DeepCellEss: cell line-specific essential protein prediction with attention-based interpretable deep learning.

ABSTRACT:

Motivation

Protein essentiality is usually accepted to be a conditional trait and strongly affected by cellular environments. However, existing computational methods often do not take such characteristics into account, preferring to incorporate all available data and train a general model for all cell lines. In addition, the lack of model interpretability limits further exploration and analysis of essential protein predictions.

Results

In this study, we proposed DeepCellEss, a sequence-based interpretable deep learning framework for cell line-specific essential protein predictions. DeepCellEss utilizes a convolutional neural network and bidirectional long short-term memory to learn short- and long-range latent information from protein sequences. Further, a multi-head self-at

SUBMITTER: Li Y 

PROVIDER: S-EPMC9825760 | biostudies-literature | 2023 Jan

REPOSITORIES: biostudies-literature