ArticleBriefings in bioinformatics2024
Interpretable feature extraction and dimensionality reduction in ESM2 for protein localization prediction.
Article in Briefings in bioinformatics, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 32 papers, 1 of them a synthesis that pooled it.
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Who cites it
32 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Protein Sequence Analysis landscape: A Systematic Review of Task Types, Databases, Datasets, Word Embeddings Methods, and Language Models.Database : the journal of biological databases and curation · 2025Pooled it
- Construction of a multi-label odor prediction model based on molecular structures and olfactory receptor binding profiles with a novel interpretability framework.Analytical sciences : the international journal of the Japan Society for Analytical Chemistry · 2026Article
- PEPE: scalable extraction of multi-modal protein language model representations.Bioinformatics (Oxford, England) · 2026Article
- KSDiffusion: conditional diffusion for kinase-specific phosphorylation site prediction under data-limited and imbalanced regimes.Briefings in bioinformatics · 2026Article
- A survey of downstream applications of evolutionary scale modeling protein language models.Quantitative biology (Beijing, China) · 2026Review
- PBP_ICBA: a prediction of bacterial promoters in specific organisms using an improved convolutional block attention module.Journal of computer-aided molecular design · 2026Article
- Classification of virulence factors based on dual-channel neural networks with pre-trained language models.PloS one · 2026Article
- Interpretable Transfer Learning for Cancer Drug Resistance: Candidate Target Identification.Current issues in molecular biology · 2025Article
- Paying attention to attention: High attention sites as indicators of protein family and function in language models.PLoS computational biology · 2025Article
- VF-Fuse: a dual-path feature fusion and iterative update architecture for virulence factor prediction.Briefings in bioinformatics · 2025Article
- Highly accurate prophage island detection with PIDE.Genome biology · 2025Article
- Environmental adaptations in metagenomes revealed by deep learning.BMC biology · 2025Article
- ASCE-PPIS: a protein-protein interaction sites predictor based on equivariant graph neural network with fusion of structure-aware pooling and graph collapse.Bioinformatics (Oxford, England) · 2025Article
- Empirical Assessment of Sequence-Based Predictions of Intrinsically Disordered Regions Involved in Phase Separation.Biomolecules · 2025Article
- ESM2_AMP: an interpretable framework for protein-protein interactions prediction and biological mechanism discovery.Briefings in bioinformatics · 2025Article
- HybridKla: a hybrid deep learning framework for lactylation site prediction.Briefings in bioinformatics · 2025Article
- Prediction of liquid-phase separation proteins using Siamese network with feature fusion.Briefings in bioinformatics · 2025Article
- Multistage attention-based extraction and fusion of protein sequence and structural features for protein function prediction.Bioinformatics (Oxford, England) · 2025Article
- EUP: Enhanced cross-species prediction of ubiquitination sites via a conditional variational autoencoder network based on ESM2.PLoS computational biology · 2025Article
- Accurate prediction of virulence factors using pre-train protein language model and ensemble learning.BMC genomics · 2025Article
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Authors and funding
6 authors.
Funding
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, we have illuminated potential associations between diverse feature types and specific subcellular localizations. Particularly, the prediction of Mitochondrion and Golgi apparatus prefer segments feature closer to the N-terminal, and phosphorylation site-based features could mirror phosphorylation properties. We also evaluate the prediction performance and interpretability robustness of Random Forest and Deep Neural Networks with varied feature inputs. This work offers novel insights into maximizing LLMs' utility, understanding their mechanisms, and extracting biological domain knowledge. Furthermore, we have made the code, feature extraction API, and all relevant materials available at https://github.com/yujuan-zhang/feature-representation-for-LLMs.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.