PFMBench: Protein Foundation Model Benchmark | Paper Citation Record

Observation b422274c-0ee7-4787-aec7-d69c20e48850 · outbound

This paper cites Deeploc: prediction of protein subcellular localization using deep learning.

PFMBench: Protein Foundation Model Benchmark Deeploc: prediction of protein subcellular localization using deep learning

Reference 1

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T11:59:36.534973Z

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Observation 8f7c2052-2681-4107-b43b-f86636c2d924 · outbound

This paper cites Gene ontology: tool for the unification of biology.Nature genetics, 25(1):25–29, 2000.

PFMBench: Protein Foundation Model Benchmark Gene ontology: tool for the unification of biology.Nature genetics, 25(1):25–29, 2000

Reference 2

Resolution: unresolved; no resolver link, observed 2026-08-07T11:59:19.439600Z

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Observation 2222f298-0d96-43b7-8f17-ec2fba926f42 · outbound

This paper cites The enzyme database in 2000.Nucleic acids research, 28(1):304–305, 2000.

PFMBench: Protein Foundation Model Benchmark The enzyme database in 2000.Nucleic acids research, 28(1):304–305, 2000

Reference 3

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T11:59:36.395442Z

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Observation a3713fab-9a0b-42ff-aad8-c55d79826ebc · outbound

This paper cites Foundation models of protein sequences: A brief overview.Current Opinion in Structural Biology, 91:103004, 2025.

PFMBench: Protein Foundation Model Benchmark Foundation models of protein sequences: A brief overview.Current Opinion in Structural Biology, 91:103004, 2025

Reference 4

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T11:59:36.223727Z

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Observation 03af1fe1-fdf1-4f00-a061-50cb1eb26b60 · outbound

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

PFMBench: Protein Foundation Model Benchmark xTrimoPGLM: Unified 100B-Scale Pre-trained Transformer for Deciphering the Language of Protein

Reference 5

Resolution: unresolved; no resolver link, observed 2026-08-07T11:59:19.696834Z

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Observation 0ee5416a-b294-4127-a4cf-5cd653ec56d2 · outbound

This paper cites Structure-aware protein solubility prediction from sequence through graph convolutional network and predicted contact map.Journal of cheminformatics, 13:1–10, 2021.

PFMBench: Protein Foundation Model Benchmark Structure-aware protein solubility prediction from sequence through graph convolutional network and predicted contact map.Journal of cheminformatics, 13:1–10, 2021

Reference 6

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T11:59:36.105084Z

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Observation 04c2fcf2-6869-4d20-8610-4e06cee242cb · outbound

This paper cites Flip: Benchmark tasks in fitness landscape inference for proteins.

PFMBench: Protein Foundation Model Benchmark Flip: Benchmark tasks in fitness landscape inference for proteins

Reference 7

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T11:59:35.951769Z

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Observation fad04567-681c-4375-8ad9-05507dc20bd9 · outbound

This paper cites Qlora: Efficient finetuning of quantized llms.Advances in neural information processing systems, 36:10088– 10115, 2023.

PFMBench: Protein Foundation Model Benchmark Qlora: Efficient finetuning of quantized llms.Advances in neural information processing systems, 36:10088– 10115, 2023

Reference 8

Resolution: unresolved; no resolver link, observed 2026-08-07T11:59:19.906232Z

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Observation 57340fc8-bb45-45f8-8d52-79866ff887bc · outbound

This paper cites LoCA: Location-Aware Cosine Adaptation for Parameter-Efficient Fine-Tuning.

PFMBench: Protein Foundation Model Benchmark LoCA: Location-Aware Cosine Adaptation for Parameter-Efficient Fine-Tuning

Reference 9

Resolution: unresolved; no resolver link, observed 2026-08-07T11:59:19.999097Z

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Observation 08508e91-b3a6-4810-b6c8-12016f7bdee6 · outbound

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

PFMBench: Protein Foundation Model Benchmark Ankh: Optimized Protein Language Model Unlocks General-Purpose Modelling

Reference 10

Resolution: unresolved; no resolver link, observed 2026-08-07T11:59:20.070055Z

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Observation 5af32e7d-96e7-4007-a3db-f4a64e441522 · outbound

This paper cites Prottrans: towards cracking the language of life’s code through self-supervised learning.IEEE Transactions on Pattern Analysis and Machine Intelligence, 44:7112–7127, 2021.

PFMBench: Protein Foundation Model Benchmark Prottrans: towards cracking the language of life’s code through self-supervised learning.IEEE Transactions on Pattern Analysis and Machine Intelligence, 44:7112–7127, 2021

Reference 11

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T11:59:35.832226Z

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Observation 1b32e51f-82be-437a-8209-63756a049a0f · outbound

This paper cites Protgpt2 is a deep unsupervised language model for protein design.Nature communications, 13(1):4348, 2022.

PFMBench: Protein Foundation Model Benchmark Protgpt2 is a deep unsupervised language model for protein design.Nature communications, 13(1):4348, 2022

Reference 12

Resolution: unresolved; no resolver link, observed 2026-08-07T11:59:20.260358Z

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Observation 91d6685c-f77e-4d6b-8ded-cff2054acf94 · outbound

This paper cites Deep learning prediction of enzyme optimum ph.bioRxiv, pages 2023–06, 2023.

PFMBench: Protein Foundation Model Benchmark Deep learning prediction of enzyme optimum ph.bioRxiv, pages 2023–06, 2023

Reference 13

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T11:59:35.651269Z

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Observation 5a978e3e-5132-453c-ba85-58ad96f50842 · outbound

This paper cites Proteinin- vbench: Benchmarking protein inverse folding on diverse tasks, models, and metrics.Advances in Neural Information Processing Systems, 36:68207–68220, 2023.

PFMBench: Protein Foundation Model Benchmark Proteinin- vbench: Benchmarking protein inverse folding on diverse tasks, models, and metrics.Advances in Neural Information Processing Systems, 36:68207–68220, 2023

Reference 14

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T11:59:35.549994Z

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Observation b84c7089-8e67-4f8e-8e82-a1925412efcf · outbound

This paper cites Quan- titative missense variant effect prediction using large-scale mutagenesis data.Cell systems, 6(1):116–124, 2018.

PFMBench: Protein Foundation Model Benchmark Quan- titative missense variant effect prediction using large-scale mutagenesis data.Cell systems, 6(1):116–124, 2018

Reference 15

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T11:59:35.420228Z

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Observation 8de1245b-010f-4fbd-97ef-fc823d7380dd · outbound

This paper cites Foundation models in bioinformatics.National Science Review, page nwaf028, 2025.

PFMBench: Protein Foundation Model Benchmark Foundation models in bioinformatics.National Science Review, page nwaf028, 2025

Reference 16

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T11:59:35.282311Z

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Observation 966b1588-6616-417d-87e0-e8d6e2b26b6e · outbound

This paper cites Using support vector machine combined with auto covariance to predict protein–protein interactions from protein sequences.

PFMBench: Protein Foundation Model Benchmark Using support vector machine combined with auto covariance to predict protein–protein interactions from protein sequences

Reference 17

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T11:59:35.154131Z

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Observation 188a5d81-8fe2-4a6c-befe-d40942636ac7 · outbound

This paper cites Simulating 500 million years of evolution with a language model.Science, page eads0018, 2025.

PFMBench: Protein Foundation Model Benchmark Simulating 500 million years of evolution with a language model.Science, page eads0018, 2025

Reference 18

Resolution: unresolved; no resolver link, observed 2026-08-07T11:59:20.788335Z

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Observation 542d615f-8cbd-4ef9-a5ca-bb34d001a2b0 · outbound

This paper cites To- wards a unified view of parameter-efficient transfer learning.

PFMBench: Protein Foundation Model Benchmark To- wards a unified view of parameter-efficient transfer learning

Reference 19

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T11:59:34.994670Z

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Observation 88a86a35-33ce-4604-a0cc-84d71fe4fe17 · outbound

This paper cites Deep residual learning for image recognition.

PFMBench: Protein Foundation Model Benchmark Deep residual learning for image recognition

Reference 20

Resolution: unresolved; no resolver link, observed 2026-08-07T11:59:20.927765Z

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Observation 0fc4eb12-f7bc-43d5-9e48-7ae9ba2d9409 · outbound

This paper cites Bilingual language model for protein sequence and structure.NAR Genomics and Bioinformatics, 6(4):lqae150, 2024.

PFMBench: Protein Foundation Model Benchmark Bilingual language model for protein sequence and structure.NAR Genomics and Bioinformatics, 6(4):lqae150, 2024

Reference 21

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T11:59:34.855110Z

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Observation 6a9371f0-b286-4f0c-ba81-f93a736274b6 · outbound

This paper cites Long short-term memory.Neural computation, 9(8):1735–1780, 1997.

PFMBench: Protein Foundation Model Benchmark Long short-term memory.Neural computation, 9(8):1735–1780, 1997

Reference 22

Resolution: unresolved; no resolver link, observed 2026-08-07T11:59:21.079778Z

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Observation e0225e32-a0e0-498c-9768-603226001ae0 · outbound

This paper cites Parameter-efficient transfer learning for nlp.

PFMBench: Protein Foundation Model Benchmark Parameter-efficient transfer learning for nlp

Reference 23

Resolution: unresolved; no resolver link, observed 2026-08-07T11:59:21.186074Z

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Observation 96fe74a0-3847-409a-8929-5b4eaa9c4dd3 · outbound

This paper cites Lora: Low-rank adaptation of large language models.ICLR, 1(2):3, 2022.

PFMBench: Protein Foundation Model Benchmark Lora: Low-rank adaptation of large language models.ICLR, 1(2):3, 2022

Reference 24

Resolution: unresolved; no resolver link, observed 2026-08-07T11:59:21.334027Z

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Observation ff8191a4-627b-4386-8f80-7dfd906eac53 · outbound

This paper cites Exploring evolution-aware &-free protein language models as protein function predictors.

PFMBench: Protein Foundation Model Benchmark Exploring evolution-aware &-free protein language models as protein function predictors

Reference 25

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T11:59:34.704786Z

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Observation e99e4dc7-2c5b-4fe7-96cb-6e26ad3b8700 · outbound

This paper cites Meltome at- las—thermal proteome stability across the tree of life.Nature methods, 17(5):495–503, 2020.

PFMBench: Protein Foundation Model Benchmark Meltome at- las—thermal proteome stability across the tree of life.Nature methods, 17(5):495–503, 2020

Reference 26

Resolution: unresolved; no resolver link, observed 2026-08-07T11:59:21.572191Z

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Observation 4f7517b8-462b-4215-82a4-926de7c33b89 · outbound

This paper cites Deepsol: a deep learning framework for sequence-based protein solubility prediction.

PFMBench: Protein Foundation Model Benchmark Deepsol: a deep learning framework for sequence-based protein solubility prediction

Reference 27

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T11:59:34.523476Z

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Observation 9ec1019f-846b-4d2f-b54f-8a418b5a2041 · outbound

This paper cites Netsurfp-2.0: Improved prediction of protein structural features by integrated deep learning.Proteins: Structure, Function, and Bioinformatics, 87(6):520–527, 2019.

PFMBench: Protein Foundation Model Benchmark Netsurfp-2.0: Improved prediction of protein structural features by integrated deep learning.Proteins: Structure, Function, and Bioinformatics, 87(6):520–527, 2019

Reference 28

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T11:59:34.396051Z

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Observation 1382aca7-51bc-4dac-8642-c556d340c6a8 · outbound

This paper cites an unresolved cited work.

PFMBench: Protein Foundation Model Benchmark Unresolved cited work

Reference 29

Resolution: unresolved; raw_fallback, observed 2026-08-07T11:59:34.250094Z

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Observation c210d1db-24c4-4bf5-981e-18760396a9e9 · outbound

This paper cites The power of scale for parameter-efficient prompt tuning.

PFMBench: Protein Foundation Model Benchmark The power of scale for parameter-efficient prompt tuning

Reference 30

Resolution: unresolved; no resolver link, observed 2026-08-07T11:59:22.221195Z

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Observation ade1e79d-19d9-47c8-a4e4-caae3e09f90d · outbound

This paper cites Deep learning-based k cat prediction enables improved enzyme- constrained model reconstruction.Nature Catalysis, 5(8):662–672, 2022.

PFMBench: Protein Foundation Model Benchmark Deep learning-based k cat prediction enables improved enzyme- constrained model reconstruction.Nature Catalysis, 5(8):662–672, 2022

Reference 31

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T11:59:34.115359Z

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Observation 9d0d978d-a9e7-495d-957f-077bffeec8ae · outbound

This paper cites Learning deep representations of enzyme thermal adaptation.Protein Science, 31(12):e4480, 2022.

PFMBench: Protein Foundation Model Benchmark Learning deep representations of enzyme thermal adaptation.Protein Science, 31(12):e4480, 2022

Reference 32

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T11:59:33.993688Z

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Observation 5246b111-9128-403a-a18d-d86fdb4b2c0a · outbound

This paper cites Progress and opportunities of foundation models in bioinformatics.

PFMBench: Protein Foundation Model Benchmark Progress and opportunities of foundation models in bioinformatics

Reference 33

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T11:59:33.845531Z

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Observation db508d5b-4081-4cca-bda4-757574c1c482 · outbound

This paper cites Prefix-tuning: Optimizing continuous prompts for generation.

PFMBench: Protein Foundation Model Benchmark Prefix-tuning: Optimizing continuous prompts for generation

Reference 34

Resolution: unresolved; no resolver link, observed 2026-08-07T11:59:22.615504Z

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Observation 2ce0ff9a-f808-433c-af12-21cbfec885c2 · outbound

This paper cites Evolutionary-scale prediction of atomic-level protein structure with a language model.Science, 379(6637):1123–1130, 2023.

PFMBench: Protein Foundation Model Benchmark Evolutionary-scale prediction of atomic-level protein structure with a language model.Science, 379(6637):1123–1130, 2023

Reference 35

Resolution: unresolved; no resolver link, observed 2026-08-07T11:59:22.697858Z

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Observation 99b323f8-2231-45d5-9768-f51b2c300f38 · outbound

This paper cites Few-shot parameter-efficient fine-tuning is better and cheaper than in-context learning.Advances in Neural Information Processing Systems, 35:1950–1965, 2022.

PFMBench: Protein Foundation Model Benchmark Few-shot parameter-efficient fine-tuning is better and cheaper than in-context learning.Advances in Neural Information Processing Systems, 35:1950–1965, 2022

Reference 36

Resolution: unresolved; no resolver link, observed 2026-08-07T11:59:22.762592Z

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Observation 6fd36ee6-59af-4b60-ad4c-2e8c3b4ad04d · outbound

This paper cites Bindingdb: a web-accessible database of experimentally determined protein–ligand binding affinities.Nucleic acids research, 35(suppl_1):D198–D201, 2007.

PFMBench: Protein Foundation Model Benchmark Bindingdb: a web-accessible database of experimentally determined protein–ligand binding affinities.Nucleic acids research, 35(suppl_1):D198–D201, 2007

Reference 37

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T11:59:33.710983Z

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No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation e5ed9c18-1a0c-43d0-b719-4830a5ac7c77 · outbound

This paper cites Forging the basis for developing protein–ligand interaction scoring functions.Accounts of chemical research, 50(2):302–309, 2017.

PFMBench: Protein Foundation Model Benchmark Forging the basis for developing protein–ligand interaction scoring functions.Accounts of chemical research, 50(2):302–309, 2017

Reference 38

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T11:59:33.542311Z

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Observation ed625cb8-0cc5-4d47-9b76-ec0879b44678 · outbound

This paper cites Scop: a structural classification of proteins database.Nucleic acids research, 28(1):257–259, 2000.

PFMBench: Protein Foundation Model Benchmark Scop: a structural classification of proteins database.Nucleic acids research, 28(1):257–259, 2000

Reference 39

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T11:59:33.423416Z

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Observation 7fbf1989-d0ab-4dfc-9b89-6ab707a8e361 · outbound

This paper cites Prollama: A protein large language model for multi-task protein language processing.IEEE Transactions on Artificial Intelligence, 2025.

PFMBench: Protein Foundation Model Benchmark Prollama: A protein large language model for multi-task protein language processing.IEEE Transactions on Artificial Intelligence, 2025

Reference 40

Resolution: unresolved; no resolver link, observed 2026-08-07T11:59:23.058220Z

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Observation 0a3642a7-1ab1-418e-97cc-c58865ebbbcb · outbound

This paper cites Large language models generate functional protein sequences across diverse families.Nature biotechnology, 41(8):1099–1106, 2023.

PFMBench: Protein Foundation Model Benchmark Large language models generate functional protein sequences across diverse families.Nature biotechnology, 41(8):1099–1106, 2023

Reference 41

Resolution: unresolved; no resolver link, observed 2026-08-07T11:59:23.190277Z

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Observation f9c19e08-1f65-451f-8f18-03e667fe5bf9 · outbound

This paper cites Peft: State-of-the-art parameter-efficient fine-tuning methods.

PFMBench: Protein Foundation Model Benchmark Peft: State-of-the-art parameter-efficient fine-tuning methods

Reference 42

Resolution: unresolved; no resolver link, observed 2026-08-07T11:59:23.311250Z

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Observation 899526e1-a0e9-4893-b56f-dfc20e21f4d3 · outbound

This paper cites Dora: Enhancing parameter-efficient fine-tuning with dynamic rank distribution.

PFMBench: Protein Foundation Model Benchmark Dora: Enhancing parameter-efficient fine-tuning with dynamic rank distribution

Reference 43

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T11:59:33.256770Z

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Observation 6dff45c2-244c-474f-8ad9-5b77b2aaa31a · outbound

This paper cites Formal limitations on the measurement of mutual infor- mation.

PFMBench: Protein Foundation Model Benchmark Formal limitations on the measurement of mutual infor- mation

Reference 44

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T11:59:33.087161Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation ff135eb6-4097-476f-83dc-982f34f0332b · outbound

This paper cites Skempi: a structural kinetic and energetic database of mutant protein interactions and its use in empirical models.Bioinformatics, 28(20):2600–2607, 2012.

PFMBench: Protein Foundation Model Benchmark Skempi: a structural kinetic and energetic database of mutant protein interactions and its use in empirical models.Bioinformatics, 28(20):2600–2607, 2012

Reference 45

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T11:59:32.944164Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation b83e276a-d577-4ee4-9b10-0b50e98c5be9 · outbound

This paper cites Progen2: exploring the boundaries of protein language models.Cell systems, 14(11):968–978, 2023.

PFMBench: Protein Foundation Model Benchmark Progen2: exploring the boundaries of protein language models.Cell systems, 14(11):968–978, 2023

Reference 46

Resolution: unresolved; no resolver link, observed 2026-08-07T11:59:23.854105Z

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Observation ce426fed-6c45-4f97-b080-9eabf20d85a4 · outbound

This paper cites Proteingym: Large- scale benchmarks for protein fitness prediction and design.Advances in Neural Information Processing Systems, 36:64331–64379, 2023.

PFMBench: Protein Foundation Model Benchmark Proteingym: Large- scale benchmarks for protein fitness prediction and design.Advances in Neural Information Processing Systems, 36:64331–64379, 2023

Reference 47

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T11:59:32.777669Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation 9355f709-f234-46d4-a4a5-dc56e9681f24 · outbound

This paper cites Large-scale prediction of human protein- protein interactions from amino acid sequence based on latent topic features.Journal of proteome research, 9(10):4992–5001, 2010.

PFMBench: Protein Foundation Model Benchmark Large-scale prediction of human protein- protein interactions from amino acid sequence based on latent topic features.Journal of proteome research, 9(10):4992–5001, 2010

Reference 48

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T11:59:32.631220Z

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No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation a78be927-0119-4698-984d-8a0e53ec7ae7 · outbound

This paper cites AdapterFusion: Non-Destructive Task Composition for Transfer Learning.

PFMBench: Protein Foundation Model Benchmark AdapterFusion: Non-Destructive Task Composition for Transfer Learning

Reference 49

Resolution: unresolved; no resolver link, observed 2026-08-07T11:59:24.271111Z

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Observation 16bc98ca-7b47-442c-a73a-7a320c2eb5e9 · outbound

This paper cites On variational bounds of mutual information.

PFMBench: Protein Foundation Model Benchmark On variational bounds of mutual information

Reference 50

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T11:59:32.487165Z

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No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation 7f7b70c2-b628-4bda-93d1-b0a2c146f97f · outbound

This paper cites Procyon: A multimodal foundation model for protein phenotypes.BioRxiv, pages 2024–12, 2024.

PFMBench: Protein Foundation Model Benchmark Procyon: A multimodal foundation model for protein phenotypes.BioRxiv, pages 2024–12, 2024

Reference 51

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T11:59:32.355527Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation d0a203bb-e132-49d9-b128-30fa42150bf8 · outbound

This paper cites Evaluating protein transfer learning with tape.Advances in neural information processing systems, 32, 2019.

PFMBench: Protein Foundation Model Benchmark Evaluating protein transfer learning with tape.Advances in neural information processing systems, 32, 2019

Reference 52

Resolution: unresolved; no resolver link, observed 2026-08-07T11:59:24.735664Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 0f4941b1-a72f-4a1f-b07d-cf40247924db · outbound

This paper cites an unresolved cited work.

PFMBench: Protein Foundation Model Benchmark Unresolved cited work

Reference 53

Resolution: unresolved; no resolver link, observed 2026-08-07T11:59:24.911733Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 64e722f7-abb2-490a-b4da-4ce3ef37fe9f · outbound

This paper cites Mmseqs2 enables sensitive protein sequence searching for the analysis of massive data sets.Nature biotechnology, 35(11):1026–1028, 2017.

PFMBench: Protein Foundation Model Benchmark Mmseqs2 enables sensitive protein sequence searching for the analysis of massive data sets.Nature biotechnology, 35(11):1026–1028, 2017

Reference 54

Resolution: unresolved; no resolver link, observed 2026-08-07T11:59:25.059294Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation bd1c07db-e062-4514-a770-413d35234e81 · outbound

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

PFMBench: Protein Foundation Model Benchmark Saprot: Protein language modeling with structure-aware vocabulary

Reference 55

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T11:59:32.194612Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation c0227acb-dbec-400b-96a7-4144e4e410f5 · outbound

This paper cites Protrek: Navigating the protein universe through tri-modal contrastive learning.bioRxiv, pages 2024–05, 2024.

PFMBench: Protein Foundation Model Benchmark Protrek: Navigating the protein universe through tri-modal contrastive learning.bioRxiv, pages 2024–05, 2024

Reference 56

Resolution: unresolved; no resolver link, observed 2026-08-07T11:59:25.397237Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation c64b54d1-b2de-48c7-be25-b3a1a651ce2d · outbound

This paper cites Uniref clusters: a comprehensive and scalable alternative for improving sequence similarity searches.Bioinformatics, 31(6):926–932, 2015.

PFMBench: Protein Foundation Model Benchmark Uniref clusters: a comprehensive and scalable alternative for improving sequence similarity searches.Bioinformatics, 31(6):926–932, 2015

Reference 57

Resolution: unresolved; no resolver link, observed 2026-08-07T11:59:25.544019Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation d8b3d526-ddfd-4f0a-ba80-5ea9f208a2be · outbound

This paper cites Peta: evaluating the impact of protein transfer learning with sub-word tokenization on downstream applications.Journal of Cheminformatics, 16(1):92, 2024.

PFMBench: Protein Foundation Model Benchmark Peta: evaluating the impact of protein transfer learning with sub-word tokenization on downstream applications.Journal of Cheminformatics, 16(1):92, 2024

Reference 58

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T11:59:31.946849Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation 6d8a7157-d077-4c43-abfa-5c7edda3dada · outbound

This paper cites VenusFactory: A Unified Platform for Protein Engineering Data Retrieval and Language Model Fine-Tuning.

PFMBench: Protein Foundation Model Benchmark VenusFactory: A Unified Platform for Protein Engineering Data Retrieval and Language Model Fine-Tuning

Reference 59

Resolution: unresolved; no resolver link, observed 2026-08-07T11:59:25.798400Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 27108c1b-c32a-4697-8a56-3d779d5d59b3 · outbound

This paper cites Protsolm: Protein solubility prediction with multi-modal features.

PFMBench: Protein Foundation Model Benchmark Protsolm: Protein solubility prediction with multi-modal features

Reference 60

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T11:59:31.713076Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation 8a85558c-51a5-4b94-91d5-d08ab7d0299e · outbound

This paper cites Deeploc 2.0: multi-label subcellular localization prediction using protein language models.Nucleic acids research, 50(W1):W228–W234, 2022.

PFMBench: Protein Foundation Model Benchmark Deeploc 2.0: multi-label subcellular localization prediction using protein language models.Nucleic acids research, 50(W1):W228–W234, 2022

Reference 61

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T11:59:31.459443Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation 779b57a8-1f36-48b5-940f-5d81bf4fb171 · outbound

This paper cites On mutual information maximization for representation learning.

PFMBench: Protein Foundation Model Benchmark On mutual information maximization for representation learning

Reference 62

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T11:59:31.241829Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation 42acec6c-9a76-4df5-be1f-7a9b84121d95 · outbound

This paper cites Alphafold protein structure database: massively expanding the structural coverage of protein-sequence space with high-accuracy models.Nucleic acids research, 50(D1):D439–D444, 2022.

PFMBench: Protein Foundation Model Benchmark Alphafold protein structure database: massively expanding the structural coverage of protein-sequence space with high-accuracy models.Nucleic acids research, 50(D1):D439–D444, 2022

Reference 63

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T11:59:31.082567Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation abe6f152-0855-45de-b22b-9037ba76c13a · outbound

This paper cites Attention is all you need.NeurIPS, 30, 2017.

PFMBench: Protein Foundation Model Benchmark Attention is all you need.NeurIPS, 30, 2017

Reference 64

Resolution: unresolved; no resolver link, observed 2026-08-07T11:59:26.414774Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation df7518e0-5c38-4f3f-9983-1010b658c6ff · outbound

This paper cites Prediction of protein solubility based on sequence physicochemical patterns and distributed representation information with deepsolue.BMC biology, 21(1):12, 2023.

PFMBench: Protein Foundation Model Benchmark Prediction of protein solubility based on sequence physicochemical patterns and distributed representation information with deepsolue.BMC biology, 21(1):12, 2023

Reference 65

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T11:59:30.665731Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation af2eb6d6-c086-4190-bedc-8b94b0ee3e54 · outbound

This paper cites an unresolved cited work.

PFMBench: Protein Foundation Model Benchmark Unresolved cited work

Reference 66

Resolution: unresolved; raw_fallback, observed 2026-08-07T11:59:30.437987Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation 0d597568-3b04-4a7d-81a7-69d926c28ff6 · outbound

This paper cites A comprehensive computational benchmark for evaluating deep learning-based protein function prediction approaches.Briefings in Bioinformatics, 25(2):bbae050, 2024.

PFMBench: Protein Foundation Model Benchmark A comprehensive computational benchmark for evaluating deep learning-based protein function prediction approaches.Briefings in Bioinformatics, 25(2):bbae050, 2024

Reference 67

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T11:59:30.193171Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation fd3e0a5e-0e8c-4b9c-9711-854b4ac5bdb5 · outbound

This paper cites Diffusion language models are versatile protein learners.

PFMBench: Protein Foundation Model Benchmark Diffusion language models are versatile protein learners

Reference 68

Resolution: unresolved; no resolver link, observed 2026-08-07T11:59:26.928157Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 8be6d044-787c-447f-b91e-44cc2dd8c9fb · outbound

This paper cites Mixture of LoRA Experts.

PFMBench: Protein Foundation Model Benchmark Mixture of LoRA Experts

Reference 69

Resolution: unresolved; no resolver link, observed 2026-08-07T11:59:27.074703Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation dbbe57b1-2422-40c9-9104-ec8dbeed97f4 · outbound

This paper cites Ccbhla: pan-specific peptide–hla class i binding prediction via convolutional and bilstm features.bioRxiv, pages 2023–04, 2023.

PFMBench: Protein Foundation Model Benchmark Ccbhla: pan-specific peptide–hla class i binding prediction via convolutional and bilstm features.bioRxiv, pages 2023–04, 2023

Reference 70

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T11:59:29.951596Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation fbd1b007-5e2a-42d6-a13c-d3d77fd0afef · outbound

This paper cites Protst: Multi-modality learning of protein sequences and biomedical texts.

PFMBench: Protein Foundation Model Benchmark Protst: Multi-modality learning of protein sequences and biomedical texts

Reference 71

Resolution: unresolved; no resolver link, observed 2026-08-07T11:59:27.350084Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation ff9ec8c0-696c-4f1d-9525-db65817551b6 · outbound

This paper cites Peer: a comprehensive and multi-task benchmark for protein sequence understanding.Advances in Neural Information Processing Systems, 35:35156–35173, 2022.

PFMBench: Protein Foundation Model Benchmark Peer: a comprehensive and multi-task benchmark for protein sequence understanding.Advances in Neural Information Processing Systems, 35:35156–35173, 2022

Reference 72

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T11:59:29.653696Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation 2e601e7d-9371-4cf3-ad93-bc414d310a34 · outbound

This paper cites Care: a benchmark suite for the classification and retrieval of enzymes.

PFMBench: Protein Foundation Model Benchmark Care: a benchmark suite for the classification and retrieval of enzymes

Reference 73

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T11:59:29.397940Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation a1608f23-e444-4b04-bc62-3057bfbb315f · outbound

This paper cites Improved protein structure prediction using predicted interresidue orientations.

PFMBench: Protein Foundation Model Benchmark Improved protein structure prediction using predicted interresidue orientations

Reference 74

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T11:59:29.227855Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation d35937c4-0bad-4095-a247-c503a62b7c3e · outbound

This paper cites ProteinBench: A Holistic Evaluation of Protein Foundation Models.

PFMBench: Protein Foundation Model Benchmark ProteinBench: A Holistic Evaluation of Protein Foundation Models

Reference 75

Resolution: unresolved; no resolver link, observed 2026-08-07T11:59:27.806358Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 18dbd60a-12bb-4104-94cd-ec7758150a1a · outbound

This paper cites Bitfit: Simple parameter-efficient fine-tuning for transformer-based masked language-models.

PFMBench: Protein Foundation Model Benchmark Bitfit: Simple parameter-efficient fine-tuning for transformer-based masked language-models

Reference 76

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T11:59:29.071700Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation f3fef223-3ad6-4edd-a4bb-51283adde86a · outbound

This paper cites Ontoprotein: Protein pretraining with gene ontology embedding.

PFMBench: Protein Foundation Model Benchmark Ontoprotein: Protein pretraining with gene ontology embedding

Reference 77

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T11:59:28.917048Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation 7645acea-2279-45ae-ba0c-b60299d5f33f · outbound

This paper cites AdaLoRA: Adaptive Budget Allocation for Parameter-Efficient Fine-Tuning.

PFMBench: Protein Foundation Model Benchmark AdaLoRA: Adaptive Budget Allocation for Parameter-Efficient Fine-Tuning

Reference 78

Resolution: unresolved; no resolver link, observed 2026-08-07T11:59:28.126728Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation f46eea87-5c6c-41f0-9a62-576a7ec00bf1 · outbound

This paper cites Protein representation learning by geometric structure pretraining.

PFMBench: Protein Foundation Model Benchmark Protein representation learning by geometric structure pretraining

Reference 79

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T11:59:28.815074Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation c66e53bd-254e-44a2-9ae3-6b4a1eb4d8bc · outbound

This paper cites Protclip: Function-informed protein multi-modal learning.

PFMBench: Protein Foundation Model Benchmark Protclip: Function-informed protein multi-modal learning

Reference 80

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T11:59:28.616876Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Coverage vector

Reference resolution

Outbound references

Pith citing papers