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In the genome biology research, regulatory genome modeling is an important topic for many regulatory downstream tasks, such as promoter classification, transaction factor binding sites prediction.
Why Transcription Factor Binding Sites Are Ten Nucleotides Long
Alexander J Stewart, Sridhar Hannenhalli, and Joshua B Plotkin · 1943
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Quantitative monitoring of gene expression patterns with a complementary dna microarray
Mark Schena, Dari Shalon, Ronald W. Davis, and Patrick O. Brown · 1995
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Systematic variation in gene expression patterns in human cancer cell lines
Douglas T Ross, Uwe Scherf, Michael B Eisen, Charles M Perou, Christian Rees, Paul Spellman, Vishwanath Iyer, Stefanie S Jeffrey, Matt Van de Rijn, Mark Waltham, Alexander Pergamenschikov, Jeffrey C.F. Lee, Deval Lashkari, Dari Shalon, Timothy G Myers, John N Weinstein, David Botstein, and Patrick O Brown · 2000
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Improved baselines with momentum contrastive learning
Xinlei Chen, Haoqi Fan, Ross Girshick, and Kaiming He · 2003
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Establishing glucose- and aba-regulated transcription networks in arabidopsis by microarray analysis and promoter classification using a relevance vector machine
Yunhai Li, Kee Khoon Lee, Sean Walsh, Caroline Smith, Sophie Hadingham, Karim Sorefan, Gavin C. Cawley, and Michael W. Bevan · 2006
Earlier work this paper cites.
An integrated encyclopedia of dna elements in the human genome
ENCODE Project Consortium et al · 2012
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Predicting the sequence specificities of dna-and rna-binding proteins by deep learning
Babak Alipanahi, Andrew Delong, Matthew T Weirauch, and Brendan J Frey · 2015
Earlier work this paper cites.
Gene expression inference with deep learning
Yifei Chen, Yi Li, Rajiv Narayan, Aravind Subramanian, and Xiaohui Xie · 2016
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Basset: Learning the regulatory code of the accessible genome with deep convolutional neural networks
David R Kelley, Jasper Snoek, and John Rinn · 2016
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Diet networks: Thin parameters for fat genomic
Adriana Romero, Pierre Luc Carrier, Akram Erraqabi, Tristan Sylvain, Alex Auvolat, Etienne Dejoie, Marc-André Legault, Marie-Pierre Dubé, Julie G. Hussin, and Yoshua Bengio · 2017
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BERT: Pre-training of deep bidirectional transformers for language understanding
Jacob Devlin, Ming-Wei Chang, Kenton Lee, and Kristina Toutanova · 2018
Cited alongside, same era.
Predicting Splicing from Primary Sequence with Deep Learning
Kishore Jaganathan, Sofia Kyriazopoulou Panagiotopoulou, Jeremy F. McRae, Siavash Fazel Darbandi, David Knowles, Yang I. Li, Jack A. Kosmicki, Juan Arbelaez, Wenwu Cui, Grace B. Schwartz, Eric D. Chow, Efstathios Kanterakis, Hong Gao, Amirali Kia, Serafim Batzoglou, Stephan J. Sanders, and Kyle Kai-How Farh · 2018
Cited alongside, same era.
JASPAR 2018: update of the open-access database of transcription factor binding profiles and its web framework
Aziz Khan, Oriol Fornes, Arnaud Stigliani, Marius Gheorghe, Jaime A Castro-Mondragon, Robin van der Lee, Adrien Bessy, Jeanne Chèneby, Shubhada R Kulkarni, Ge Tan, Damir Baranasic, David J Arenillas, Albin Sandelin, Klaas Vandepoele, Boris Lenhard, Benoît Ballester, Wyeth W Wasserman, François Parcy, and Anthony Mathelier · 2018
Cited alongside, same era.
A deep learning framework for imputing missing values in genomic data
Yeping Lina Qiu, Hong Zheng, and Olivier Gevaert · 2018
Cited alongside, same era.
Momentum contrast for unsupervised visual representation learning
Kaiming He, Haoqi Fan, Yuxin Wu, Saining Xie, and Ross Girshick · 2020
Later among the works it cites.
Flexible k-mers with variable-length indels for identifying binding sequences of protein dimers
Chenyang Hong and Kevin Y Yip · 2020
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Training data-efficient image transformers & distillation through attention
Hugo Touvron, Matthieu Cord, Matthijs Douze, Francisco Massa, Alexandre Sablayrolles, and Hervé Jégou · 2020
Later among the works it cites.
Effective gene expression prediction from sequence by integrating long-range interactions
Žiga Avsec, Vikram Agarwal, Daniel Visentin, Joseph R. Ledsam, Agnieszka Grabska-Barwinska, Kyle R. Taylor, Yannis Assael, John Jumper, Pushmeet Kohli, and David R. Kelley · 2021
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An image is worth 16x16 words: Transformers for image recognition at scale
Alexey Dosovitskiy, Lucas Beyer, Alexander Kolesnikov, Dirk Weissenborn, Xiaohua Zhai, Thomas Unterthiner, Mostafa Dehghani, Matthias Minderer, Georg Heigold, Sylvain Gelly, Jakob Uszkoreit, and Neil Houlsby · 2021
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Improving language under-standing by generative pre-training
Alec Radford, Karthik Narasimhan, Tim Salimans, and Ilya Sutskever · 2018
Cited alongside, same era.
Roberta: A robustly optimized bert pretraining approach
Yinhan Liu, Myle Ott, Naman Goyal, Jingfei Du, Mandar Joshi, Danqi Chen, Omer Levy, Mike Lewis, Luke Zettlemoyer, and Veselin Stoyanov · 2019
Cited alongside, same era.
Deepromoter: robust promoter predictor using deep learning
Mhaned Oubounyt, Zakaria Louadi, Hilal Tayara, and Kil To Chong · 2019
Cited alongside, same era.
Ernie: Enhanced representation through knowledge integration
Yu Sun, Shuohuan Wang, Yukun Li, Shikun Feng, Xuyi Chen, Han Zhang, Xin Tian, Danxiang Zhu, Hao Tian, and Hua Wu · 2019
Cited alongside, same era.
Imagene: a convolutional neural network to quantify natural selection from genomic data
Luis Torada, Lucrezia Lorenzon, Alice Beddis, Ulas Isildak, Linda Pattini, Sara Mathieson, and Matteo Fumagalli · 2019
Cited alongside, same era.
A human cell atlas of fetal chromatin accessibility
Silvia Domcke, Andrew J. Hill, Riza M. Daza, Junyue Cao, Diana R. O’Day, Hannah A. Pliner, Kimberly A. Aldinger, Dmitry Pokholok, Fan Zhang, Jennifer H. Milbank, Michael A. Zager, Ian A. Glass, Frank J. Steemers, Dan Doherty, Cole Trapnell, Darren A. Cusanovich, and Jay Shendure · 2020
Cited alongside, same era.
Deep-learning approach to identifying cancer subtypes using high-dimensional genomic data
Runpu Chen, Le Yang, Steve Goodison, and Yijun Sun
Cited in the paper.
A simple framework for contrastive learning of visual representations
Ting Chen, Simon Kornblith, Mohammad Norouzi, and Geoffrey Hinton
Cited in the paper.
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WenLan: Bridging vision and language by large-scale multi-modal pre-training
Yuqi Huo, Manli Zhang, Guangzhen Liu, Haoyu Lu, Yizhao Gao, Guoxing Yang, Jingyuan Wen, Heng Zhang, Baogui Xu, Weihao Zheng, Zongzheng Xi, Yueqian Yang, Anwen Hu, Jinming Zhao, Ruichen Li, Yida Zhao, Liang Zhang, Yuqing Song, Xin Hong, Wanqing Cui, Danyang Hou, Yingyan Li, Junyi Li, Peiyu Liu, Zheng Gong, Chuhao Jin, Yuchong Sun, Shizhe Chen, Zhiwu Lu, Zhicheng Dou, Qin Jin, Yanyan Lan, Wayne Xin Zhao, Ruihua Song, and Ji-Rong Wen · 2021
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Dnabert: pre-trained bidirectional encoder representations from transformers model for dna-language in genome
Yanrong Ji, Zhihan Zhou, Han Liu, and Ramana V Davuluri · 2021
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Swin transformer: Hierarchical vision transformer using shifted windows
Ze Liu, Yutong Lin, Yue Cao, Han Hu, Yixuan Wei, Zheng Zhang, Stephen Lin, and Baining Guo · 2021
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Learning transferable visual models from natural language supervision
Alec Radford, Jong Wook Kim, Chris Hallacy, Aditya Ramesh, Gabriel Goh, Sandhini Agarwal, Girish Sastry, Amanda Askell, Pamela Mishkin, Jack Clark, Gretchen Krueger, and Ilya Sutskever · 2021
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Tokens-to-token vit: Training vision transformers from scratch on imagenet
Li Yuan, Yunpeng Chen, Tao Wang, Weihao Yu, Yujun Shi, Francis EH Tay, Jiashi Feng, and Shuicheng Yan · 2021
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