Denise Regina Pechmann

Comparison of deterministic and fuzzy finite automata extraction methods from Jordan networks

This paper compares two methods for the extraction of finite state automata from recurrent neural networks (RNNs). Neural networks store the knowledge implicit in the data in their weights, but do not provide an easy explanation of this knowledge to the user. This is a difficult task due to the spatial (distributed information in the network) and temporal (network states) relations built by the network among the data. One form to present the knowledge stored inside a RNN is using finite state automata, which shows explicitly the relations among the variables and their temporal causality. In this paper, we treat the nonlinear dynamical system inverted pendulum and controller and compare the performance of the extraction algorithm using two clustering methods: k-means and fuzzy clustering in terms of exactness and knowledge conciseness.

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.

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.