Data analysis - Cluster identification using hierarchical clustering (HCA) and K-means.

Data Analysis - Cluster characterization and identification of regulatory associations (using gene ontology (GO) terms annotating the genes in the cluster and transcription factors (TFs) regulating the genes in the cluster).

1) Graphs and DNA sequencing

Introduction to DNA sequencing.

Fragment assembling and the Shortest Superstring Problem (SSP). Solving the SSP problem using graphs.

Sequencing by Hybridization (SBH) problem. Solving SBH problem using graphs:

- SBH: Hamiltonian Path Approach.

- SBH: Eulerian Path Approach.

2) Examples of graph application to biological network analysis.

Data analysis - Cluster identification using hierarchical clustering (HCA) and K-means.

Data Analysis - Cluster characterization and identification of regulatory associations (using gene ontology (GO) terms annotating the genes in the cluster and transcription factors (TFs) regulating the genes in the cluster).

Data analysis - Cluster identification using hierarchical clustering (HCA) and K-means.

Data Analysis - Cluster characterization and identification of regulatory associations (using gene ontology (GO) terms annotating the genes in the cluster and transcription factors (TFs) regulating the genes in the cluster).

Introduction to Graph Theory.

Eulerian & Hamiltonian Cycle Problems.

Benzer Experiment and Internal Graphs.