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Research in systems biology integrates experimental, theoretical, and modeling techniques to study and understand biological processes such as gene regulation. The genomic sequences for human and other model organisms such as yeast and bacteria are already established. The next major step is to discover functional roles of genes whose functions are not yet discovered and to investigate how genes interact...
Linear Linkage Encoding (LLE) is a powerful encoding scheme utilized when genetic algorithms (GAs) are applied to grouping problems. It discards the redundancy of other traditional encoding schemes. However, some genetic operators are quite costly in terms of computational time when LLE is utilized. In this study, two supplementary encoding schemes Linear Linkage Encoding with Ending Node Links (LLE-e)...
This paper studies the suitability of Extreme Learning Machines (ELM) for resolving bioinformatic and biomedical classification problems. In order to test their overall performance, an experimental study is presented based on five gene microarray datasets found in bioinformatic and biomedical domains. The Fast Correlation-Based Filter (FCBF) was applied in order to identify salient expression genes...
Classification in imbalanced domains has become one of the most relevant problems within the area of Machine Learning at the present. This problem has raised in significance due to its presence in many real applications and it occurs when the distribution of the available examples to carry out the learning process is very different between the classes (often for binary class data-sets). Usually, the...
Visualization techniques provide attractive tools to explore and analyze huge and high dimensional gene expression sets. Several visualization techniques have been developed that enabled users to visually analyze high dimensional data. However, these techniques should be integrated with efficient exploration techniques, as efficient clustering, outlier analysis, ensembles and cluster validation to...
Techniques exist to synthesize software architecture using genetic algorithms that employ transformations based on mutations and crossover. In this paper, we demonstrate that complementary crossover can significantly improve this technique. We study two versions of complementary crossover, one in which parents are selected so that they complement each other but the genes are inherited randomly from...
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