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Effective protein representation learning is crucial for predicting protein functions.
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Hermosilla, P., Schäfer, M., Lang, M., Fackelmann, G., Vázquez, P.P., Kozlíková, B., Krone, M., Ritschel, T., Ropinski, T.: Intrinsic-extrinsic convolution and pooling for learning on 3d protein structures. Learning (2020)
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Rao, R., Meier, J., Sercu, T., Ovchinnikov, S., Rives, A.: Transformer protein language models are unsupervised structure learners. Biorxiv (2020)
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Zheng, J., Zhao, Z., Chen, M., Chen, J., Wu, C., Chen, Y., Shi, X., Tong, Y.: An improved sign language translation model with explainable adaptations for processing long sign sentences. Computational Intelligence and Neuroscience 2020
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Hermosilla, P., Schfer, M., Lang, M., Fackelmann, G., Vázquez, P.P., Kozlikova, B., Krone, M., Ritschel, T., Ropinski, T.: Intrinsic-extrinsic convolution and pooling for learning on 3d protein structures (2021)
2021
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Jumper, J., Evans, R., Pritzel, A., Green, T., Figurnov, M., Ronneberger, O., Tunyasuvunakool, K., Bates, R., Žídek, A., Potapenko, A., et al.: Highly accurate protein structure prediction with alphafold. Nature 596
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Mistry, J., Chuguransky, S., Williams, L., Qureshi, M., Salazar, G.A., Sonnhammer, E.L., Tosatto, S.C., Paladin, L., Raj, S., Richardson, L.J., et al.: Pfam: The protein families database in 2021. Nucleic acids research 49
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Zheng, J., Chen, Y., Wu, C., Shi, X., Kamal, S.M.: Enhancing neural sign language translation by highlighting the facial expression information. Neurocomputing 464
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Lin, Z., Akin, H., Rao, R., Hie, B., Zhu, Z., Lu, W., dos Santos Costa, A., Fazel-Zarandi, M., Sercu, T., Candido, S., et al.: Language models of protein sequences at the scale of evolution enable accurate structure prediction. bioRxiv (2022)
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2022
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Zhang, Z., Xu, M., Jamasb, A., Chenthamarakshan, V., Lozano, A., Das, P., Tang, J.: Protein representation learning by geometric structure pretraining (2022)
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Dauparas, J., Anishchenko, I., Bennett, N., Bai, H., Ragotte, R.J., Milles, L.F., Wicky, B.I., Courbet, A., de Haas, R.J., Bethel, N., et al.: Robust deep learning–based protein sequence design using proteinmpnn. Science 378
2022
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Fan, H., Wang, Z., Yang, Y., Kankanhalli, M.: Continuous-discrete convolution for geometry-sequence modeling in proteins. In: The Eleventh International Conference on Learning Representations (2022)
2022
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2022
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Guo, Y., Wu, J., Ma, H., Huang, J.: Self-supervised pre-training for protein embeddings using tertiary structures (2022)
2022
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2022
Cited alongside, same era.
Hsu, C., Verkuil, R., Liu, J., Lin, Z., Hie, B., Sercu, T., Lerer, A., Rives, A.: Learning inverse folding from millions of predicted structures. bioRxiv (2022)
2022
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Lin, Z., Akin, H., Rao, R., Hie, B., Zhu, Z., Lu, W., Costa, A.D.S., Fazel-Zarandi, M., Sercu, T., Candido, S., Rives, A.: Language models of protein sequences at the scale of evolution enable accurate structure prediction (2022)
2022
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2022
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Hu, B., Tan, C., Xia, J., Zheng, J., Huang, Y., Wu, L., Liu, Y., Xu, Y., Li, S.Z.: Learning complete protein representation by deep coupling of sequence and structure. bioRxiv pp. 2023–07 (2023)
2023
Closest in time.
Notin, P., Kollasch, A.W., Ritter, D., van Niekerk, L., Paul, S., Spinner, H., Rollins, N., Shaw, A., Weitzman, R., Frazer, J., et al.: Proteingym: Large-scale benchmarks for protein design and fitness prediction. bioRxiv pp. 2023–12 (2023)
2023
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2023
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2023
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