Foundation model for efficient biological discovery in single-molecule data.

ABSTRACT: Modern data-intensive techniques offer ever deeper insights into biology, but render the process of discovery increasingly complex. For example, exploiting the unique ability of single-molecule fluorescence microscopy (SMFM)^1-5. to uncover rare but critical intermediates often demands manual inspection of time traces and iterative ad hoc approaches that are difficult to systematize. To facilitate systematic and efficient discovery from SMFM data, we introduce META-SiM, a transformer-based foundation model pre-trained on diverse SMFM analysis tasks. META-SiM achieves high performance-rivaling best-in-class algorithms-on a broad range of analysis tasks including trace selection, classification, segmentation, idealization, and stepwise photobleaching analysis. Additionally,

SUBMITTER: Li J 

PROVIDER: S-EPMC11383305 | biostudies-literature | 2024 Aug

REPOSITORIES: biostudies-literature