Bioinformatics in genetics
1
2023-2024
02037958
Laboratorial Genetics
Portuguese
English
Face-to-face
SEMESTRIAL
4.0
Compulsory
2nd Cycle Studies - Mestrado
Recommended Prerequisites
Knowledge of biology, biochemistry and statistics in the pre graduate cycle of courses in the scope of Biomedical Sciences .
Teaching Methods
Lecturing, discussing and resolving practical problems.
Learning Outcomes
Recognize the organization and difficulties associated to large genetic data sets;
Analyse tools for genomes alignment
Analyse tools for variant calls and data base anotation
Understand the need for data reduction in large genetic data sets;
Acquire the fundamentals of data reduction techniques;
Use software tools to build classification statistical models applied to the relation genotype/phenotype.
Work Placement(s)
NoSyllabus
Very large data sets.
Genomes alignment
Tools for variant calls and data base anotation
Data reduction methods. Principal component analysis. Clustering techniques.
Genomic wide association studies resorting to classification algorithms.
Head Lecturer(s)
Francisco José Santiago Fernandes Amado Caramelo
Assessment Methods
Assessment
The evaluation consists of a multiple-choice questions and/or others types : 100.0%
Bibliography
Shai Shalev-Shwartz and Shai Ben-David. Understanding Machine Learning: From Theory to Algorithms. Cambridge University Press, 1edition, May 2014
James, G.; Witten, D.; Hastie, T. & Tibshirani, R. An Introduction to Statistical Learning: With Applications in R Springer Publishing Company, Incorporated, 2014
Design, Analysis, and Interpretation of Genome-Wide Association Scans, Daniel O. Stram, Springer
Genome-Wide Association Studies and Genomic Prediction, Gondro, Cedric, van der Werf, Julius, Hayes, Ben (Eds.), Springer.