Abstract
We propose a novel transfer learning approach for orphan screening called corresponding projections. In orphan screening the learning task is to predict the binding affinities of compounds to an orphan protein, i.e., one for which no training data is available. The identification of compounds with high affinity is a central concern in medicine since it can be used for drug discovery and design. Given a set of prediction models for proteins with labelled training data and a similarity between the proteins, corresponding projections constructs a model for the orphan protein from them such that the similarity between models resembles the one between proteins. Under the assumption that the similarity resemblance holds, we derive an efficient algorithm for kernel methods We empirically show that the approach outperforms the state-of-the-art in orphan screening.
Original language | English |
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Title of host publication | ML4H: Machine Learning for Health, Workshop at NeurIPs 2018 |
Editors | Sam Finlayson, Brett K Beaulieu-Jones |
Place of Publication | San Diego CA USA |
Publisher | Neural Information Processing Systems (NIPS) |
Number of pages | 10 |
Publication status | Published - 2018 |
Externally published | Yes |
Event | Workshop on Machine Learning for Health at NeurIPs 2018 - Montreal, Canada Duration: 8 Dec 2018 → 8 Dec 2018 https://ml4health.github.io/2018/ |
Conference
Conference | Workshop on Machine Learning for Health at NeurIPs 2018 |
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Abbreviated title | ML4H |
Country/Territory | Canada |
City | Montreal |
Period | 8/12/18 → 8/12/18 |
Internet address |