Gene Analytics: Discovery and Contextualization of Enriched Gene Sets
Description
The paper present a preliminary study of creative knowledge
discovery through bisociative data analysis. Bisociative reasoning is at
the heart of creative, accidental discovery (serendipity), and is focused on finding unexpected links by crossing different contexts. Contextualization
and linking between highly diverse and distributed data and knowledge
sources is therefore crucial for implementation of bisociative reasoning.
In the paper we explore these ideas on the problem of analysis of microarray
data. We show how enriched gene sets are found by using ontology
information as background knowledge in semantic subgroup discovery.
These genes are then contextualized by the computation of probabilistic
links to diverse bioinformatics resources. Results of two case studies are used to illustrate the approach.
| Slides | |
| 0:00 | Gene Analytics: Discovery and Contextualization of Enriched Gene Groups |
| 0:10 | Talk outline |
| 0:58 | Data Mining |
| 1:18 | Subgroup discovery task definition |
| 1:58 | Sample microarray analysis tasks |
| 2:34 | Relational Data Mining (Inductive Logic Programming) |
| 2:50 | Relational Data Mining (ILP) |
| 3:50 | Gene Ontology (GO) |
| 4:34 | Ontology encoded as relational background knowledge |
| 5:14 | Multi-Relational representation |
| 5:30 | Propositionalization in RDM |
| 7:14 | Gene set enrichmentanalysis with SEGS |
| 7:34 | Ontologies |
| 7:46 | Identifying differentially expressed genes in data preprocessing 1 |
| 8:22 | Ranking of differentially expressed genes |
| 8:46 | Gene expression data: Positive and negative examples for data mining |
| 9:14 | Ontology encoded as relational background knowledge + gene expression data 1 |
| 9:26 | Ontology encoded as relational features + gene expression data |
| 9:34 | Propositionalization |
| 10:10 | Propositional subgroup discovery |
| 10:34 | Summary: SEGS Method and Results |
| 10:46 | SEGS implementation |
| 10:58 | BISON project |
| 11:18 | SEGS implementation |
| 11:54 | BISON project |
| 12:58 | Heterogeneous data sources(BISON, M. Berthold, 2008) |
| 13:10 | Bridging concepts (BISON, M. Berthold, 2008) |
| 13:18 | Use Case: Glioma Cancer(investigated at NIB) |
| 13:50 | Glioma treatment |
| 14:30 | Gene Analytics |
| 14:54 | SEGS+Biomine Methodology |
| 15:42 | Biomine |
| 16:22 | Biomine Information fusion |
| 16:30 | SEGS+Biomine Methodology |
| 17:18 | Biomine: Bisociative link discovery |
| 17:38 | SEGS+Biomine Information fusion |
| 17:46 | SEGS+Biomine Creative knowledge discovery |
| 17:50 | SEGS+Biomine Creative link discovery |
| 17:58 | SEGS+Biomine Exploration and explanation |
| 18:02 | Summary |
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