Abstract
Currently, bioimaging databases cannot be queried by chemical structures that induce the phenotypic effects captured by the image. We present a novel retrieval system
based on contrastive learning that is able to identify the chemical structure inducing the phenotype out of ~2,000 candidates with a top-1 accuracy >70 times higher than a random baseline.
| Original language | English |
|---|---|
| Title of host publication | Neural Information Processing Systems Foundation (NeurIPS 2022) |
| Number of pages | 1 |
| Publication status | Published - 2022 |
Fields of science
- 305907 Medical statistics
- 202017 Embedded systems
- 202036 Sensor systems
- 101004 Biomathematics
- 101014 Numerical mathematics
- 101015 Operations research
- 101016 Optimisation
- 101017 Game theory
- 101018 Statistics
- 101019 Stochastics
- 101024 Probability theory
- 101026 Time series analysis
- 101027 Dynamical systems
- 101028 Mathematical modelling
- 101029 Mathematical statistics
- 101031 Approximation theory
- 102 Computer Sciences
- 102001 Artificial intelligence
- 102003 Image processing
- 102004 Bioinformatics
- 102013 Human-computer interaction
- 102018 Artificial neural networks
- 102019 Machine learning
- 102032 Computational intelligence
- 102033 Data mining
- 305901 Computer-aided diagnosis and therapy
- 305905 Medical informatics
- 202035 Robotics
- 202037 Signal processing
- 103029 Statistical physics
- 106005 Bioinformatics
- 106007 Biostatistics
JKU Focus areas
- Digital Transformation