An Evidence-Based Combining Classifier for Brain Signal Analysis

Nowadays, brain signals are employed in various scientific and practical fields such as Medical Science, Cognitive Science, Neuroscience, and Brain Computer Interfaces. Hence, the need for robust signal analysis methods with adequate accuracy and generalizability is inevitable. The brain signal analysis is faced with complex challenges including small sample size, high dimensionality and noisy signals. Moreover, because of the non-stationarity of brain signals and the impacts of mental states on brain function, the brain signals are associated with an inherent uncertainty. In this paper, an evidence-based combining classifiers method is proposed for brain signal analysis. This method exploits the power of combining classifiers for solving complex problems and the ability of evidence theory to model as well as to reduce the existing uncertainty. The proposed method models the uncertainty in the labels of training samples in each feature space by assigning soft and crisp labels to them. Then, some classifiers are employed to approximate the belief function corresponding to each feature space. By combining the evidence raised from each classifier through the evidence theory, more confident decisions about testing samples can be made. The obtained results by the proposed method compared to some other evidence-based and fixed rule combining methods on artificial and real datasets exhibit the ability of the proposed method in dealing with complex and uncertain classification problems.     

Lack of association between cyclooxygenase 2−765G/C gene polymorphism and breast cancer risk in Ahvaz, west-south Iran

Cyclooxygenases are key enzymes in conversion of arachidonic acid into prostaglandin H2. Cyclooxygenase-2 (COX-2) increases prostaglandins in neoplastic tissue. COX-2 has important roles in cell proliferation cancers, angiogenesis, and alzheimer. COX-2 is up-regulated in several types of cancer, and it is hypothesized that COX-2 expression may be genetically influenced. Our main objective was to evaluated the association of polymorphism COX-2 with risk of breast cancer in khouzestan province, and the second objective of the study was to evaluate the association with biochemistry parameters. This study consisting of 150 patients with breast cancer and 120 normal DNA was extracted from the white blood cells. Polymorphism cox2 gene was detected by polymerase chain reaction according to the standard methods. The profile lipids and estrogen were measured in two groups by standard methods. Chi square analysis showed that there was no association between breast cancer risk and COX-2 ?765G>C genotype and alleles. Also, no association were observed between ?765G>C polymorphism and biochemistry parameters. A multiple logistic regression model with cox2 genotypes and LDL and HDL as covariates revealed that there is no significant association between cox2 genotypes and risk of breast cancer, but higher values of LDL and HDL significantly increase risk of breast cancer.