A Ranking Approach for Probe Selection and Classification of Microarray Data with Artificial Neural Networks.

Acute leukemia classification into its myeloid and lymphoblastic subtypes is usually accomplished according to the morphology of the tumor. Nevertheless, the subtypes may have similar histopathological appearance, making screening procedures difficult. In addition, approximately one-third of acute myeloid leukemias are characterized by aberrant cytoplasmic localization of nucleophosmin (NPMc(+)), where the majority has a normal karyotype.

Sequential selection of variables using short permutation procedures and multiple adjustments: An application to genomic data.

This work proposes a sequential methodology for selecting variables in classification problems in which the number of predictors is much larger than the sample size. The methodology includes a Monte Carlo permutation procedure that conditionally tests the null hypothesis of no association among the outcomes and the available predictors. In order to improve computing aspects, we propose a new parametric distribution, the Truncated and Zero Inflated Gumbel Distribution.

Convergence analysis of sliding mode trajectories in multi-objective neural networks learning.

The Pareto-optimality concept is used in this paper in order to represent a constrained set of solutions that are able to trade-off the two main objective functions involved in neural networks supervised learning: data-set error and network complexity. The neural network is described as a dynamic system having error and complexity as its state variables and learning is presented as a process of controlling a learning trajectory in the resulting state space. In order to control the trajectories, sliding mode dynamics is imposed to the network.

Protein classification with Extended-Sequence Coding by sliding window.

A large number of unclassified sequences is still found in public databases, which suggests that there is still need for new investigations in the area. In this contribution, we present a methodology based on Artificial Neural Networks for protein functional classification. A new protein coding scheme, called here Extended-Sequence Coding by Sliding Windows, is presented with the goal of overcoming some of the difficulties of the well method Sequence Coding by Sliding Window.

Affinity-Matrix representation of spike train activities in the early visual pathway.

The understanding of how neurons interact in the visual cortex and what types of neurons are responsable for each interaction are still open questions. In order to analyse such problem, the spiking activity of neurons in the central visual pathway of awake owls was analyzed with Principal Component Analysis (PCA) and clustering techniques. Further analysis using kernel representation revealed the existence of two large groups of neurons with distinguishable behavior.

Genetic algorithm applied to hierarchically coupled associative memories.

Inspired by the theory of neuronal group selection (TNGS), we have carried out an analysis of the capacity of convergence of a multi-level associative memory based on coupled generalized-brain-state-in-a-box (GBSB) networks through evolutionary computation. The TNGS establishes that a memory process can be described as being organized functionally in hierarchical levels where higher levels coordinate sets of functions of lower levels. According to this theory, the most basic units in the cortical area of the brain are called neuronal groups or first-level blocks of memories and the higher-level memories are formed through selective strengthening or weakening of the synapses amongst the neuronal groups.