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In modern proteomics, prediction of protein–protein interactions (PPIs) is a key research line, as these interactions take part in most essential biological processes. In this paper, a new approach is proposed to PPI data classification based on the extraction of genomic and proteomic information from well-known databases and the incorporation of semantic measures. This approach is carried out through...
Manual visualization-based sleep stage classification is a time-consuming task prone to errors. Since the correct identification of sleep stages is vital for the correct identification of sleep disorders and for the research in this field in general, there is a growing demand for efficient automatic classification methods. However, there is still no symbolic representation of the biomedical signals...
This paper reports the investigations and experimental procedures conducted for designing an automatic sleep classification tool basedconly in the features extracted with wavelets from EEG, EMG and EOG (electro encephalo-mio- and oculo-gram) signals, without any visual aid or context-based evaluation. Real data collected from infants was processed and classified by several traditional and bio-inspired...
This work presents a feasible solution to the problem of book losses prediction from financial and general data in companies. The specific problem tackled in this work corresponds to a real dataset of Spanish companies. A Mutual Information-based criterion has been applied in order to reduce the initial set of variables, and a Support Vector Machine classifier has been designed to perform the prediction...
Affymetrix High Oligonucleotide expression arrays are widely used for the high-throughput assessment of gene expression of thousands of genes simultaneously. Although disputed by several authors, there are non-biological variations and systematic biases that must be removed as much as possible through the pre-processing step before an absolute expression level for every gene is assessed. It is important...
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