Synthetic augmentation of cancer cell line multi-omic datasets using unsupervised deep learning.

Integrating diverse types of biological data is essential for a holistic understanding of cancer biology, yet it remains challenging due to data heterogeneity, complexity, and sparsity. Addressing this, our study introduces an unsupervised deep learning model, MOSA (Multi-Omic Synthetic Augmentation), specifically designed to integrate and augment the Cancer Dependency Map (DepMap). Harnessing orthogonal multi-omic information, this model successfully generates molecular and phenotypic profiles, resulting in an increase of 32.

Sea-level rise and extreme Indian Ocean Dipole explain mangrove dieback in the Maldives.

Mangrove forests enhance Small Island Developing States' resilience to climate change, yet in 2020, a mangrove dieback impacted ~ 25% of mangrove-containing islands in the Maldives. Using remote sensing, dendrology and sediment geochemistry, we document a significant decrease in mangrove health post-2020 (NDVI: 0.75 ± 0.

Base editing screens define the genetic landscape of cancer drug resistance mechanisms.

Drug resistance is a principal limitation to the long-term efficacy of cancer therapies. Cancer genome sequencing can retrospectively delineate the genetic basis of drug resistance, but this requires large numbers of post-treatment samples to nominate causal variants. Here we prospectively identify genetic mechanisms of resistance to ten oncology drugs from CRISPR base editing mutagenesis screens in four cancer cell lines using a guide RNA library predicted to install 32,476 variants in 11 cancer genes.