CLOOME: contrastive learning unlocks bioimaging databases for queries with chemical structures.

ABSTRACT: The field of bioimage analysis is currently impacted by a profound transformation, driven by the advancements in imaging technologies and artificial intelligence. The emergence of multi-modal AI systems could allow extracting and utilizing knowledge from bioimaging databases based on information from other data modalities. We leverage the multi-modal contrastive learning paradigm, which enables the embedding of both bioimages and chemical structures into a unified space by means of bioimage and molecular structure encoders. This common embedding space unlocks the possibility of querying bioimaging databases with chemical structures that induce different phenotypic effects. Concretely, in this work we show that a retrieval system based on multi-modal contrastive learning is capable of ident

SUBMITTER: Sanchez-Fernandez A 

PROVIDER: S-EPMC10643690 | biostudies-literature | 2023 Nov

REPOSITORIES: biostudies-literature