From Tokens to Cells: Foundation Models for Single-Cell Biology - Akram Baharlouei, Altos Labs
Jul 19, 2026 · 16:57
Akram Baharlouei, a machine learning engineer at Altos Labs, explains the engineering challenges of building foundation models for single-cell biology, arguing that flow matching models like PrimeFlow outperform transformer-based models for this noisy, heterogeneous data. He highlights the Yamanaka factor, discovered in 2006, which reprogrammed aged skin cells to an embryonic-like state, earning a Nobel Prize in 2012 and enabling possibilities for cellular rejuvenation medicine. Baharlouei notes that drug development takes up to 10 years with billions in cost, and AI could shorten the pipeline. He describes RNA-seq as the primary modality for training, with datasets reaching 1 billion cells, but warns that scaling alone fails without quality improvements. Benchmarking at NURIPS showed transformer models often underperform simple linear models, while flow matching models better capture the distribution of cell states, as measured by MMD scores.
