Combining Machine Learning and External Knowledge for Analyzing Gene Expression Profiles
Gene expression is the cell process by which information from specific sections of the DNA, i.e. genes, is used to synthesized functional products like proteins, which are catalyzing the metabolic processes in our cells. Analyzing gene expression profiles is of particular interest for researchers, as they provide insights on cell processes and gene functions and can thus improve disease diagnosis and treatment.
Nowadays, gene expression profiles from several thousand genes of several hundred tissue samples can be generated. These data sets require computational tools applying Machine Learning techniques for a meaningful analysis. On the other hand, there exist many publicly available databases containing curated biomedical information, e.g. on protein-disease interactions. Contact: Cindy Perscheid
Topic: Association Rule Mining on Gene Expression Data (Contact: Cindy Perscheid)
Association Rule Mining, or Itemset Mining, is applied on gene expression data to identify correlations between the expression levels of different genes. A derived rule would have the form of GeneA (up) —> GeneB (up), meaning that if GeneA is upregulated, then typically GeneB is upregulated as well. This information helps researchers to derive unknown gene functions and better understand regulatory processes in cells for different disease types. The amount of rules resulting from those analyses are typically filtered with standard interestingness measures, e.g. support and confidence. These measures are driven by statistical analyses of the data sets. However, the interestingness of a rule for gene expression data should also take into account its biological relevance, which can only be derived from external sources. There exist multiple knowledge bases with curated information on gene-gene/gene-disease associations that are publicly available. Your task will be to apply association rule mining to gene expression data and adapt the approach in a way to make it computation feasible for those high-dimensional data sets and to consider biological context information in the computation. You will implement and test your approach with publicly available real-world data and compare the results to state-of-the-art approaches.