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Paper Citation Record · LEDGER

A Comprehensive Review of Protein Language Models

As of 14 August 2026, this Paper Citation Record lists 100 of 186 outbound references and 7 inbound Pith citation observations for arXiv:2502.06881.

A citation records a reference. It does not transfer a finding from one paper to another.

pith.paper-citation-record.v1
2502.06881 v1

Coverage vector

measured 100 of 186 reference resolution

Typed states for the displayed outbound observations.

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measured 107 of 107 standing notices

One-hop event checks from named stored sources.

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measured 7 of 7 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-07T11:35:42.887822Z

measured 0 of 1 external citation measurements

A source-named dated measurement, never combined with another source.

Source: arxiv_reference, observed 2026-07-02T07:06:44.072099Z

Reference resolution

100 of 186 outbound references displayed

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Outbound references

Observation 9506a499-0d3c-4819-a821-4e38557e5769 · outbound

This paper cites Attention is all you need.

A Comprehensive Review of Protein Language Models Attention is all you need

Reference 1

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Observation 7941504c-75bf-406c-8597-c697546204f3 · outbound

This paper cites Nomenclature and symbolism for amino acids and pep- tides.

A Comprehensive Review of Protein Language Models Nomenclature and symbolism for amino acids and pep- tides

Reference 2

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Observation 3197ab4f-8b6e-49a3-a5b8-f65817a4cfa9 · outbound

This paper cites Principles that govern the folding of protein chains.

A Comprehensive Review of Protein Language Models Principles that govern the folding of protein chains

Reference 3

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Observation f0380ed8-7b96-4aff-ba5d-f11dae933479 · outbound

This paper cites Learning the pro- tein language: Evolution, structure, and function.

A Comprehensive Review of Protein Language Models Learning the pro- tein language: Evolution, structure, and function

Reference 4

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This paper cites Biolog- ical structure and function emerge from scaling un- supervised learning to 250 million protein sequences.

A Comprehensive Review of Protein Language Models Biolog- ical structure and function emerge from scaling un- supervised learning to 250 million protein sequences

Reference 5

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Observation e7658d47-99b3-46cc-a296-f2a86f60d935 · outbound

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

A Comprehensive Review of Protein Language Models Prottrans: Toward understanding the language of life through self-supervised learning

Reference 6

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This paper cites Fine-tuning protein language models boosts pre- dictions across diverse tasks.

A Comprehensive Review of Protein Language Models Fine-tuning protein language models boosts pre- dictions across diverse tasks

Reference 7

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Observation d7b071e0-1b44-471d-82d4-37d7fbbcab06 · outbound

This paper cites Lm-gvp: an extensible sequence and structure informed deep learning frame- work for protein property prediction.

A Comprehensive Review of Protein Language Models Lm-gvp: an extensible sequence and structure informed deep learning frame- work for protein property prediction

Reference 8

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This paper cites Deep learning in bioinformatics: Introduction, application, and perspective in the big data era.

A Comprehensive Review of Protein Language Models Deep learning in bioinformatics: Introduction, application, and perspective in the big data era

Reference 9

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This paper cites Efficient Estimation of Word Representations in Vector Space.

A Comprehensive Review of Protein Language Models Efficient Estimation of Word Representations in Vector Space

Reference 10

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This paper cites Con- tinuous distributed representation of biological se- quences for deep proteomics and genomics.

A Comprehensive Review of Protein Language Models Con- tinuous distributed representation of biological se- quences for deep proteomics and genomics

Reference 11

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This paper cites Uniprotkb/swiss-prot, the manually annotated section of the uniprot knowledgebase: how to use the entry view.

A Comprehensive Review of Protein Language Models Uniprotkb/swiss-prot, the manually annotated section of the uniprot knowledgebase: how to use the entry view

Reference 12

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This paper cites Probabilistic variable-length segmen- tation of protein sequences for discriminative motif dis- JOURNAL OF LATEX CLASS FILES, VOL.

A Comprehensive Review of Protein Language Models Probabilistic variable-length segmen- tation of protein sequences for discriminative motif dis- JOURNAL OF LATEX CLASS FILES, VOL

Reference 13

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This paper cites seq2vec: Analyzing sequential data using multi-rank embedding vectors.

A Comprehensive Review of Protein Language Models seq2vec: Analyzing sequential data using multi-rank embedding vectors

Reference 14

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This paper cites Distributed represen- tations of sentences and documents.

A Comprehensive Review of Protein Language Models Distributed represen- tations of sentences and documents

Reference 15

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This paper cites Masked inverse folding with sequence transfer for protein representation learning.

A Comprehensive Review of Protein Language Models Masked inverse folding with sequence transfer for protein representation learning

Reference 16

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This paper cites Gradient-based learning applied to document recognition.

A Comprehensive Review of Protein Language Models Gradient-based learning applied to document recognition

Reference 17

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This paper cites The graph neural network model.

A Comprehensive Review of Protein Language Models The graph neural network model

Reference 18

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This paper cites Convo- lutions are competitive with transformers for protein sequence pretraining.

A Comprehensive Review of Protein Language Models Convo- lutions are competitive with transformers for protein sequence pretraining

Reference 19

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This paper cites Recurrent Neural Network Regularization.

A Comprehensive Review of Protein Language Models Recurrent Neural Network Regularization

Reference 20

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This paper cites Long short-term mem- ory.

A Comprehensive Review of Protein Language Models Long short-term mem- ory

Reference 21

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

A Comprehensive Review of Protein Language Models Unified rational protein engineering with sequence-based deep representation learning

Reference 22

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This paper cites Multiplicative LSTM for sequence modelling.

A Comprehensive Review of Protein Language Models Multiplicative LSTM for sequence modelling

Reference 23

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This paper cites Uniref: comprehensive and non-redundant uniprot reference clusters.

A Comprehensive Review of Protein Language Models Uniref: comprehensive and non-redundant uniprot reference clusters

Reference 24

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This paper cites Uniref clusters: a comprehensive and scalable alternative for improving sequence similarity searches.

A Comprehensive Review of Protein Language Models Uniref clusters: a comprehensive and scalable alternative for improving sequence similarity searches

Reference 25

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This paper cites Udsmprot: universal deep sequence models for protein classification.

A Comprehensive Review of Protein Language Models Udsmprot: universal deep sequence models for protein classification

Reference 26

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A Comprehensive Review of Protein Language Models Regularizing and Optimizing LSTM Language Models

Reference 27

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This paper cites Deep contextualized word representations.

A Comprehensive Review of Protein Language Models Deep contextualized word representations

Reference 28

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

A Comprehensive Review of Protein Language Models Modeling aspects of the language of life through transfer-learning protein sequences

Reference 29

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A Comprehensive Review of Protein Language Models Faster and smaller n-gram language models

Reference 30

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A Comprehensive Review of Protein Language Models Optimizing multi-gpu parallelization strategies for deep learning training

Reference 31

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

A Comprehensive Review of Protein Language Models Transformer-based deep learning for predicting protein properties in the life sciences

Reference 32

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This paper cites Enhancing efficiency of protein language models with minimal wet-lab data through few-shot learning.

A Comprehensive Review of Protein Language Models Enhancing efficiency of protein language models with minimal wet-lab data through few-shot learning

Reference 33

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A Comprehensive Review of Protein Language Models BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding

Reference 34

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A Comprehensive Review of Protein Language Models A Structured Self-attentive Sentence Embedding

Reference 36

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This paper cites Deciphering the protein landscape with protflash, a lightweight language model.

A Comprehensive Review of Protein Language Models Deciphering the protein landscape with protflash, a lightweight language model

Reference 37

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A Comprehensive Review of Protein Language Models Unified language model pre-training for nat- ural language understanding and generation

Reference 38

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A Comprehensive Review of Protein Language Models Language models en- able zero-shot prediction of the effects of mutations on protein function

Reference 39

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This paper cites Simulating 500 million years of evolution with a language model.

A Comprehensive Review of Protein Language Models Simulating 500 million years of evolution with a language model

Reference 41

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Observation d70d67bc-70da-4074-abf6-e63be147f4a0 · outbound

This paper cites Esm cambrian: Revealing the mysteries of proteins with unsupervised learning.

A Comprehensive Review of Protein Language Models Esm cambrian: Revealing the mysteries of proteins with unsupervised learning

Reference 42

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Observation 6dac531c-e36c-4d89-8748-f700b28bbca9 · outbound

This paper cites Audio albert: A lite bert for self-supervised learning of audio representation.

A Comprehensive Review of Protein Language Models Audio albert: A lite bert for self-supervised learning of audio representation

Reference 43

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Observation 11a93bd5-564e-404a-96c3-fe232cf36a63 · outbound

This paper cites ELECTRA: Pre-training Text Encoders as Discriminators Rather Than Generators.

A Comprehensive Review of Protein Language Models ELECTRA: Pre-training Text Encoders as Discriminators Rather Than Generators

Reference 44

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no resolver link, observed 2026-08-08T18:43:34.109567Z

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source=pdf_text observed=2026-08-08T18:43:34.109567Z digest=sha256:bd3c9133c9e87becc7dab40906bbdd8f48c6c364f94bf4bea8c589e3453d5b71

Observation 90293a2a-9c8a-4d3d-9ac0-7ee7070bb30e · outbound

This paper cites Protein-level assembly increases protein sequence re- covery from metagenomic samples manyfold.

A Comprehensive Review of Protein Language Models Protein-level assembly increases protein sequence re- covery from metagenomic samples manyfold

Reference 45

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no resolver link, observed 2026-08-08T18:43:34.114498Z

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source=pdf_text observed=2026-08-08T18:43:34.114498Z digest=sha256:08f6af1fdf9973cf197c305020987a91240ca60bacec6eed50ded230f6e106ec

Observation a33b3e27-9110-4e0a-9609-1ffef01c154d · outbound

This paper cites Distilprot- bert: a distilled protein language model used to distin- guish between real proteins and their randomly shuffled counterparts.

A Comprehensive Review of Protein Language Models Distilprot- bert: a distilled protein language model used to distin- guish between real proteins and their randomly shuffled counterparts

Reference 46

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source=pdf_text observed=2026-08-08T18:43:34.119163Z digest=sha256:84d6f117e4dae781c6a874aac5eb0a8626b5a5b802e0d83027966d15fedd98d4

Observation 44919c6a-eb9f-4d6a-912a-a3d2bd92157b · outbound

This paper cites Modeling Protein Using Large-scale Pretrain Language Model.

A Comprehensive Review of Protein Language Models Modeling Protein Using Large-scale Pretrain Language Model

Reference 47

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no resolver link, observed 2026-08-08T18:43:34.123895Z

Source-reported events for the cited work

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source=pdf_text observed=2026-08-08T18:43:34.123895Z digest=sha256:06abdf5c24e9e52918530738ca05d774432acc428a9e0825d8b054203d1d29fe

Observation 46378def-76f6-42db-8087-58a0c68b62f9 · outbound

This paper cites Transforming the language of life: transformer neural networks for protein prediction tasks.

A Comprehensive Review of Protein Language Models Transforming the language of life: transformer neural networks for protein prediction tasks

Reference 48

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no resolver link, observed 2026-08-08T18:43:34.129069Z

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source=pdf_text observed=2026-08-08T18:43:34.129069Z digest=sha256:ecbca53ffeb844521ad647b2c038f4a5b20aae671efc7e3eb0810c0e7e80edcd

Observation c24e211d-49da-4fdd-b5dd-122e3c69a13c · outbound

This paper cites RoBERTa: A Robustly Optimized BERT Pretraining Approach.

A Comprehensive Review of Protein Language Models RoBERTa: A Robustly Optimized BERT Pretraining Approach

Reference 49

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no resolver link, observed 2026-08-08T18:43:34.133924Z

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source=pdf_text observed=2026-08-08T18:43:34.133924Z digest=sha256:a6c2b720ea66d2b047856639bdc8afd4d4b87b5c3d5c0776023d6f733a947634

Observation bc60c4df-83d7-46bb-9fc9-d30e4d2de92d · outbound

This paper cites Large Batch Optimization for Deep Learning: Training BERT in 76 minutes.

A Comprehensive Review of Protein Language Models Large Batch Optimization for Deep Learning: Training BERT in 76 minutes

Reference 50

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no resolver link, observed 2026-08-08T18:43:34.139245Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-08T18:43:34.139245Z digest=sha256:b0c63c49ec7e49afa9bee7fd4a5a2310f02b18f748fa0ab10e889957847e2db2

Observation 6082ccd6-fdd9-4fb1-b39b-def9ca085843 · outbound

This paper cites Pre-training Co-evolutionary Protein Representation via A Pairwise Masked Language Model.

A Comprehensive Review of Protein Language Models Pre-training Co-evolutionary Protein Representation via A Pairwise Masked Language Model

Reference 51

Resolution
verified exact
local_arxiv, observed 2026-08-08T18:43:35.230517Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

source=pdf_text observed=2026-08-08T18:43:34.144350Z digest=sha256:469588321928722ff570c9e41d86b93bf126b01eb8e91c87454e8dd447acf894

Observation c5e5874e-738c-4960-86b2-683c2dd3d149 · outbound

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

A Comprehensive Review of Protein Language Models Proteinbert: a universal deep-learning model of protein sequence and function

Reference 52

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no resolver link, observed 2026-08-08T18:43:34.149904Z

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source=pdf_text observed=2026-08-08T18:43:34.149904Z digest=sha256:0c4bbe9d8be818ad1bba9a8078d0744f1610680094b5be84626009ee19bd5323

Observation 7a0546b1-e954-4b8f-a235-c68ae423a1c1 · outbound

This paper cites Single-sequence protein structure prediction using a language model and deep learning.

A Comprehensive Review of Protein Language Models Single-sequence protein structure prediction using a language model and deep learning

Reference 53

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no resolver link, observed 2026-08-08T18:43:34.154737Z

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source=pdf_text observed=2026-08-08T18:43:34.154737Z digest=sha256:ce1661fae8c22ae9d32118eff373e5105600ad2055ee97a9fe13ae16a9f2c878

Observation 48c0c323-36e3-4491-80d6-269f05093b6b · outbound

This paper cites Multi- level protein structure pre-training via prompt learning.

A Comprehensive Review of Protein Language Models Multi- level protein structure pre-training via prompt learning

Reference 54

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no resolver link, observed 2026-08-08T18:43:34.159352Z

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source=pdf_text observed=2026-08-08T18:43:34.159352Z digest=sha256:000756cec735166d804507a78dc060e24eee201f011a415538cc6c48c1dc59fa

Observation 201355d1-a39a-49c7-a39c-6037889f9814 · outbound

This paper cites Esm all-atom: Multi-scale protein lan- guage model for unified molecular modeling.

A Comprehensive Review of Protein Language Models Esm all-atom: Multi-scale protein lan- guage model for unified molecular modeling

Reference 55

Resolution
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no resolver link, observed 2026-08-08T18:43:34.163935Z

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source=pdf_text observed=2026-08-08T18:43:34.163935Z digest=sha256:9a1a6e11b75df10d78760a0e1350abb05cd6ac32ba67120e327cbadeddac83ec

Observation c6f21fdd-0692-477f-acee-521b86bbbc99 · outbound

This paper cites Tcr-bert: learning the grammar of t-cell receptors for flexible antigen-binding analyses.

A Comprehensive Review of Protein Language Models Tcr-bert: learning the grammar of t-cell receptors for flexible antigen-binding analyses

Reference 56

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no resolver link, observed 2026-08-08T18:43:34.168834Z

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source=pdf_text observed=2026-08-08T18:43:34.168834Z digest=sha256:e707c57b2413804eac276272c34184d8f81b2ac1f4252419a4c5a4d0aff795d9

Observation b739e9f9-091c-478d-8eab-06b9ff8c305b · outbound

This paper cites Deciphering the language of antibodies using self-supervised learning.

A Comprehensive Review of Protein Language Models Deciphering the language of antibodies using self-supervised learning

Reference 57

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no resolver link, observed 2026-08-08T18:43:34.173981Z

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source=pdf_text observed=2026-08-08T18:43:34.173981Z digest=sha256:2f5cf20adff68229cbc4aab94d8c968357522605b9dcff21faece52ddb88ae45

Observation 2ef574c4-8c7a-4756-afc7-e3aa4de430eb · outbound

This paper cites Deciphering antibody affinity maturation with language models and weakly supervised learning.

A Comprehensive Review of Protein Language Models Deciphering antibody affinity maturation with language models and weakly supervised learning

Reference 58

Resolution
unresolved
no resolver link, observed 2026-08-08T18:43:34.178697Z

Source-reported events for the cited work

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source=pdf_text observed=2026-08-08T18:43:34.178697Z digest=sha256:8c9bc3763cf2a469a21f9990f114dfbce7b78ba7876692f96c82023ba73c7418

Observation 44e2019d-5f65-4e17-88ac-c4a1dbb960d1 · outbound

This paper cites Accurate prediction of antibody func- tion and structure using bio-inspired antibody language model.

A Comprehensive Review of Protein Language Models Accurate prediction of antibody func- tion and structure using bio-inspired antibody language model

Reference 60

Resolution
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no resolver link, observed 2026-08-08T18:43:34.188441Z

Source-reported events for the cited work

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source=pdf_text observed=2026-08-08T18:43:34.188441Z digest=sha256:fbfff5788261126c132231e16ea3d7e159283f9f5c2d07c49e96dc1b664e6f1c

Observation 9e687226-eca3-45f5-8e22-f6e64e34adaa · outbound

This paper cites Large scale paired antibody language models.

A Comprehensive Review of Protein Language Models Large scale paired antibody language models

Reference 61

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unresolved
no resolver link, observed 2026-08-08T18:43:34.193378Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-08T18:43:34.193378Z digest=sha256:87905a41af27e53bedfdc27bd9d70d9bb2edd50a6cbe25d581965d3013b305c5

Observation e3773199-d3fe-4e6d-96f6-6c0cb64537be · outbound

This paper cites Saprot: Protein language modeling with structure-aware vocabulary.

A Comprehensive Review of Protein Language Models Saprot: Protein language modeling with structure-aware vocabulary

Reference 62

Resolution
unresolved
no resolver link, observed 2026-08-08T18:43:34.198188Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-08T18:43:34.198188Z digest=sha256:fbe5467fd1aa30b70e284846d9763335f04abf8d297b895efea2f823e700f8ff

Observation 52d6ccd6-c8f7-4225-bee7-6a20a388db58 · outbound

This paper cites Petribert: Augmenting bert with tridimensional encoding for inverse protein folding and design.

A Comprehensive Review of Protein Language Models Petribert: Augmenting bert with tridimensional encoding for inverse protein folding and design

Reference 63

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no resolver link, observed 2026-08-08T18:43:34.203042Z

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source=pdf_text observed=2026-08-08T18:43:34.203042Z digest=sha256:8e6dff9f62662b5505c8a92409d825f10b4b3fe531d2e8ae82f3103d97e2a592

Observation 1e7067d9-37ee-415d-b677-ebe1ace0ed2c · outbound

This paper cites Msa transformer.

A Comprehensive Review of Protein Language Models Msa transformer

Reference 64

Resolution
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no resolver link, observed 2026-08-08T18:43:34.207604Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-08T18:43:34.207604Z digest=sha256:57fb75c1c94bd0fa7cdd0bcb39e9dd86fe6e0b1b28ef5373d85accbeac3ba686

Observation 72d8c624-9048-442a-8b5e-ec340dcf34b6 · outbound

This paper cites Enhancing the Protein Tertiary Structure Prediction by Multiple Sequence Alignment Generation.

A Comprehensive Review of Protein Language Models Enhancing the Protein Tertiary Structure Prediction by Multiple Sequence Alignment Generation

Reference 65

Resolution
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no resolver link, observed 2026-08-08T18:43:34.212199Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-08T18:43:34.212199Z digest=sha256:aa93ecf98fe16f723e109076ac1a6146bb9f22ee3dd23c0b2a95338805123e24

Observation 3bbe578f-da88-4276-a860-345c045ea0cb · outbound

This paper cites ProGen: Language Modeling for Protein Generation.

A Comprehensive Review of Protein Language Models ProGen: Language Modeling for Protein Generation

Reference 66

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unresolved
no resolver link, observed 2026-08-08T18:43:34.217184Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-08T18:43:34.217184Z digest=sha256:48183a6df3c016896fcb17a7a2d2281dbe36f1753ad182f57059f328f6aa6ae0

Observation 8c8cf0b7-db1a-450a-8561-1440eeefb6eb · outbound

This paper cites Progen2: exploring the boundaries of protein language models.

A Comprehensive Review of Protein Language Models Progen2: exploring the boundaries of protein language models

Reference 67

Resolution
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no resolver link, observed 2026-08-08T18:43:34.222053Z

Source-reported events for the cited work

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source=pdf_text observed=2026-08-08T18:43:34.222053Z digest=sha256:e2dfb4e80424f1f43ebd93d86278f6b45d6958a2370aadd1744df8bfa4dd3717

Observation 0626e956-7ee8-448c-9970-274d184071c9 · outbound

This paper cites RITA: a Study on Scaling Up Generative Protein Sequence Models.

A Comprehensive Review of Protein Language Models RITA: a Study on Scaling Up Generative Protein Sequence Models

Reference 68

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Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-08T18:43:34.226380Z digest=sha256:43fa4f3928d1484b13aede74c04a8b8bc8ec7d8c0d783f85abab1fceac9deae0

Observation e555a9d1-d0a1-4bfe-ade4-41517af50007 · outbound

This paper cites Prot- gpt2 is a deep unsupervised language model for protein design.

A Comprehensive Review of Protein Language Models Prot- gpt2 is a deep unsupervised language model for protein design

Reference 69

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no resolver link, observed 2026-08-08T18:43:34.231175Z

Source-reported events for the cited work

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source=pdf_text observed=2026-08-08T18:43:34.231175Z digest=sha256:44a48ca22fa724e0b5208200f86afc89c16c68b5ca7253b52dda0e8d9277170a

Observation 456d726e-c5f3-422a-a152-9748085611a5 · outbound

This paper cites Poet: A generative model of protein families as sequences-of- sequences.

A Comprehensive Review of Protein Language Models Poet: A generative model of protein families as sequences-of- sequences

Reference 70

Resolution
unresolved
no resolver link, observed 2026-08-08T18:43:34.235519Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-08T18:43:34.235519Z digest=sha256:1a154f65728e13dd00fc3d1e9d364a65af042ba23b7cf006fed67760cdae8055

Observation 2cfd5ce1-15ee-4e3c-9ff1-486b3ae99d4b · outbound

This paper cites Generative language modeling for antibody design.

A Comprehensive Review of Protein Language Models Generative language modeling for antibody design

Reference 71

Resolution
unresolved
no resolver link, observed 2026-08-08T18:43:34.240072Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-08T18:43:34.240072Z digest=sha256:50c191a6a509b31347fa2bbf271dacdc2340da3d615add95eac81ecfac16fb8d

Observation 55b4e733-7b5f-4d29-b5d6-a7d9da6fcaac · outbound

This paper cites Zymctrl: a conditional language model for the controllable generation of artificial enzymes.

A Comprehensive Review of Protein Language Models Zymctrl: a conditional language model for the controllable generation of artificial enzymes

Reference 72

Resolution
unresolved
no resolver link, observed 2026-08-08T18:43:34.244770Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-08T18:43:34.244770Z digest=sha256:b8bdeed98b0505f3da2942ce80755aaf8ae7dd4e8f2c72724cdb6abf480b784c

Observation 18e4cc5a-99cd-4914-84d0-07076bc6e039 · outbound

This paper cites ProLLaMA: A Protein Large Language Model for Multi-Task Protein Language Processing.

A Comprehensive Review of Protein Language Models ProLLaMA: A Protein Large Language Model for Multi-Task Protein Language Processing

Reference 73

Resolution
unresolved
no resolver link, observed 2026-08-08T18:43:34.249521Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-08T18:43:34.249521Z digest=sha256:eeb9a9034f90a9a5b391240a5298429cb6d11bd200413f6215b776030eca6f38

Observation 6b812a0f-5952-44a1-9e7e-ee1edca3b148 · outbound

This paper cites Generative Antibody Design for Complementary Chain Pairing Sequences through Encoder-Decoder Language Model.

A Comprehensive Review of Protein Language Models Generative Antibody Design for Complementary Chain Pairing Sequences through Encoder-Decoder Language Model

Reference 74

Resolution
verified exact
local_arxiv, observed 2026-08-08T18:43:35.107929Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

source=pdf_text observed=2026-08-08T18:43:34.254566Z digest=sha256:c92c11c50e29a43e092867d66abf04b8c0b0e96969f9b72535aefc0f9307ac30

Observation 35028b98-79ec-45d2-818f-f23ecc27a02f · outbound

This paper cites Exploring the limits of transfer learning with a unified text-to-text transformer.

A Comprehensive Review of Protein Language Models Exploring the limits of transfer learning with a unified text-to-text transformer

Reference 75

Resolution
unresolved
no resolver link, observed 2026-08-08T18:43:34.259377Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-08T18:43:34.259377Z digest=sha256:128febf2bb362c76cb2b941fb214e21bda2ba4878f8c684126fc52c5e0175dd4

Observation 633162ec-5106-4113-8b4c-babe38927d2a · outbound

This paper cites xTrimoPGLM: Unified 100B-Scale Pre-trained Transformer for Deciphering the Language of Protein.

A Comprehensive Review of Protein Language Models xTrimoPGLM: Unified 100B-Scale Pre-trained Transformer for Deciphering the Language of Protein

Reference 76

Resolution
unresolved
no resolver link, observed 2026-08-08T18:43:34.263934Z

Source-reported events for the cited work

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source=pdf_text observed=2026-08-08T18:43:34.263934Z digest=sha256:6394eeaa1f73dfdb110bcbfc96857b18da79a56f9a161c8a36b93f65ede33aea

Observation 9fce844a-5f83-4b3e-b875-582be6dd2bcb · outbound

This paper cites GLM: General Language Model Pretraining with Autoregressive Blank Infilling.

A Comprehensive Review of Protein Language Models GLM: General Language Model Pretraining with Autoregressive Blank Infilling

Reference 77

Resolution
unresolved
no resolver link, observed 2026-08-08T18:43:34.269002Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-08T18:43:34.269002Z digest=sha256:de1f4c7a630564008652d00915384873f774e22546d0ac5cf2a3a4e43daa6e2b

Observation 3772ab28-fe58-4f2c-8df7-12460b7c159a · outbound

This paper cites Efficient and accurate sequence generation with small-scale protein language models.

A Comprehensive Review of Protein Language Models Efficient and accurate sequence generation with small-scale protein language models

Reference 78

Resolution
unresolved
no resolver link, observed 2026-08-08T18:43:34.273928Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-08T18:43:34.273928Z digest=sha256:82f719b3ba387a40027c92511dfcb175954c4d4d3f38ae9f08b3d3515d858d4f

Observation 061453d0-7864-4f50-89f0-a2f476cc9292 · outbound

This paper cites Ankh: Optimized Protein Language Model Unlocks General-Purpose Modelling.

A Comprehensive Review of Protein Language Models Ankh: Optimized Protein Language Model Unlocks General-Purpose Modelling

Reference 79

Resolution
unresolved
no resolver link, observed 2026-08-08T18:43:34.278481Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-08T18:43:34.278481Z digest=sha256:9909dcb7a61b175ecc6b38b2acfb0c0415e467ed4279c0525511221dbc28526c

Observation 32c8086e-2fb3-4c8c-be6f-3bc0c69702b2 · outbound

This paper cites Bilingual language model for protein sequence and structure.

A Comprehensive Review of Protein Language Models Bilingual language model for protein sequence and structure

Reference 80

Resolution
unresolved
no resolver link, observed 2026-08-08T18:43:34.283362Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-08T18:43:34.283362Z digest=sha256:e870accbf4c365159595e5edc76e8125131d6a00bf537235c4b8cee036b74ac3

Observation 172c9dfd-c34d-4087-b65a-d36d5e042d43 · outbound

This paper cites Prosst: Protein language modeling with quantized structure and disentangled attention.

A Comprehensive Review of Protein Language Models Prosst: Protein language modeling with quantized structure and disentangled attention

Reference 81

Resolution
unresolved
no resolver link, observed 2026-08-08T18:43:34.288014Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-08T18:43:34.288014Z digest=sha256:529fd960b09bdb4ca783a90f35122830e6e513a85c5603e42ae5fcae5b69ccda

Observation e8be2a3a-fdea-46d9-acb8-3dc215812321 · outbound

This paper cites Structure-informed language models are protein designers.

A Comprehensive Review of Protein Language Models Structure-informed language models are protein designers

Reference 82

Resolution
unresolved
no resolver link, observed 2026-08-08T18:43:34.292888Z

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Observation 2a6279b4-cf7f-4719-ab76-6aa728168b1c · outbound

This paper cites Self-Attention with Relative Position Representations.

A Comprehensive Review of Protein Language Models Self-Attention with Relative Position Representations

Reference 83

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Observation 4f3a8f41-3c39-4353-b3e1-26eaf60080f0 · outbound

This paper cites The Curious Case of Absolute Position Embeddings.

A Comprehensive Review of Protein Language Models The Curious Case of Absolute Position Embeddings

Reference 84

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Observation 8ee2ab4f-7a3d-469f-b06d-fb9bd6dc62a0 · outbound

This paper cites Roformer: Enhanced trans- former with rotary position embedding.

A Comprehensive Review of Protein Language Models Roformer: Enhanced trans- former with rotary position embedding

Reference 85

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Unavailable: canonical work link unavailable.

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Observation 9f5937da-e400-49d6-b7cc-d0dcede75bc4 · outbound

This paper cites Transformer-XL: Attentive Language Models Beyond a Fixed-Length Context.

A Comprehensive Review of Protein Language Models Transformer-XL: Attentive Language Models Beyond a Fixed-Length Context

Reference 86

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Observation 1d948662-9eb2-4e4e-9610-846b9efda4f0 · outbound

This paper cites DeBERTa: Decoding-enhanced BERT with Disentangled Attention.

A Comprehensive Review of Protein Language Models DeBERTa: Decoding-enhanced BERT with Disentangled Attention

Reference 87

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Observation c502715a-2d8f-493c-a977-04b52d759047 · outbound

This paper cites Train Short, Test Long: Attention with Linear Biases Enables Input Length Extrapolation.

A Comprehensive Review of Protein Language Models Train Short, Test Long: Attention with Linear Biases Enables Input Length Extrapolation

Reference 88

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Observation c6246150-68e4-45f0-a9b8-cac5f9dd3345 · outbound

This paper cites Multilingual elmo and the effects of corpus sampling.

A Comprehensive Review of Protein Language Models Multilingual elmo and the effects of corpus sampling

Reference 89

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Observation 91dc9350-4611-4b55-be11-d306bb3456b7 · outbound

This paper cites Protein language models: Is scaling necessary? bioRxiv, pages 2024–09, 2024.

A Comprehensive Review of Protein Language Models Protein language models: Is scaling necessary? bioRxiv, pages 2024–09, 2024

Reference 90

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Source-reported events for the cited work

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Observation 76e59554-0053-467a-a237-f31180872b95 · outbound

This paper cites Scaling Laws for Neural Language Models.

A Comprehensive Review of Protein Language Models Scaling Laws for Neural Language Models

Reference 91

Resolution
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Source-reported events for the cited work

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Observation 519f0d3b-b7b5-4b5f-8043-883ac9a38e26 · outbound

This paper cites Improving language understanding by generative pre-training.

A Comprehensive Review of Protein Language Models Improving language understanding by generative pre-training

Reference 92

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Observation 5d6ac91d-10c9-4c01-b167-4b0d18cf3b4c · outbound

This paper cites Language models are unsupervised multitask learners.

A Comprehensive Review of Protein Language Models Language models are unsupervised multitask learners

Reference 93

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Observation 8090d6bb-9496-425a-b7b8-8655a749889b · outbound

This paper cites Language Models are Few-Shot Learners.

A Comprehensive Review of Protein Language Models Language Models are Few-Shot Learners

Reference 94

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Source-reported events for the cited work

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Observation 6015ba23-38d8-4728-8c39-369c6f247e6a · outbound

This paper cites JOURNAL OF LATEX CLASS FILES, VOL.

A Comprehensive Review of Protein Language Models JOURNAL OF LATEX CLASS FILES, VOL

Reference 95

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Observation 36941bf3-b8d3-4144-94a0-21035bfcd42c · outbound

This paper cites Uniprot: a worldwide hub of pro- tein knowledge.

A Comprehensive Review of Protein Language Models Uniprot: a worldwide hub of pro- tein knowledge

Reference 96

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Observation 29589f7d-e10b-46cc-b58e-4645010fe05d · outbound

This paper cites Edittotrembl: a distributed approach to high- quality automated protein sequence annotation.

A Comprehensive Review of Protein Language Models Edittotrembl: a distributed approach to high- quality automated protein sequence annotation

Reference 97

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Observation cf204d54-84b0-40bd-80fc-20fd0d5a0d11 · outbound

This paper cites Uniclust databases of clustered and deeply anno- tated protein sequences and alignments.

A Comprehensive Review of Protein Language Models Uniclust databases of clustered and deeply anno- tated protein sequences and alignments

Reference 98

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Observation a2807fe6-b7ec-4981-829b-f9e8ae396036 · outbound

This paper cites Highly accurate protein structure prediction with alphafold.

A Comprehensive Review of Protein Language Models Highly accurate protein structure prediction with alphafold

Reference 99

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Observation ca998bbf-5cd0-4c61-aed2-ac1ceabdbccb · outbound

This paper cites Uniprot archive.

A Comprehensive Review of Protein Language Models Uniprot archive

Reference 100

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Source-reported events for the cited work

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Observation 498023bb-10ea-425d-b990-7c84d7462416 · outbound

This paper cites Pfam: The protein families database in 2021.

A Comprehensive Review of Protein Language Models Pfam: The protein families database in 2021

Reference 101

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Observation 3ec340bb-7a8f-4bb3-9bd9-74b0025b87c1 · outbound

This paper cites Pfam: the protein families database.

A Comprehensive Review of Protein Language Models Pfam: the protein families database

Reference 102

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Source-reported events for the cited work

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Observation c90aeb7d-4075-45a5-92ab-c7b072f4072c · outbound

This paper cites Evaluating protein transfer learning with tape.

A Comprehensive Review of Protein Language Models Evaluating protein transfer learning with tape

Reference 103

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Source-reported events for the cited work

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Pith citing papers

Observation 77004d3b-9afb-4cc3-8130-603cb1174248 · inbound

Constrained Sliced Wasserstein Embedding cites this paper.

Constrained Sliced Wasserstein Embedding A Comprehensive Review of Protein Language Models

Reference 106

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Observation 5431070d-026a-4d5d-8813-4b2f22649764 · inbound

EvoStruct: Bridging Evolutionary and Structural Priors for Antibody CDR Design via Protein Language Model Adaptation cites this paper.

EvoStruct: Bridging Evolutionary and Structural Priors for Antibody CDR Design via Protein Language Model Adaptation A Comprehensive Review of Protein Language Models

Reference 187

Resolution
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Observation 0e843f8a-ddfc-4e73-ab03-042923c60f45 · inbound

ConTact: Contact-First Antibody CDR Design via Explicit Interface Reasoning cites this paper.

ConTact: Contact-First Antibody CDR Design via Explicit Interface Reasoning A Comprehensive Review of Protein Language Models

Reference 187

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verified exact
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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

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Observation 47c3dbb9-aeaf-4806-b9f2-91ddebf5e44e · inbound

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AgForce Enables Antigen-conditioned Generative Antibody Design A Comprehensive Review of Protein Language Models

Reference 187

Resolution
verified exact
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Observation 1ef55337-6f40-4eb1-9f3e-8f846ca6ad92 · inbound

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EpiFormer: Learning Antigen-Antibody Interactions for Epitope Prediction via Geometric Deep Learning A Comprehensive Review of Protein Language Models

Reference 163

Resolution
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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

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Observation 59436795-4f5c-4c1d-ba50-858edd3fc86f · inbound

Modeling Protein Evolution with Generative Models: from Extant Sequence Data to Evolutionary Dynamics cites this paper.

Modeling Protein Evolution with Generative Models: from Extant Sequence Data to Evolutionary Dynamics A Comprehensive Review of Protein Language Models

Reference 81

Resolution
verified exact
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Observation b2895b3d-195a-4315-9541-e90f20a91c83 · inbound

Variable-Length Generative Protein Design via Generalized Poisson Flow cites this paper.

Variable-Length Generative Protein Design via Generalized Poisson Flow A Comprehensive Review of Protein Language Models

Reference 49

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Unavailable: canonical work link unavailable.

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