PURPOSE Personalized medicine is predicated on the concept of identifying subgroups of a common disease for better treatment. Identifying biomarkers that predict disease subtypes has been a major focus of biomedical science. In the era of genome-wide profiling, there is controversy as to the optimal number of genes as an input of a feature selection algorithm for survival modeling. PATIENTS A...

Identifying disease genes is very important not only for better understanding of gene function and biological process but also for human medical improvement. Many computational methods have been proposed based on the similarity between all known disease genes (seed genes) and candidate genes in the entire gene interaction network. Under the hypothesis that potential disease-related genes should...

abstract           introduction: genetic variations in the gene encoding endothelial nitric oxide synthase (enos) enzyme affect the susceptibility to cardiovascular disease. identification of the way these changes affect enos structure and function in laboratory conditions is difficult and time-consuming. thus, it seems essential to perform bioinformatics studies prior to laboratory studies to ...

Sickle cell disease and its variants constitute the most common inherited blood disorders affecting millions of individuals worldwide. Significant information regarding nature genetic mutations modifier genes that result in increased or decreased severity are available. In recent years, detailed data molecular genetics, pathophysiology, mechanisms for development symptoms side effects sickle ha...

Background Alzheimer’s disease is the leading cause of brain dementia, along with which substantial failure organs and mental issues arise. The abundance AD related data in current decade has allowed for much advancement field using modern machine learning deep techniques to decrypt pathology. Though diagnostic tools have been modeled over such large expanses data, black box problem decoding si...

An increasing amount of genomic data has become available. The work deals with class prediction with highdimensional gene expression data. Combining gene expression data with other data can improve the prediction of disease prognosis. The main part of the work is aimed at combining gene expression data with clinical data. We use logistic regression models that can be built through various regul...