Abstract: Gastric Cancer (GC) has high morbidity and fatality
rate in various countries. It is still one of the most frequent and
deadly diseases. Gastrokine1 (GKN1) and gastrokine2 (GKN2) genes
are highly expressed in the normal stomach epithelium and play
important roles in maintaining the integrity and homeostasis of
stomach mucosal epithelial cells. In this study, 47 paired samples that
were grouped according to the types of gastric cancer and the clinical
characteristics of the patients, including gender and average of age.
They were investigated with gene expression analysis and mutation
screening by monitoring RT-PCR, SSCP and nucleotide sequencing
techniques. Both GKN1 and GKN2 genes were observed significantly
reduced found by (Wilcoxon signed rank test; p
Abstract: Kidney cancer is the most lethal urological cancer
accounting for 3% of adult malignancies. VHL, a tumor-suppressor
gene, is best known to be associated with renal cell carcinoma
(RCC). The VHL functions as negative regulator of hypoxia inducible
factors. Recent sequencing efforts have identified several novel
frequent mutations of histone modifying and chromatin remodeling
genes in ccRCC (clear cell RCC) including PBRM1 and SETD2. The
PBRM1 gene encodes the BAF180 protein, which involved in
transcriptional activation and repression of selected genes. SETD2
encodes a histone methyltransferase, which may play a role in
suppressing tumor development. In this study, RNAs of 30 paired
tumor and normal samples that were grouped according to the types
of kidney cancer and clinical characteristics of patients, including
gender and average age were examined by RT-PCR, SSCP and
sequencing techniques. VHL, PBRM1 and SETD2 expressions were
relatively down-regulated. However, statistically no significance was
found (Wilcoxon signed rank test, p>0.05). Interestingly, no mutation
was observed on the contrary of previous studies. Understanding the
molecular mechanisms involved in the pathogenesis of RCC has
aided the development of molecular-targeted drugs for kidney cancer.
Further analysis is required to identify the responsible genes rather
than VHL, PBRM1 and SETD2 in kidney cancer.
Abstract: Travelling salesman problem (TSP) is a combinational
optimization problem and solution approaches have been applied
many real world problems. Pure TSP assumes the cities to visit are
fixed in time and thus solutions are created to find shortest path
according to these point. But some of the points are canceled to visit
in time. If the problem is not time crucial it is not important to
determine new routing plan but if the points are changing rapidly and
time is necessary do decide a new route plan a new approach should
be applied in such cases. We developed a route plan transfer method
based on transfer learning and we achieved high performance against
determining a new model from scratch in every change.
Abstract: The main goal of data mining is to extract accurate, comprehensible and interesting knowledge from databases that may be considered as large search spaces. In this paper, a new, efficient type of Genetic Algorithm (GA) called uniform two-level GA is proposed as a search strategy to discover truly interesting, high-level prediction rules, a difficult problem and relatively little researched, rather than discovering classification knowledge as usual in the literatures. The proposed method uses the advantage of uniform population method and addresses the task of generalized rule induction that can be regarded as a generalization of the task of classification. Although the task of generalized rule induction requires a lot of computations, which is usually not satisfied with the normal algorithms, it was demonstrated that this method increased the performance of GAs and rapidly found interesting rules.
Abstract: In this study, fuzzy rule-based classifier is used for the
diagnosis of congenital heart disease. Congenital heart diseases are
defined as structural or functional heart disease. Medical data sets
were obtained from Pediatric Cardiology Department at Selcuk
University, from years 2000 to 2003. Firstly, fuzzy rules were
generated by using medical data. Then the weights of fuzzy rules
were calculated. Two different reasoning methods as “weighted vote
method" and “singles winner method" were used in this study. The
results of fuzzy classifiers were compared.
Abstract: This study analyzes the effect of discretization on
classification of datasets including continuous valued features. Six
datasets from UCI which containing continuous valued features are
discretized with entropy-based discretization method. The
performance improvement between the dataset with original features
and the dataset with discretized features is compared with k-nearest
neighbors, Naive Bayes, C4.5 and CN2 data mining classification
algorithms. As the result the classification accuracies of the six
datasets are improved averagely by 1.71% to 12.31%.