Biomarker Discovery


Repository is empty


No polls currently selected on this page!

Biomarker Discovery

Code: 239991
ECTS: 4.0
Lecturers in charge:
Take exam: Studomat

1. komponenta

Lecture typeTotal
Lectures 30
* Load is given in academic hour (1 academic hour = 45 minutes)
The course is designed to provide an approach to understanding the use of biomarkers along the entire spectrum of disease from the earliest manifestation to the terminal stages. Through this course students will get familiar with applications in observational and analytical epidemiology, randomized clinical trials, screening, prognosis as well as diagnosis and therapy. Following successful completion of the course, the student will have acquired knowledge and capacity to understand:

1. discovery, development, validation, and implementation of biomarkers for some of the main brain diseases, and
2. statistical methods used in analysis of biomarker performance and efficiency for early detection and differential diagnosis of disease.

1. Similarities and differences between biomarkers of state (exposure) and biomarkers of disease,
2. Identification of biomarker signatures for early events in natural history and classification of human disease; target selection and validation,
3. Delineation of events between exposure and disease: establishment of dose-response, identification of mechanisms by which exposure and disease are related, reduction in misclassification of exposure or risk factors, identification of stratification markers,
4. Factor analysis and calculation of factor scores,
5. Analysis of biomarker's sensitivity, specificity, analysis of receiver operating characteristic (ROC) space, correlation with disease progression), and standardization,
6. Marker assay validation and pre-analytical and analytical biomarker variability: target range, standards, quality control, stability and matrix effect (for amyloid beta and tau proteins), calibration curve model selection, precision, bias, quantification (detection) limits, confounding variables, evaluation, and weighting,
7. Use of sample controls for in-study performance monitoring and conformance testing among different laboratories, analysis using Passing-Bablok method,
8. Factors needed to be considered for successful implementation of biomarkers in the clinic: from preclinical feasibility to clinical utility: examples from cases studies from internal and external data.
1. semester
Elective courses 1, 2 - Regular study - Biomedical Mathematics

2. semester
Elective courses 1, 2 - Regular study - Biomedical Mathematics
Consultations schedule: