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. This work is based on two DNA microarray datasets, available publicly, to differentiate leukemia subtypes. The datasets were split into training and test sets, and feature selection methods were applied. Artificial neural network classifiers were developed to compare the feature selection methods. For the first dataset, 50 genes selected using the best classifier was able to classify all patients in the test set. For the second dataset, five genes yielded 97.5% accuracy in the test set.

A maximum margin-based kernel width estimator and its application to the response to neoadjuvant chemotherapy

INTRODUCTION: Function induction problems are frequently represented by affinity measures between the elements of the inductive sample set, and kernel matrices are a well-known example of affinity measures. METHODS: The objective of the present work is to obtain information about the relations between data from a calculated kernel matrix by initially assuming that those geometric relations are consistent with known labels. To assess the relation between the data structure and the labels, a classifier based on kernel density estimation (KDE) was used. The performance of the selected width using the method presented in this paper was compared to the performance of a method described in the literature; the literature method was based on minimizing error minimization and balancing bias and variance. The main case study, which was to predict the response to neoadjuvant chemotherapy treatment, consists of evaluating whether a set of training data from genomic expression data from breast tumors and the genomic expression from the tumor of one patient can be used to determine whether there will be a pathological complete response. RESULTS: For the tested databases, the proposed method showed statistically equivalent results with the literature method; however, in some cases, the proposed method had a better overall performance when considering both large and small classes. CONCLUSION: The results demonstrate the feasibility of selecting models by directly calculating densities and the geometry from the class separation.

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. It is shown that arbitrary learning trajectories can be achieved by maintaining the sliding mode gains within their convergence intervals. Formal proofs of convergence conditions are therefore presented. The concept of trajectory learning presented in this paper goes further beyond the selection of a final state in the Pareto set, since it can be reached through different trajectories and states in the trajectory can be assessed individually against an additional objective function.

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. The new protein coding scheme uses more than one sliding window length with a weight factor that is proportional to the window length, avoiding the ambiguity problem without ignoring the identity of small subsequences Accuracy for Sequence Coding by Sliding Windows ranged from 60.1 to 77.7 percent for the first bacterium protein set and from 61.9 to 76.7 percent for the second one, whereas the accuracy for the proposed Extended-Sequence Coding by Sliding Windows scheme ranged from 70.7 to 97.1 percent for the first bacterium protein set and from 61.1 to 93.3 percent for the second one. Additionally, protein sequences classified inconsistently by the Artificial Neural Networks were analyzed by CD-Search revealing that there are some disagreement in public repositories, calling the attention for the relevant issue of error propagation in annotated databases due the incorrect transferred annotations.

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. In order to analyse this effect, we propose that the higher levels should emerge through a learning mechanism as correlations of lower level memories. According to this proposal, this paper describes a method of acquiring the inter-group synapses based on a genetic algorithm. Thus the results show that genetic algorithms are feasible as they allow the emergence of complex behaviours which could be potentially excluded in other learning process.