Transformers in Protein: A Survey | Paper Citation Record

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This paper cites Attention is all you need,.

Transformers in Protein: A Survey Attention is all you need,

Reference 1

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This paper cites Bert: Pretraining of deep bidirectional transformers for language understanding,.

Transformers in Protein: A Survey Bert: Pretraining of deep bidirectional transformers for language understanding,

Reference 2

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This paper cites Language Models are Few-Shot Learners.

Transformers in Protein: A Survey Language Models are Few-Shot Learners

Reference 3

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This paper cites Pmanet: Malicious url detection via post-trained language model guided multi-level feature attention network,.

Transformers in Protein: A Survey Pmanet: Malicious url detection via post-trained language model guided multi-level feature attention network,

Reference 4

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This paper cites Vul- lmgnns: Fusing language models and online-distilled graph neural networks for code vulnerability detection,.

Transformers in Protein: A Survey Vul- lmgnns: Fusing language models and online-distilled graph neural networks for code vulnerability detection,

Reference 5

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This paper cites Ethereum fraud detection via joint transaction language model and graph representation learning,.

Transformers in Protein: A Survey Ethereum fraud detection via joint transaction language model and graph representation learning,

Reference 6

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This paper cites Biological structure and function emerge from scaling unsupervised learning to 250 million protein sequences,.

Transformers in Protein: A Survey Biological structure and function emerge from scaling unsupervised learning to 250 million protein sequences,

Reference 7

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This paper cites Highly accurate protein structure prediction with alphafold,.

Transformers in Protein: A Survey Highly accurate protein structure prediction with alphafold,

Reference 8

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This paper cites Evaluating protein transfer learning with tape,.

Transformers in Protein: A Survey Evaluating protein transfer learning with tape,

Reference 9

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This paper cites Evolutionary velocity with protein language models predicts evolutionary dynamics of diverse proteins,.

Transformers in Protein: A Survey Evolutionary velocity with protein language models predicts evolutionary dynamics of diverse proteins,

Reference 10

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This paper cites Language models of protein sequences at the scale of evolution enable accurate structure prediction,.

Transformers in Protein: A Survey Language models of protein sequences at the scale of evolution enable accurate structure prediction,

Reference 11

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This paper cites Deep learning in bioinformatics,.

Transformers in Protein: A Survey Deep learning in bioinformatics,

Reference 12

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This paper cites Machine learning solutions for predicting protein–protein interactions,.

Transformers in Protein: A Survey Machine learning solutions for predicting protein–protein interactions,

Reference 13

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This paper cites Unified rational protein engineering with sequence-based deep representation learning,.

Transformers in Protein: A Survey Unified rational protein engineering with sequence-based deep representation learning,

Reference 14

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This paper cites Protein- bert: a universal deep-learning model of protein sequence and function,.

Transformers in Protein: A Survey Protein- bert: a universal deep-learning model of protein sequence and function,

Reference 15

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This paper cites Deep learning in proteomics,.

Transformers in Protein: A Survey Deep learning in proteomics,

Reference 16

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This paper cites Transformer- based deep learning for predicting protein properties in the life sci- ences,.

Transformers in Protein: A Survey Transformer- based deep learning for predicting protein properties in the life sci- ences,

Reference 17

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This paper cites Machine learning: its challenges and opportunities in plant system biology,.

Transformers in Protein: A Survey Machine learning: its challenges and opportunities in plant system biology,

Reference 18

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This paper cites Artificial intelligence in the prediction of protein–ligand interactions: recent advances and future directions,.

Transformers in Protein: A Survey Artificial intelligence in the prediction of protein–ligand interactions: recent advances and future directions,

Reference 19

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This paper cites Roberta: A robustly optimized bert pretraining approach,.

Transformers in Protein: A Survey Roberta: A robustly optimized bert pretraining approach,

Reference 20

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This paper cites Linformer: Self- attention with linear complexity,.

Transformers in Protein: A Survey Linformer: Self- attention with linear complexity,

Reference 21

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This paper cites An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale.

Transformers in Protein: A Survey An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale

Reference 22

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This paper cites On the turing completeness of modern neural network architectures,.

Transformers in Protein: A Survey On the turing completeness of modern neural network architectures,

Reference 23

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This paper cites On the relationship between self-attention and convolutional layers,.

Transformers in Protein: A Survey On the relationship between self-attention and convolutional layers,

Reference 24

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This paper cites Deformable convolutional networks,.

Transformers in Protein: A Survey Deformable convolutional networks,

Reference 25

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This paper cites Prottrans: Towards cracking the language of life’s code through self-supervised deep learning and high performance computing,.

Transformers in Protein: A Survey Prottrans: Towards cracking the language of life’s code through self-supervised deep learning and high performance computing,

Reference 26

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This paper cites PeptideBERT: A Language Model based on Transformers for Peptide Property Prediction.

Transformers in Protein: A Survey PeptideBERT: A Language Model based on Transformers for Peptide Property Prediction

Reference 28

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This paper cites Language models of protein sequences at the scale of evolution enable accurate structure prediction,.

Transformers in Protein: A Survey Language models of protein sequences at the scale of evolution enable accurate structure prediction,

Reference 29

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This paper cites Msa transformer,.

Transformers in Protein: A Survey Msa transformer,

Reference 30

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This paper cites Endowing Protein Language Models with Structural Knowledge.

Transformers in Protein: A Survey Endowing Protein Language Models with Structural Knowledge

Reference 31

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This paper cites Alberts, D.

Transformers in Protein: A Survey Alberts, D

Reference 32

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This paper cites Prediction of protein conformation,.

Transformers in Protein: A Survey Prediction of protein conformation,

Reference 33

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This paper cites Analysis of the accuracy and implications of simple methods for predicting the secondary structure of globular proteins,.

Transformers in Protein: A Survey Analysis of the accuracy and implications of simple methods for predicting the secondary structure of globular proteins,

Reference 35

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This paper cites Modeling aspects of the language of life through transfer-learning protein sequences,.

Transformers in Protein: A Survey Modeling aspects of the language of life through transfer-learning protein sequences,

Reference 36

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This paper cites Tm-align: a protein structure alignment algorithm based on the tm-score,.

Transformers in Protein: A Survey Tm-align: a protein structure alignment algorithm based on the tm-score,

Reference 39

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This paper cites Prottrans: towards cracking the language of life’s code through self-supervised learning,.

Transformers in Protein: A Survey Prottrans: towards cracking the language of life’s code through self-supervised learning,

Reference 40

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This paper cites The protein-folding problem, 50 years on,.

Transformers in Protein: A Survey The protein-folding problem, 50 years on,

Reference 41

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This paper cites Protein folding and misfolding,.

Transformers in Protein: A Survey Protein folding and misfolding,

Reference 42

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This paper cites Goap: a generalized orientation-dependent, all-atom statistical potential for protein structure prediction,.

Transformers in Protein: A Survey Goap: a generalized orientation-dependent, all-atom statistical potential for protein structure prediction,

Reference 48

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This paper cites Protein secondary structure prediction based on position- specific scoring matrices,.

Transformers in Protein: A Survey Protein secondary structure prediction based on position- specific scoring matrices,

Reference 49

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This paper cites Prediction of protein secondary structure at better than 70% accuracy,.

Transformers in Protein: A Survey Prediction of protein secondary structure at better than 70% accuracy,

Reference 50

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This paper cites Protein-folding dynamics: overview of molecular simulation techniques,.

Transformers in Protein: A Survey Protein-folding dynamics: overview of molecular simulation techniques,

Reference 51

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This paper cites Protein modeling by e-mail,.

Transformers in Protein: A Survey Protein modeling by e-mail,

Reference 53

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This paper cites Language models of protein sequences at the scale of evolution enable accurate structure prediction,.

Transformers in Protein: A Survey Language models of protein sequences at the scale of evolution enable accurate structure prediction,

Reference 57

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This paper cites Suppression of alpha-band power underlies exogenous attention to emotional distractors,.

Transformers in Protein: A Survey Suppression of alpha-band power underlies exogenous attention to emotional distractors,

Reference 58

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This paper cites Improved protein structure prediction using potentials from deep learning,.

Transformers in Protein: A Survey Improved protein structure prediction using potentials from deep learning,

Reference 60

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This paper cites Evaluating protein transfer learning with tape,.

Transformers in Protein: A Survey Evaluating protein transfer learning with tape,

Reference 61

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This paper cites A Protein Structure Prediction Approach Leveraging Transformer and CNN Integration.

Transformers in Protein: A Survey A Protein Structure Prediction Approach Leveraging Transformer and CNN Integration

Reference 62

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This paper cites Protein- bert: a universal deep-learning model of protein sequence and function,.

Transformers in Protein: A Survey Protein- bert: a universal deep-learning model of protein sequence and function,

Reference 63

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This paper cites Trans-morfs: A disordered protein predictor based on the transformer architecture,.

Transformers in Protein: A Survey Trans-morfs: A disordered protein predictor based on the transformer architecture,

Reference 64

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This paper cites ChemBERTa: Large-Scale Self-Supervised Pretraining for Molecular Property Prediction.

Transformers in Protein: A Survey ChemBERTa: Large-Scale Self-Supervised Pretraining for Molecular Property Prediction

Reference 65

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This paper cites Ramsundar, P.

Transformers in Protein: A Survey Ramsundar, P

Reference 66

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This paper cites High-resolution de novo structure prediction from primary sequence,.

Transformers in Protein: A Survey High-resolution de novo structure prediction from primary sequence,

Reference 67

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This paper cites Protgpt2 is a deep unsupervised language model for protein design,.

Transformers in Protein: A Survey Protgpt2 is a deep unsupervised language model for protein design,

Reference 68

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This paper cites Large language models generate functional protein sequences across diverse families,.

Transformers in Protein: A Survey Large language models generate functional protein sequences across diverse families,

Reference 69

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This paper cites De novo design of protein structure and function with rfdiffusion,.

Transformers in Protein: A Survey De novo design of protein structure and function with rfdiffusion,

Reference 70

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This paper cites Mftrans: A multi-feature transformer network for protein secondary structure prediction,.

Transformers in Protein: A Survey Mftrans: A multi-feature transformer network for protein secondary structure prediction,

Reference 72

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Observation 509e0b86-24b5-40ac-a3b2-af81106d7fe1 · outbound

This paper cites Transconv: Convolution-infused transformer for protein secondary structure prediction,.

Transformers in Protein: A Survey Transconv: Convolution-infused transformer for protein secondary structure prediction,

Reference 73

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Observation 58ebcf24-a840-4a10-9328-e230b9efd3d9 · outbound

This paper cites De novo atomic protein structure modeling for cryoem density maps using 3d transformer and hmm,.

Transformers in Protein: A Survey De novo atomic protein structure modeling for cryoem density maps using 3d transformer and hmm,

Reference 74

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Observation 2eb72f34-a157-481a-ab92-92356814afe9 · outbound

This paper cites A critical review of five machine learning-based algorithms for predicting protein stability changes upon mutation,.

Transformers in Protein: A Survey A critical review of five machine learning-based algorithms for predicting protein stability changes upon mutation,

Reference 75

Resolution: verified exact; doi, observed 2026-08-07T14:03:15.363896Z

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Observation 3ffbd77e-f306-48d3-9915-4ad3da1dd2f9 · outbound

This paper cites Gpcrpred: an svm-based method for prediction of families and subfamilies of g-protein coupled receptors,.

Transformers in Protein: A Survey Gpcrpred: an svm-based method for prediction of families and subfamilies of g-protein coupled receptors,

Reference 76

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T14:03:21.529936Z

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Observation b01c79f0-8e6a-4b0f-9f83-8f44638e1f7c · outbound

This paper cites Multi-scale deep learning for the imbalanced multi-label protein subcellular localization prediction based on im- munohistochemistry images,.

Transformers in Protein: A Survey Multi-scale deep learning for the imbalanced multi-label protein subcellular localization prediction based on im- munohistochemistry images,

Reference 77

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T14:03:21.364194Z

Source-reported events for the cited work

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Observation 4c72bd69-6b51-40b4-82f0-1c107e93f1d1 · outbound

This paper cites Prog-sol: Predicting protein solubility using protein embeddings and dual-graph convolutional networks,.

Transformers in Protein: A Survey Prog-sol: Predicting protein solubility using protein embeddings and dual-graph convolutional networks,

Reference 78

Resolution: verified exact; doi, observed 2026-08-07T14:03:14.923769Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation f7d1767f-cf51-49ea-84bb-7adabd13f9a3 · outbound

This paper cites Deep-probind: Bind- ing protein prediction with transformer-based deep learning model,.

Transformers in Protein: A Survey Deep-probind: Bind- ing protein prediction with transformer-based deep learning model,

Reference 79

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T14:03:21.190261Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation 58a2d6e2-2d30-4b74-a47f-f5515ea23450 · outbound

This paper cites Insights into the inner workings of transformer models for protein function prediction,.

Transformers in Protein: A Survey Insights into the inner workings of transformer models for protein function prediction,

Reference 80

Resolution: verified exact; doi, observed 2026-08-07T14:03:14.742427Z

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Observation 05be466a-50f3-454f-9861-e34b23e4c80e · outbound

This paper cites Segt-go: a graph transformer method based on ppi serialization and explanatory artificial intelligence for protein function prediction,.

Transformers in Protein: A Survey Segt-go: a graph transformer method based on ppi serialization and explanatory artificial intelligence for protein function prediction,

Reference 81

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T14:03:20.984237Z

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Observation 35fb3eee-b421-4fd4-900e-4b4c3c7eb2fb · outbound

This paper cites Integrating transformers and automl for protein function prediction,.

Transformers in Protein: A Survey Integrating transformers and automl for protein function prediction,

Reference 82

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T14:03:20.837485Z

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Observation 36a79ae2-e10c-47b4-96a0-53e33de61c8c · outbound

This paper cites Deepppi: boosting prediction of protein–protein interactions with deep neural networks,.

Transformers in Protein: A Survey Deepppi: boosting prediction of protein–protein interactions with deep neural networks,

Reference 83

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T14:03:20.675614Z

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Observation 5e388b23-5ee9-416a-b5a4-bf6179981660 · outbound

This paper cites Graphtrans: a software system for network conversions for simulation, structural analysis, and graph operations,.

Transformers in Protein: A Survey Graphtrans: a software system for network conversions for simulation, structural analysis, and graph operations,

Reference 84

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T14:03:22.124288Z

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Observation 32ed5314-19e9-40c4-8a07-c93c325096c1 · outbound

This paper cites Gact-ppis: Prediction of protein-protein interaction sites based on graph structure and transformer network,.

Transformers in Protein: A Survey Gact-ppis: Prediction of protein-protein interaction sites based on graph structure and transformer network,

Reference 85

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T14:03:20.457893Z

Source-reported events for the cited work

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Observation 1086db35-16c7-4d12-8e9d-7d0026f45ead · outbound

This paper cites Tranp-b-site: A transformer enhanced method for prediction of binding sites of protein-protein interactions,.

Transformers in Protein: A Survey Tranp-b-site: A transformer enhanced method for prediction of binding sites of protein-protein interactions,

Reference 86

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T14:03:20.253915Z

Source-reported events for the cited work

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Observation e48d37ec-5e6f-49c8-89d8-f649a50b3ab6 · outbound

This paper cites Tuna: An uncertainty- aware transformer model for sequence-based protein–protein interaction prediction,.

Transformers in Protein: A Survey Tuna: An uncertainty- aware transformer model for sequence-based protein–protein interaction prediction,

Reference 87

Resolution: unresolved; no resolver link, observed 2026-08-07T14:03:09.972916Z

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Observation 32dc7779-c7b7-4af7-b70a-e44b07e35594 · outbound

This paper cites Predicting protein-protein binding affinity with deep learning: A comparative analysis of cnn and transformer models,.

Transformers in Protein: A Survey Predicting protein-protein binding affinity with deep learning: A comparative analysis of cnn and transformer models,

Reference 88

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T14:03:20.026045Z

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No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation 07d8e7d2-c562-4a2b-ab64-11ef1557cf97 · outbound

This paper cites A review of transformers in drug discovery and beyond,.

Transformers in Protein: A Survey A review of transformers in drug discovery and beyond,

Reference 89

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T14:03:19.833901Z

Source-reported events for the cited work

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Observation ff5502d0-a63a-4327-bea0-0be01e7a034c · outbound

This paper cites Mol-bert: An effective molecular representation with bert for molecular property prediction,.

Transformers in Protein: A Survey Mol-bert: An effective molecular representation with bert for molecular property prediction,

Reference 90

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T14:03:19.619012Z

Source-reported events for the cited work

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Observation 2baa566d-23c5-4480-be12-a6a5ce5e3f9a · outbound

This paper cites Molecular generative graph neural networks for drug discovery,.

Transformers in Protein: A Survey Molecular generative graph neural networks for drug discovery,

Reference 91

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T14:03:19.438610Z

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Observation 4489ff67-aa95-4854-887e-fa150d1feddb · outbound

This paper cites Integrating transformer-based language model for drug discovery,.

Transformers in Protein: A Survey Integrating transformer-based language model for drug discovery,

Reference 93

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T14:03:19.247088Z

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Observation 1d0c8684-1a9d-414b-8c8c-0083a8808a68 · outbound

This paper cites Integrating transformers and many-objective optimization for drug design,.

Transformers in Protein: A Survey Integrating transformers and many-objective optimization for drug design,

Reference 94

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T14:03:19.064622Z

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Observation 81764d03-a23c-4a9c-8e3a-308eeb2466aa · outbound

This paper cites Transformers and large language models for chemistry and drug discovery,.

Transformers in Protein: A Survey Transformers and large language models for chemistry and drug discovery,

Reference 95

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T14:03:18.813725Z

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Observation 664b84b4-39ec-44ab-8381-4aea678813aa · outbound

This paper cites Sspro/accpro 5: almost perfect prediction of protein secondary structure and relative solvent accessibility using profiles, machine learning and structural similarity,.

Transformers in Protein: A Survey Sspro/accpro 5: almost perfect prediction of protein secondary structure and relative solvent accessibility using profiles, machine learning and structural similarity,

Reference 96

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T14:03:24.481228Z

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Observation 19205735-2dad-4908-9e8e-cc0768992f2f · outbound

This paper cites Uniprot: the universal protein knowledgebase in 2021,.

Transformers in Protein: A Survey Uniprot: the universal protein knowledgebase in 2021,

Reference 97

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T14:03:25.320178Z

Source-reported events for the cited work

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Observation ea613f91-af85-4546-93ee-eb9491a7c1ef · outbound

This paper cites Mmseqs2 enables sensitive protein sequence searching for the analysis of massive data sets,.

Transformers in Protein: A Survey Mmseqs2 enables sensitive protein sequence searching for the analysis of massive data sets,

Reference 98

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T14:03:25.709269Z

Source-reported events for the cited work

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Observation 7c7300b7-64d2-4927-ab96-5a4f24587eed · outbound

This paper cites Confold2: improved contact-driven ab initio protein structure modeling,.

Transformers in Protein: A Survey Confold2: improved contact-driven ab initio protein structure modeling,

Reference 99

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T14:03:25.162050Z

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Observation e74dcf80-3079-4e48-bb98-598c1a471000 · outbound

This paper cites Language models enable zero-shot prediction of the effects of mutations on protein function,.

Transformers in Protein: A Survey Language models enable zero-shot prediction of the effects of mutations on protein function,

Reference 101

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T14:03:18.416264Z

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Observation 13e188e6-4851-4a09-a590-76eeef4fd645 · outbound

This paper cites Energy and policy con- siderations for modern deep learning research,.

Transformers in Protein: A Survey Energy and policy con- siderations for modern deep learning research,

Reference 102

Resolution: unresolved; no resolver link, observed 2026-08-07T14:03:11.547056Z

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Observation 3b0323c1-b835-4478-ba94-ed9fa7843598 · outbound

This paper cites Deep learning in proteomics,.

Transformers in Protein: A Survey Deep learning in proteomics,

Reference 103

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T14:03:18.240507Z

Source-reported events for the cited work

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Observation 011ba748-bb11-4b40-88f6-4c3b9c138ca6 · outbound

This paper cites Deep multi-view learning methods: A review,.

Transformers in Protein: A Survey Deep multi-view learning methods: A review,

Reference 104

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T14:03:17.990361Z

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Observation dc50b9cb-37df-44a5-950c-48b896926721 · outbound

This paper cites Towards A Rigorous Science of Interpretable Machine Learning.

Transformers in Protein: A Survey Towards A Rigorous Science of Interpretable Machine Learning

Reference 106

Resolution: unresolved; no resolver link, observed 2026-08-07T14:03:12.045481Z

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This paper cites Explainable Artificial Intelligence: Understanding, Visualizing and Interpreting Deep Learning Models.

Transformers in Protein: A Survey Explainable Artificial Intelligence: Understanding, Visualizing and Interpreting Deep Learning Models

Reference 107

Resolution: unresolved; no resolver link, observed 2026-08-07T14:03:12.178432Z

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Observation f5e44775-b372-4eaa-9dcf-8680729f41e5 · outbound

This paper cites Transformer protein language models are unsupervised structure learners,.

Transformers in Protein: A Survey Transformer protein language models are unsupervised structure learners,

Reference 108

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T14:03:18.634300Z

Source-reported events for the cited work

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Observation 24bfbff4-4e82-4449-a1f5-576b491f2131 · outbound

This paper cites Protein tertiary structure prediction and refinement using deep learning and rosetta in casp14,.

Transformers in Protein: A Survey Protein tertiary structure prediction and refinement using deep learning and rosetta in casp14,

Reference 109

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T14:03:17.722242Z

Source-reported events for the cited work

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Observation 93f1b046-098d-472a-a6d8-66a12ec95006 · outbound

This paper cites Deepaffinity: interpretable deep learning of compound–protein affinity through unified recurrent and convolutional neural networks,.

Transformers in Protein: A Survey Deepaffinity: interpretable deep learning of compound–protein affinity through unified recurrent and convolutional neural networks,

Reference 110

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T14:03:17.518518Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation 783a8155-2014-4996-a1b2-c039bd54024f · outbound

This paper cites Grandmaster level in starcraft ii using multi-agent reinforcement learning,.

Transformers in Protein: A Survey Grandmaster level in starcraft ii using multi-agent reinforcement learning,

Reference 111

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T14:03:17.331316Z

Source-reported events for the cited work

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Observation 7ba9d5bb-e8b4-4b82-86bd-cc8ef6b67cd0 · outbound

This paper cites Toward a shared vision for cancer genomic data,.

Transformers in Protein: A Survey Toward a shared vision for cancer genomic data,

Reference 112

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T14:03:17.131373Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation 203cac42-87ae-4618-98cc-09752ef89b2a · outbound

This paper cites Prottrans: Toward understanding the language of life through self-supervised learning,.

Transformers in Protein: A Survey Prottrans: Toward understanding the language of life through self-supervised learning,

Reference 114

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T14:03:24.151134Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation 70a97fb9-a971-476c-be20-39a89c9e819a · outbound

This paper cites Biological structure and function emerge from scaling unsupervised learning to 250 million protein sequences,.

Transformers in Protein: A Survey Biological structure and function emerge from scaling unsupervised learning to 250 million protein sequences,

Reference 115

Resolution: unresolved; no resolver link, observed 2026-08-07T14:03:13.225771Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 5b9238b1-ee7e-43b0-9a14-05faa089b9c7 · outbound

This paper cites Accurate prediction of protein structures and interactions using a three- track neural network,.

Transformers in Protein: A Survey Accurate prediction of protein structures and interactions using a three- track neural network,

Reference 116

Resolution: unresolved; no resolver link, observed 2026-08-07T14:03:13.362425Z

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Observation 321c2afe-540e-4991-a851-571bebac858d · outbound

This paper cites Molmol: a program for dis- play and analysis of macromolecular structures,.

Transformers in Protein: A Survey Molmol: a program for dis- play and analysis of macromolecular structures,

Reference 117

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T14:03:25.521265Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation f81f4e42-28fc-4085-9b55-caebfb4a2618 · outbound

This paper cites Transformer architecture and attention mech- anisms in genome data analysis: a comprehensive review,.

Transformers in Protein: A Survey Transformer architecture and attention mech- anisms in genome data analysis: a comprehensive review,

Reference 118

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T14:03:16.884822Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation dd09bc71-1e03-468a-a493-56a1e19628a3 · outbound

This paper cites A Unified Approach to Interpreting Model Predictions.

Transformers in Protein: A Survey A Unified Approach to Interpreting Model Predictions

Reference 119

Resolution: unresolved; no resolver link, observed 2026-08-07T14:03:13.766676Z

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