Hierarchical Multilabel Classification Trees for Gene Function Prediction
author:
Hendrik Blockeel,
Katholieke Universiteit Leuven
Description
Prediction of gene function is a so-called hierarchical multilabel classification (HMC) task: a single instance can be labelled with multiple classes rather than just one (i.e., a gene can have multiple functions), and these classes are organized in a hierarchy. Many machine learning methods focus on learning predictive models with a single target variable. One can then learn to predict all classes separately and combine the predictions afterwards.
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| Slides | |
| 0:00 | Hierarchical Multilabel Classification Trees for Gene Function Prediction |
| 0:17 | Overview |
| 0:43 | Gene Function Prediction |
| 1:32 | Machine Learning |
| 2:07 | Hierarchical Multilabel Classification |
| 2:49 | Gene Function Prediction (a) |
| 3:02 | Two HMC Approaches |
| 4:34 | Two HMC Approaches (c’ted) |
| 5:53 | Related Work on HMC |
| 6:30 | Two HMC Approaches (a) |
| 6:39 | Related Work on HMC (a) |
| 7:33 | Why Decision Trees? |
| 9:33 | Decision Trees for HMC |
| 10:29 | Decision Trees for HMC (c’ted) |
| 11:01 | Experiments in Yeast Functional Genomics |
| 11:33 | Example Run |
| 15:18 | Comparison of Clus-HMC with [Clare03] |
| 17:05 | Extracting Rules |
| 17:57 | HMC vs. Single Classification pt 1 |
| 19:17 | HMC vs. Single Classification pt 2 |
| 19:53 | Explanation of the Results |
| 20:55 | Conclusions |
| 21:33 | Further Work |
| 23:37 | Questions? |
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