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In recent years, significant progress has been made in the field of protein function prediction with the development of various machine-learning approaches.
Language models are few-shot learners
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Discriminative structural graph classification
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Quantifying the carbon emissions of machine learning
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Exploring the limits of transfer learning with a unified text-to-text transformer
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The SWISS-PROT Protein Sequence Data Bank and Its New Supplement TREMBL
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Bleu: a method for automatic evaluation of machine translation
Papineni, K., Roukos, S., Ward, T., and Zhu, W.-J. (2002) · 2002
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Rouge: A package for automatic evaluation of summaries
Lin, C.-Y. (2004) · 2004
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Cd-hit: a fast program for clustering and comparing large sets of protein or nucleotide sequences
Li, W. and Godzik, A. (2006) · 2006
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The Graph Neural Network Model
Scarselli, F., Gori, M., Tsoi, A. C., Hagenbuchner, M., and Monfardini, G. (2009) · 2009
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Chemberta: large-scale self-supervised pretraining for molecular property prediction
Chithrananda, S., Grand, G., and Ramsundar, B. (2020) · 2010
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Uniprot: a hub for protein information
Consortium, U. (2015) · 2015
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UniProt: the universal protein knowledgebase
Consortium, T. U. (2016) · 2016
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Semi-Supervised Classification with Graph Convolutional Networks
Kipf, T. N. and Welling, M. (2017) · 2017
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Deep recurrent neural network for protein function prediction from sequence
Liu, X. (2017) · 2017
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Attention is all you need
Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A. N., Kaiser, L. u., and Polosukhin, I. (2017) · 2017
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Improving language understanding by generative pre-training
Radford, A., Narasimhan, K., Salimans, T., and Sutskever, I. (2018) · 2018
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Modeling relational data with graph convolutional networks
Schlichtkrull, M., Kipf, T. N., Bloem, P., Van Den Berg, R., Titov, I., and Welling, M. (2018) · 2018
Cited alongside, same era.
Modeling polypharmacy side effects with graph convolutional networks
Zitnik, M., Agrawal, M., and Leskovec, J. (2018) · 2018
Cited alongside, same era.
Using deep learning to annotate the protein universe
Bileschi, M. L., Belanger, D., Bryant, D., Sanderson, T., Carter, B., Sculley, D., DePristo, M. A., and Colwell, L. J. (2019) · 2019
Cited alongside, same era.
BERT: Pre-training of deep bidirectional transformers for language understanding
Devlin, J., Chang, M.-W., Lee, K., and Toutanova, K. (2019) · 2019
Cited alongside, same era.
DeepGOPlus: improved protein function prediction from sequence
Kulmanov, M. and Hoehndorf, R. (2019) · 2019
Cited alongside, same era.
Decoupled weight decay regularization
Loshchilov, I. and Hutter, F. (2019) · 2019
An image is worth 16x16 words: Transformers for image recognition at scale
Dosovitskiy, A., Beyer, L., Kolesnikov, A., Weissenborn, D., Zhai, X., Unterthiner, T., Dehghani, M., Minderer, M., Heigold, G., Gelly, S., Uszkoreit, J., and Houlsby, N. (2021) · 2021
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Structure-based protein function prediction using graph convolutional networks
Gligorijević, V., Renfrew, P. D., Kosciolek, T., Leman, J. K., Berenberg, D., Vatanen, T., Chandler, C., Taylor, B. C., Fisk, I. M., Vlamakis, H., et al. (2021) · 2021
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Recent advances in identifying protein targets in drug discovery
Ha, J., Park, H., Park, J., and Park, S. B. (2021) · 2021
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Clip: Learning transferable visual models from natural language supervision
Radford, A., Kim, J. W., Hallacy, C., Ramesh, A., Goh, G., Agarwal, S., Sastry, G., Askell, A., Mishkin, P., Clark, J., and Krueger, G. (2021) · 2021
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Biological structure and function emerge from scaling unsupervised learning to 250 million protein sequences
Rives, A., Meier, J., Sercu, T., Goyal, S., Lin, Z., Liu, J., Guo, D., Ott, M., Zitnick, C. L., Ma, J., et al. (2021) · 2021
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Relational Pooling for Graph Representations
Murphy, R., Srinivasan, B., Rao, V., and Ribeiro, B. (2019) · 2019
Cited alongside, same era.
Language models are unsupervised multitask learners
Radford, A., Wu, J., Child, R., Luan, D., Amodei, D., and Sutskever, I. (2019) · 2019
Cited alongside, same era.
Heterogeneous graph neural network
Zhang, C., Song, D., Huang, C., Swami, A., and Chawla, N. V. (2019) · 2019
Cited alongside, same era.
Molecular representation learning with language models and domain-relevant auxiliary tasks
Fabian, B., Edlich, T., Gaspar, H., Segler, M., Meyers, J., Fiscato, M., and Ahmed, M. (2020) · 2020
Cited alongside, same era.
Biobert: a pre-trained biomedical language representation model for biomedical text mining
Lee, J., Yoon, W., Kim, S., Kim, D., Kim, S., So, C. H., and Kang, J. (2020) · 2020
Cited alongside, same era.
BART: Denoising sequence-to-sequence pre-training for natural language generation, translation, and comprehension
Lewis, M., Liu, Y., Goyal, N., Ghazvininejad, M., Mohamed, A., Levy, O., Stoyanov, V., and Zettlemoyer, L. (2020) · 2020
Cited alongside, same era.
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Graph neural networks and their current applications in bioinformatics
Zhang, X.-M., Liang, L., Liu, L., and Tang, M.-J. (2021) · 2021
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Proteinbert: a universal deep-learning model of protein sequence and function
Brandes, N., Ofer, D., Peleg, Y., Rappoport, N., and Linial, M. (2022) · 2022
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Translation between molecules and natural language
Edwards, C., Lai, T., Ros, K., Honke, G., Cho, K., and Ji, H. (2022) · 2022
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Evolutionary-scale prediction of atomic-level protein structure with a language model
Lin, Z., Akin, H., Rao, R., Hie, B., Zhu, Z., Lu, W., Smetanin, N., Verkuil, R., Kabeli, O., Shmueli, Y., dos Santos Costa, A., Fazel-Zarandi, M., Sercu, T., Candido, S., and Rives, A. (2023a) · 2022
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Graph neural networks for materials science and chemistry
Reiser, P., Neubert, M., Eberhard, A., Torresi, L., Zhou, C., Shao, C., Metni, H., van Hoesel, C., Schopmans, H., Sommer, T., et al. (2022) · 2022
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Alphafold protein structure database: massively expanding the structural coverage of protein-sequence space with high-accuracy models
Varadi, M., Anyango, S., Deshpande, M., Nair, S., Natassia, C., Yordanova, G., Yuan, D., Stroe, O., Wood, G., Laydon, A., et al. (2022) · 2022
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Learning protein representations via complete 3d graph networks
Wang, L., Liu, H., Liu, Y., Kurtin, J., and Ji, S. (2022) · 2022
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Protein representation learning by geometric structure pretraining
Zhang, Z., Xu, M., Jamasb, A., Chenthamarakshan, V., Lozano, A., Das, P., and Tang, J. (2022) · 2022
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3d-equivariant graph neural networks for protein model quality assessment
Chen, C., Chen, X., Morehead, A., Wu, T., and Cheng, J. (2023) · 2023
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A text-guided protein design framework
Liu, S., Li, Y., Li, Z., Gitter, A., Zhu, Y., Lu, J., Xu, Z., Nie, W., Ramanathan, A., Xiao, C., Tang, J., Guo, H., and Anandkumar, A. (2023) · 2023
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Protst: Multi-modality learning of protein sequences and biomedical texts
Xu, M., Yuan, X., Miret, S., and Tang, J. (2023) · 2023
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