Trans-channel fluorescence learning improves high-content screening for Alzheimer's disease therapeutics.

ABSTRACT: In microscopy-based drug screens, fluorescent markers carry critical information on how compounds affect different biological processes. However, practical considerations, such as the labor and preparation formats needed to produce different image channels, hinders the use of certain fluorescent markers. Consequently, completed screens may lack biologically informative but experimentally impractical markers. Here, we present a deep learning method for overcoming these limitations. We accurately generated predicted fluorescent signals from other related markers and validated this new machine learning (ML) method on two biologically distinct datasets. We used the ML method to improve the selection of biologically active compounds for Alzheimer's disease (AD) from a completed high-content hig

SUBMITTER: Wong DR 

PROVIDER: S-EPMC9585544 | biostudies-literature | 2022 Jun

REPOSITORIES: biostudies-literature