What is Computational Biology? Introduction to biological data handling. High-throughput processes. Genome Databases. Introduction to the notion of an algorithm.
Using R - Data Input and Output
An introductory lecture to the analysis of sequence composition. Why nucleotides and oligonucleotides may be seen as "words" in the genomic text. Introduction to the notions of probability and probability distributions.
Using the observation of co-occurrences in genomic sequences as a starting point we will be discussing the basic properties of Markovian Processes and Markov Models. There will be a short introduction into Hidden Markov Models, their training and implementation.
Using the observation of co-occurrences in genomic sequences as a starting point we will be discussing the basic properties of Markovian Processes and Markov Models. There will be a short introduction into Hidden Markov Models, their training and implementation.
Using the observation of co-occurrences in genomic sequences as a starting point we will be discussing the basic properties of Markovian Processes and Markov Models. There will be a short introduction into Hidden Markov Models, their training and implementation.
Using the observation of co-occurrences in genomic sequences as a starting point we will be discussing the basic properties of Markovian Processes and Markov Models. There will be a short introduction into Hidden Markov Models, their training and implementation.
Using the observation of co-occurrences in genomic sequences as a starting point we will be discussing the basic properties of Markovian Processes and Markov Models. There will be a short introduction into Hidden Markov Models, their training and implementation.
Using the observation of co-occurrences in genomic sequences as a starting point we will be discussing the basic properties of Markovian Processes and Markov Models. There will be a short introduction into Hidden Markov Models, their training and implementation.
NGS Data Analysis
Genomics Basics. Genome Browsers and Genomic Formats
Introduction to NGS - Methodology and Applications
An introductory lecture to the analysis of sequence composition. Why nucleotides and oligonucleotides may be seen as "words" in the genomic text. Introduction to the notions of probability and probability distributions.
Using the observation of co-occurrences in genomic sequences as a starting point we will be discussing the basic properties of Markovian Processes and Markov Models. There will be a short introduction into Hidden Markov Models, their training and implementation.
Using the observation of co-occurrences in genomic sequences as a starting point we will be discussing the basic properties of Markovian Processes and Markov Models. There will be a short introduction into Hidden Markov Models, their training and implementation.
RNASeq Data analysis - Differential Expression
Using the observation of co-occurrences in genomic sequences as a starting point we will be discussing the basic properties of Markovian Processes and Markov Models. There will be a short introduction into Hidden Markov Models, their training and implementation.
Functional Analysis and Positional Enrichments
Using the observation of co-occurrences in genomic sequences as a starting point we will be discussing the basic properties of Markovian Processes and Markov Models. There will be a short introduction into Hidden Markov Models, their training and implementation.
Whole Exome Sequencing - Variant detection and Gene Prioritization
Using the observation of co-occurrences in genomic sequences as a starting point we will be discussing the basic properties of Markovian Processes and Markov Models. There will be a short introduction into Hidden Markov Models, their training and implementation.
Methods for Data Analysis
Introduction to Data Science
Describing Data. Basic Statistics
An introductory lecture to the analysis of sequence composition. Why nucleotides and oligonucleotides may be seen as "words" in the genomic text. Introduction to the notions of probability and probability distributions.
Principal Component Analysis and Dimensionality Reduction
Using the observation of co-occurrences in genomic sequences as a starting point we will be discussing the basic properties of Markovian Processes and Markov Models. There will be a short introduction into Hidden Markov Models, their training and implementation.
Classification Problems - Regression
Using the observation of co-occurrences in genomic sequences as a starting point we will be discussing the basic properties of Markovian Processes and Markov Models. There will be a short introduction into Hidden Markov Models, their training and implementation.
Clustering
Using the observation of co-occurrences in genomic sequences as a starting point we will be discussing the basic properties of Markovian Processes and Markov Models. There will be a short introduction into Hidden Markov Models, their training and implementation.
Advanced Classification Methods (SVM, Neural Networks and Decision Trees)
Using the observation of co-occurrences in genomic sequences as a starting point we will be discussing the basic properties of Markovian Processes and Markov Models. There will be a short introduction into Hidden Markov Models, their training and implementation.
Genome Structure, Architecture and Epigenomics
Genomics Basics
Sequence to Structure to Function.
An introductory lecture to the analysis of sequence composition. Why nucleotides and oligonucleotides may be seen as "words" in the genomic text. Introduction to the notions of probability and probability distributions.