This review provides a comprehensive examination of single-cell foundation models (scFMs), large-scale AI systems pretrained on massive single-cell datasets that are revolutionizing cellular biology and drug discovery.
This article provides a comprehensive overview of tokenization strategies that enable artificial intelligence to interpret single-cell genomic data.
Single-cell foundation models (scFMs) represent a paradigm shift in computational biology, leveraging large-scale pretraining on millions of cells to develop emergent capabilities for downstream biological tasks.
Single-cell foundation models (scFMs) are transformative AI tools trained on millions of single-cell datasets to decipher the complex language of cellular biology.
Self-supervised learning (SSL) is revolutionizing the analysis of single-cell RNA sequencing (scRNA-seq) data by enabling the extraction of meaningful biological representations from vast, unlabeled datasets.
The integration of transformer architectures into single-cell biology is revolutionizing how we interpret complex cellular systems.
This article provides a comprehensive comparison of Short Tandem Repeat (STR) profiling and isoenzyme analysis for cell line authentication, essential for researchers, scientists, and drug development professionals.
This article provides a systematic review for researchers and drug development professionals on the comparative efficacy of ethanol and sodium hypochlorite (bleach) as surface disinfectants.
This article provides a systematic comparison of contamination risks associated with single-use and reusable systems in biopharmaceutical manufacturing and clinical settings.
This article provides a systematic framework for researchers, scientists, and drug development professionals to validate and optimize sterilization protocols in cell culture systems.