Interpretable feature extraction and dimensionality reduction in ESM2 for protein localization prediction.

ABSTRACT: As the application of large language models (LLMs) has broadened into the realm of biological predictions, leveraging their capacity for self-supervised learning to create feature representations of amino acid sequences, these models have set a new benchmark in tackling downstream challenges, such as subcellular localization. However, previous studies have primarily focused on either the structural design of models or differing strategies for fine-tuning, largely overlooking investigations into the nature of the features derived from LLMs. In this research, we propose different ESM2 representation extraction strategies, considering both the character type and position within the ESM2 input sequence. Using model dimensionality reduction, predictive analysis and interpretability techniques,

SUBMITTER: Luo Z 

PROVIDER: S-EPMC10818170 | biostudies-literature | 2024 Jan

REPOSITORIES: biostudies-literature