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Protein representation learning methods have shown great potential to yield useful representation for many downstream tasks, especially on protein classification.
Principles that govern the folding of protein chains
Christian B Anfinsen · 1973
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Four distances between pairs of amino acids provide a precise description of their interaction
Mati Cohen, Vladimir Potapov, and Gideon Schreiber · 2009
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A large-scale evaluation of computational protein function prediction
Predrag Radivojac, Wyatt T Clark, Tal Ronnen Oron, Alexandra M Schnoes, Tobias Wittkop, Artem Sokolov, Kiley Graim, Christopher Funk, Karin Verspoor, Asa Ben-Hur, et al · 2013
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Convolutional lstm networks for subcellular localization of proteins
Søren Kaae Sønderby, Casper Kaae Sønderby, Henrik Nielsen, and Ole Winther · 2015
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f-gan: Training generative neural samplers using variational divergence minimization
Sebastian Nowozin, Botond Cseke, and Ryota Tomioka · 2016
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Deeploc: prediction of protein subcellular localization using deep learning
José Juan Almagro Armenteros, Casper Kaae Sónderby, Sóren Kaae Sónderby, Henrik Nielsen, and Ole Winther · 2017
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Deepsf: deep convolutional neural network for mapping protein sequences to folds
Jie Hou, Badri Adhikari, and Jianlin Cheng · 2018
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Tongzhou Wang, Jun-Yan Zhu, Antonio Torralba, and Alexei A Efros · 2018
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How powerful are graph neural networks?
Keyulu Xu, Weihua Hu, Jure Leskovec, and Stefanie Jegelka · 2018
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Learning protein sequence embeddings using information from structure
Tristan Bepler and Bonnie Berger · 2019
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Evaluating protein transfer learning with tape
Roshan Rao, Nicholas Bhattacharya, Neil Thomas, Yan Duan, Xi Chen, John Canny, Pieter Abbeel, and Yun S Song · 2019
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End-to-end learning on 3d protein structure for interface prediction
Raphael Townshend, Rishi Bedi, Patricia Suriana, and Ron Dror · 2019
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Revolutionary cryo-em is taking over structural biology
Ewen Callaway · 2020
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Fine-tuning pretrained language models: Weight initializations, data orders, and early stopping
Jesse Dodge, Gabriel Ilharco, Roy Schwartz, Ali Farhadi, Hannaneh Hajishirzi, and Noah Smith · 2020
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Meta-learning in neural networks: A survey
Timothy Hospedales, Antreas Antoniou, Paul Micaelli, and Amos Storkey · 2020
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Intrinsic-extrinsic convolution and pooling for learning on 3d protein structures
Pedro Hermosilla, Marco Schäfer, Matej Lang, Gloria Fackelmann, Pere-Pau Vázquez, Barbora Kozlikova, Michael Krone, Tobias Ritschel, and Timo Ropinski · 2020
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Transformer protein language models are unsupervised structure learners
Roshan Rao, Joshua Meier, Tom Sercu, Sergey Ovchinnikov, and Alexander Rives · 2020
Multi-scale representation learning on proteins
Vignesh Ram Somnath, Charlotte Bunne, and Andreas Krause · 2021
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Geometric graph representation learning on protein structure prediction
Tian Xia and Wei-Shinn Ku · 2021
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Gradient-based bi-level optimization for deep learning: A survey
Can Chen, Xi Chen, Chen Ma, Zixuan Liu, and Xue Liu · 2022
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Unbiased implicit feedback via bi-level optimization
Can Chen, Chen Ma, Xi Chen, Sirui Song, Hao Liu, and Xue Liu · 2022
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Bidirectional learning for offline infinite-width model-based optimization
Can Chen, Yingxue Zhang, Jie Fu, Xue Liu, and Mark Coates · 2022
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Bertology meets biology: Interpreting attention in protein language models
Jesse Vig, Ali Madani, Lav R Varshney, Caiming Xiong, Richard Socher, and Nazneen Fatema Rajani · 2020
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Learning the protein language: Evolution, structure, and function
Tristan Bepler and Bonnie Berger · 2021
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Generalized dataweighting via class-level gradient manipulation
Can Chen, Shuhao Zheng, Xi Chen, Erqun Dong, Xue Steve Liu, Hao Liu, and Dejing Dou · 2021
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Prottrans: towards cracking the language of life’s code through self-supervised deep learning and high performance computing
Ahmed Elnaggar, Michael Heinzinger, Christian Dallago, Ghalia Rihawi, Yu Wang, Llion Jones, Tom Gibbs, Tamas Feher, Christoph Angerer, Martin Steinegger, et al · 2021
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Structure-based protein function prediction using graph convolutional networks
Vladimir Gligorijević, P Douglas Renfrew, Tomasz Kosciolek, Julia Koehler Leman, Daniel Berenberg, Tommi Vatanen, Chris Chandler, Bryn C Taylor, Ian M Fisk, Hera Vlamakis, et al · 2021
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Highly accurate protein structure prediction with alphafold
John Jumper, Richard Evans, Alexander Pritzel, Tim Green, Michael Figurnov, Olaf Ronneberger, Kathryn Tunyasuvunakool, Russ Bates, Augustin Žídek, Anna Potapenko, et al · 2021
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Biological structure and function emerge from scaling unsupervised learning to 250 million protein sequences
Alexander Rives, Joshua Meier, Tom Sercu, Siddharth Goyal, Zeming Lin, Jason Liu, Demi Guo, Myle Ott, C Lawrence Zitnick, Jerry Ma, et al · 2021
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Geometry-enhanced molecular representation learning for property prediction
Xiaomin Fang, Lihang Liu, Jieqiong Lei, Donglong He, Shanzhuo Zhang, Jingbo Zhou, Fan Wang, Hua Wu, and Haifeng Wang · 2022
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Protein representation learning by geometric structure pretraining
Zuobai Zhang, Minghao Xu, Arian Jamasb, Vijil Chenthamarakshan, Aurelie Lozano, Payel Das, and Jian Tang · 2022
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Bidirectional learning for offline model-based biological sequence design
Can Chen, Yingxue Zhang, Xue Liu, and Mark Coates · 2023
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On pre-training language model for antibody
Danqing Wang, Fei YE, and Hao Zhou · 2023
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Protein representation learning via knowledge enhanced primary structure reasoning
Hong-Yu Zhou, Yunxiang Fu, Zhicheng Zhang, Bian Cheng, and Yizhou Yu · 2023
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Enhancing protein language models with structure-based encoder and pre-training
Zuobai Zhang, Minghao Xu, Vijil Chenthamarakshan, Aurélie Lozano, Payel Das, and Jian Tang · 2023
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