This study, through a phylogenetic analysis, is aimed to identify potential epidemiological networks and sequence interrelationships between acute/early and chronic infections in both drug-naïve and drug-experienced individuals within a local, well-defined setting and to investigate the population dynamics of transmitted resistance and the potential contribution of untreated patients to the spr...

in today world of internet, it is important to feedback the users based on what they demand. moreover, one of the important tasks in data mining is classification. today, there are several classification techniques in order to solve the classification problems like genetic algorithm, decision tree, bayesian and others. in this article, it is attempted to classify researchers to “expert” and “no...

A novel semi-naive Bayesian classifier is introduced that is particularly suitable to data with many attributes. The naive Bayesian classifier is taken as a starting point and correlations are reduced through joining of highly correlated attributes. Our technique differs from related work in its use of kernel-functions that systematically include continuous attributes rather than relying on dis...

BACKGROUND Mammalian target of rapamycin (mTOR) is a central controller of cell growth, proliferation, metabolism, and angiogenesis. Thus, there is a great deal of interest in developing clinical drugs based on mTOR. In this paper, in silico models based on multi-scaffolds were developed to predict mTOR inhibitors or non-inhibitors. METHODS First 1,264 diverse compounds were collected and cat...

Single nucleotide polymorphisms (SNPs) contribute most of the genetic variation to the human genome. SNPs associate with many complex and common diseases like Alzheimer's disease (AD). Discovering SNP biomarkers at different loci can improve early diagnosis and treatment of these diseases. Bayesian network provides a comprehensible and modular framework for representing interactions between gen...

Classification is one of the most popular data mining tasks with a wide range of applications, and lots of algorithms have been proposed to build accurate and scalable classifiers. Most of these algorithms only take a single table as input, whereas in the real world most data are stored in multiple tables and managed by relational database systems. As transferring data from multiple tables into...