Work

CELL-E: A Text-To-Image Transformer for Protein Localization Prediction

Year: 2022

Type: preprint

Abstract: Accurately predicting cellular activities of proteins based on their primary amino acid sequences would greatly improve our understanding of the proteome. In this paper, we present CELL-E, a text-to-image transformer architecture that generates a 2D probability density map of protein distribution within cells. Given a amino acid sequence and a reference image for cell or nucleus morphology, CELL-E offers a more direct representation of protein localization, as opposed to previous in silico methods that rely on pre-defined, discrete class annotations of protein localization to subcellular compartments.

Source: bioRxiv (Cold Spring Harbor Laboratory)

DOI: 10.1101/2022.05.27.493774

Authors: Emaad Khwaja, Yun S. Song, Bo Huang

Institutions: University of California, San Francisco, University of California, Berkeley, Chan Zuckerberg Biohub San Francisco

Language: English

Topic: Machine Learning in Bioinformatics

Subfield: Molecular Biology

Field: Biochemistry, Genetics and Molecular Biology

Domain: Life Sciences

Open Access status: green

OpenAlex ID: W4282040929

Cites: 60

Cited by: 3

Related to: 10