Evaluating Protein Transfer Learning with TAPE

Cited by (723)

Items 201–300 of 723

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5. T4SEfinder: a bioinformatics tool for genome-scale prediction of bacterial type IV secreted effectors using pre-trained protein language model
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14. Self-Supervised Pre-training for Protein Embeddings Using Tertiary Structures
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15. Identify Bitter Peptides by Using Deep Representation Learning Features
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22. Single Layers of Attention Suffice to Predict Protein Contacts
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23. Hierarchical multimodal self-attention-based graph neural network for DTI prediction
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31. tFold-Ab: Fast and Accurate Antibody Structure Prediction without Sequence Homologs
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32. Improved prediction of MHC-peptide binding using protein language models
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34. BERT-Kgly: A Bidirectional Encoder Representations From Transformers (BERT)-Based Model for Predicting Lysine Glycation Site for Homo sapiens
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36. Language models can identify enzymatic binding sites in protein sequences
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37. HybridGCN for protein solubility prediction with adaptive weighting of multiple features
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39. Protein Language Pragmatic Analysis and Progressive Transfer Learning for Profiling Peptide-Protein Interactions
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40. PTG-PLM: Predicting Post-Translational Glycosylation and Glycation Sites Using Protein Language Models and Deep Learning
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41. BERTrand—peptide:TCR binding prediction using Bidirectional Encoder Representations from Transformers augmented with random TCR pairing
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42. Results of the Protein Engineering Tournament: An Open Science Benchmark for Protein Modeling and Design
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43. Combining evolutionary and assay-labelled data for protein fitness prediction
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44. Genome-wide prediction of disease variants with a deep protein language model
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45. GEFormerDTA: drug target affinity prediction based on transformer graph for early fusion
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47. A dual transfer learning method based on 3D-CNN and vision transformer for emotion recognition
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48. Comprehensive benchmarking of large language models for RNA secondary structure prediction
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49. Modelling Drug-Target Binding Affinity using a BERT based Graph Neural network
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50. Structure-inducing pre-training
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51. EpiBERTope: a sequence-based pre-trained BERT model improves linear and structural epitope prediction by learning long-distance protein interactions effectively
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52. Training Compute-Optimal Protein Language Models
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53. From Mechanistic Interpretability to Mechanistic Biology: Training, Evaluating, and Interpreting Sparse Autoencoders on Protein Language Models
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54. CELA-MFP: a contrast-enhanced and label-adaptive framework for multi-functional therapeutic peptides prediction
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55. Deep Learning in Antimicrobial Peptide Prediction
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60. Pre-trained protein language model sheds new light on the prediction of Arabidopsis protein–protein interactions
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