No abstract

No abstract

No abstract

Developed software tool is available at http://ce.istanbul.edu.tr/bioinformatics/hiv1/.

Despite the effectiveness of nucleoside/nucleotide analogues in the treatment of chronic hepatitis B (CHB), their long-term administration is associated with the emergence of resistant hepatitis B virus (HBV) mutants. In this study, mutations resulting in antiviral resistance in HBV DNA samples isolated from 23 CHB patients (nine treatment naïve and 14 treated previously) were studied using a line probe assay (INNO-LiPA HBV DR; Innogenetics) and ultradeep pyrosequencing (UDPS) methods. Whilst the INNO-LiPA HBV DR showed no resistance mutations in HBV DNA samples from treatment-naive patients, mutations mediating lamivudine resistance were detected in three samples by UDPS. Among patients who were treated previously, 19 mutations were detected in eight samples using the INNO-LiPA HBV DR and 29 mutations were detected in 12 samples using UDPS. All mutations detected by the INNO-LiPA HBV DR were also detected by UDPS. There were no mutations that could be detected by INNO-LiPA HBV DR but not by UDPS. A total of ten mutations were detected by UDPS but not by INNO-LiPA HBV DR, and the mean frequency of these mutations was 14.7 %. It was concluded that, although INNO-LiPA HBV DR is a sensitive and practical method commonly used for the detection of resistance mutations in HBV infection, UDPS may significantly increase the detection rate of genotypic resistance in HBV at an early stage.

No abstract

No abstract

Parkinson's Disease (PD) is a neurodegenerative motor system disorder, which also causes vocal impairments for most of its patients. A number of recent exploratory studies have evaluated the feasibility of detecting voice disorders by applying data mining tools to acoustic features extracted from speech recordings of patients. Selection of a minimal yet descriptive set of features is crucial for improving the classifier generalisation capability and interpretability of the classification model as well as for reducing the burden of data preprocessing. We propose a hybrid of feature selection and cross-validation procedures to lower the bias in the assessment of classifier accuracy.