Paper Citation Record · LEDGER

A Comprehensive Review of Protein Language Models

As of 8 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.

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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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External citation measurements

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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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Observation e90b71d6-4249-46fa-a14f-f6515fc6f892 · outbound

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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Observation 54be4bbd-26e2-4c54-b2fe-bfbbce837f3f · outbound

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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Observation 1767bf09-21c0-4b0e-a5a2-fc4f1e7d6038 · outbound

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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Observation fc224fc3-4498-49cd-b384-9490468146b9 · outbound

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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Observation f3a27974-6307-4baf-82ef-3480aa6623c8 · outbound

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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Observation e9891b56-af93-4d25-a1b4-b6f64212ba8b · outbound

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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Observation 0012902e-8fd9-495e-bba6-02220087430b · outbound

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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Observation da9ba811-a7ac-4002-9d3b-1a9591a40ba7 · outbound

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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Observation c45a8149-4569-48f1-b552-d71abf153247 · outbound

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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Observation 6180566f-c7b7-4a90-98cb-db91b40ee3bc · outbound

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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Observation e2376884-fff0-48ec-8fc7-e72333079481 · outbound

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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Observation 454c6c68-5f52-4c0e-a0db-a0dea44a74e0 · outbound

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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Observation 7bba43f0-7eec-497e-be86-b8cc9ec4cbb4 · outbound

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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Observation 1c1fb4e4-dd36-4d90-8021-013580ce4a92 · outbound

This paper cites Recurrent Neural Network Regularization.

A Comprehensive Review of Protein Language Models Recurrent Neural Network Regularization

Reference 20

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Observation bc704e6f-937f-418b-8fde-498479950427 · outbound

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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Observation bd14fc15-ea33-4add-9869-b3197f4225c7 · outbound

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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Observation 438b8f6d-afe6-4ecf-8899-2b4d2f33b7ff · outbound

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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Observation 79a602e8-ffcc-4903-b575-961b4c989134 · outbound

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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Observation a9abca92-154b-4bad-a78b-988a5a54d5ba · outbound

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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Observation 3293e77c-8f89-4081-8d0a-616e1edd1675 · outbound

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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Observation 8103956c-2e20-4752-aafc-b403f37c35af · outbound

This paper cites Regularizing and Optimizing LSTM Language Models.

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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Observation 47b42730-e0d1-46df-bf19-6e9d1278db0d · outbound

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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Observation 8f603199-a788-41bf-b54a-76f73d22e180 · outbound

This paper cites Faster and smaller n-gram language models.

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

Reference 30

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Observation bccd6946-0298-4b69-bdd4-9f2ae9336f64 · outbound

This paper cites Optimizing multi-gpu parallelization strategies for deep learning training.

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

Reference 31

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Observation 51d3a103-5dbf-4707-a139-14d12fff911b · outbound

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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This paper cites BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding.

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

Reference 34

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Observation 55f8035b-fd29-430f-b10f-01fed8e52a10 · outbound

This paper cites A Structured Self-attentive Sentence Embedding.

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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This paper cites Unified language model pre-training for nat- ural language understanding and generation.

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

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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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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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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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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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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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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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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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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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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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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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

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

Reference 54

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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

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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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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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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

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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

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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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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

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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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This paper cites Msa transformer.

A Comprehensive Review of Protein Language Models Msa transformer

Reference 64

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

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

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

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

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

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

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

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

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Observation b2895b3d-195a-4315-9541-e90f20a91c83 · inbound

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