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MLG - 2008: 6th International Workshop on Mining and Learning with Graphs

Biomine search engine for probabilistic graphs

author: Hannu Toivonen, Department of Computer Science, University of Helsinki

Description

Biomine is a search engine prototype under development. It can be used to find biological entities that are (indirectly) related to given query entities, as well as to display and evaluate the relations. Biomine is based on an integrated index to a number of public biological databases. The representation is a probabilistic graph, where nodes correspond to biological entities (typically a record in a biological database) and edges to their relationships (typically a cross-reference between database records). Edges are annotated with probabilities that reflect the strength or the reliability of the relation. I will discuss research problems and challenges for search in such graphs.

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Slides
0:00 Biomine search engine for probabilistic graphs
0:35 What is known about PSEN1 (presenilin1) gene?
0:50 PSEN1 (presenilin1) gene
4:36 Biomine: Search in biological graphs
5:51 Biomine graph schema
6:18 Databases and nodes indexed by Biomine
7:08 Probabilistic graphs
8:04 Connectivity between nodes
9:26 Measures of connectivity
9:51 Probabilistic graphs
10:35 Measures of connectivity
11:32 Properties of reliability
14:15 Notes on computation
15:20 Origin of probabilities in Biomine
15:25 Two search problems
16:23 Neighborhood query - 1
16:34 Neighborhood query - 2
16:39 Neighborhood query - 3
16:40 Are neighborhood queries useful? - 1
18:40 Prediction of missing protein interactions
19:54 Prediction of future gene interactions
21:04 Are neighborhood queries useful? - 2
21:56 The most reliable subgraph problem
23:42 How are genes PSEN1 (presenilin1) and APOE (apolipoprotein E) related?
23:54 Relation between genes PSEN1 (presenilin1) and APOE (apolipoprotein E)
27:18 How are genes PSEN1 (presenilin1) and DYX1C1 (apolipoprotein E) possibly related?
27:28 Possible relation between genes PSEN1 (presenilin1) and DYX1C1 (apolipoprotein E)
30:09 Subgraph extraction
32:04 Two new incremental methods
33:39 Quality of the extracted subgraph - 1
35:19 Time to extract a subgraph - 1
36:04 Quality of the extracted subgraph - 2
36:24 Time to extract a subgraph - 2
36:47 Slide 33/44
37:33 Subgraph extraction problem
38:24 Example: paths from ACHB3_HUMAN to AD
40:27 Subgraph queries with context-free grammars - 1
40:48 Subgraph queries with context-free grammars - 2
41:51 ProbLog
42:38 ProbLog semantics
43:25 ProbLog inference
44:02 Compression of ProbLog programs
45:40 - Questions

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