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Multi-modality pre-training paradigm that aligns protein sequences and biological descriptions has learned general protein representations and achieved promising performance in various downstream applications.
The SWISS-PROT protein sequence database and its supplement TrEMBL in 2000
Bairoch, A.; and Apweiler, R. 2000 · 2000
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DivideMix: Learning with noisy labels as semi-supervised learning
Li, J.; Socher, R.; and Hoi, S. C. 2020 · 2002
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Is Transfer Learning Necessary for Protein Landscape Prediction?
Shanehsazzadeh, A.; Belanger, D.; and Dohan, D. 2020 · 2011
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Embedding entities and relations for learning and inference in knowledge bases
Yang, B.; tau Yih, S. W.; He, X.; Gao, J.; and Deng, L. 2015 · 2015
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Ba, J. L.; Kiros, J. R.; and Hinton, G. E. 2016 · 2016
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Gaussian error linear units (gelus)
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Complex embeddings for simple link prediction
Trouillon, T.; Welbl, J.; Riedel, S.; Gaussier, E.; and Bouchard, G. 2016 · 2016
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Deeploc: prediction of protein subcellular localization using deep learning
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MixMatch: A holistic approach to semi-supervised learning
Berthelot, D.; Carlini, N.; Goodfellow, I.; Papernot, N.; Oliver, A.; and Raffel, C. A. 2019 · 2019
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Multifaceted protein–protein interaction prediction based on siamese residual rcnn
Chen, M.; Ju, C. J.-T.; Zhou, G.; Chen, X.; Zhang, T.; Chang, K.-W.; Zaniolo, C.; and Wang, W. 2019 · 2019
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Uniprot: a worldwide hub of protein knowledge
Consortium, U. 2019 · 2019
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BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding
Devlin, J.; Chang, M.-W.; Lee, K.; and Toutanova, K. 2019 · 2019
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Evaluating protein transfer learning with tape
Rao, R.; Bhattacharya, N.; Thomas, N.; Duan, Y.; Chen, P.; Canny, J.; Abbeel, P.; and Song, Y. 2019 · 2019
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RotatE: Knowledge Graph Embedding by Relational Rotation in Complex Space
Sun, Z.; Deng, Z.-H.; Nie, J.-Y.; and Tang, J. 2019 · 2019
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FLIP: Benchmark tasks in fitness landscape inference for proteins
Dallago, C.; Mou, J.; Johnston, K. E.; Wittmann, B. J.; Bhattacharya, N.; Goldman, S.; Madani, A.; and Yang, K. K. 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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Domain-specific language model pretraining for biomedical natural language processing
Gu, Y.; Tinn, R.; Cheng, H.; Lucas, M.; Usuyama, N.; Liu, X.; Naumann, T.; Gao, J.; and Poon, H. 2021 · 2021
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Lv, G.; Hu, Z.; Bi, Y.; and Zhang, S. 2021 · 2021
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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.; et al. 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.; and Fergus, R. 2021 · 2021
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FILIP: Fine-grained Interactive Language-Image Pre-Training
Yao, L.; Huang, R.; Hou, L.; Lu, G.; Niu, M.; Xu, H.; Liang, X.; Li, Z.; Jiang, X.; and Xu, C. 2021 · 2021
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ProtTrans: Toward Understanding the Language of Life Through Self-Supervised Learning
BLIP-2: Bootstrapping Language-Image Pre-training with Frozen Image Encoders and Large Language Models
Junnan, L.; Dongxu, L.; Silvio, S.; and Steven, H. 2023 · 2023
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LLaVA-Med: Training a Large Language-and-Vision Assistant for Biomedicine in One Day
Li, C.; Wong, C.; Zhang, S.; Usuyama, N.; Liu, H.; Yang, J.; Naumann, T.; Poon, H.; and Gao, J. 2023 · 2023
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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.; et al. 2023 · 2023
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PRIOR: Prototype Representation Joint Learning from Medical Images and Reports
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