica Educacional

This article describes the development of an application for teaching programming, encompassing educational robotics. Based on the diagnosis of high demand for professionals with a high level of logical reasoning and the rising use of mobile devices by young people, an application has been developed with the use of HTML, Javascript and the Cordova framework. The software has a ludic and pedagogical feature which aims to influence positively the teaching of programming. The application has a fill-in-theblanks game style and connects to an Arduino robot via Bluetooth. Therefore the Arduino robot is able to receive commands from the application and demonstrate the results of the programming done in the application. 1. Cenário de uso Muitas são as oportunidades para profissionais qualificados da área de Tecnologia da Informação (TI) e existe o desafio, no Brasil, de atender à demanda de mão de obra do mercado de TI, que tende a ampliar sua participação no PIB em 6,5% até 2020 [Alvarenga, 2012]. Isso implica na necessidade de abertura de cursos para formação de profissionais com conhecimentos básicos aplicados, que atendam à demanda imediata do mundo do trabalho, para possibilitar, nesse, sua rápida inserção. Entretanto, sabe-se que poucas são as pessoas interessadas em ingressar na área. DOI: 10.5753/cbie.wcbie.2017.222 222 Anais dos Workshops do VI Congresso Brasileiro de Informática na Educação (WCBIE 2017) VI Congresso Brasileiro de Informática na Educação (CBIE 2017)

Markov Chain Inference From Microarray Data

Array technologies have made it straightforward to monitor simultaneously the expression pattern of thousands of genes. Thus, a lot of data is being generated and the challenge now is to discover how to extract useful information from these data sets. Microarray data is highly specialized, involves several variables in a non-linear and temporal way, demanding nonlinear recurrent free models, which are complex to formulate and to analyse in a simple way. Markov Chains are easily visualized in the form of graphs of states, which show the influences among the gene expression levels and their changes in time. In this work, we propose a new approach to microarray data analysis, by extracting a Markov Chain from Microarray Data. Two aspects are of interest for the researcher: the time evolution of the genic expression and their mutual influence in the form of regulatory networks.