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Given the increasing volume and quality of genomics data, extracting new insights requires interpretable machine-learning models.
Transcriptional regulatory elements in the human genome
Maston, G. A., Evans, S. K., and Green, M. R · 2006
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Deep cap analysis gene expression (cage): genome-wide identification of promoters, quantification of their expression, and network inference
de Hoon, M. and Hayashizaki, Y · 2008
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Cage (cap analysis of gene expression): a protocol for the detection of promoter and transcriptional networks
Takahashi, H., Kato, S., Murata, M., and Carninci, P · 2012
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Refined dnase-seq protocol and data analysis reveals intrinsic bias in transcription factor footprint identification
He, H. H., Meyer, C. A., Hu, S. S., Chen, M.-W., Zang, C., Liu, Y., Rao, P. K., Fei, T., Xu, H., Long, H., et al · 2014
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Adam: A method for stochastic optimization
Kingma, D. P. and Ba, J · 2014
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Predicting the sequence specificities of dna-and rna-binding proteins by deep learning
Alipanahi, B., Delong, A., Weirauch, M. T., and Frey, B. J · 2015
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Fast r-cnn
Girshick, R · 2015
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Mask r-cnn
He, K., Gkioxari, G., Dollár, P., and Girshick, R · 2017
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Attention is all you need
Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A. N., Kaiser, Ł., and Polosukhin, I · 2017
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Genome-wide prediction of dnase i hypersensitivity using gene expression
Zhou, W., Sherwood, B., Ji, Z., Xue, Y., Du, F., Bai, J., Ying, M., and Ji, H · 2017
Cited alongside, same era.
A closer look at deep learning heuristics: Learning rate restarts, warmup and distillation
Gotmare, A., Keskar, N. S., Xiong, C., and Socher, R · 2018
Cited alongside, same era.
Sequential regulatory activity prediction across chromosomes with convolutional neural networks
Kelley, D. R., Reshef, Y. A., Bileschi, M., Belanger, D., McLean, C. Y., and Snoek, J · 2018
Cited alongside, same era.
Generating long sequences with sparse transformers
Child, R., Gray, S., Radford, A., and Sutskever, I · 2019
Cited alongside, same era.
Deep learning: new computational modelling techniques for genomics
Eraslan, G., Avsec, Ž., Gagneur, J., and Theis, F. J · 2019
Cited alongside, same era.
Pretrained transformers improve out-of-distribution robustness
Hendrycks, D., Liu, X., Wallace, E., Dziedzic, A., Krishnan, R., and Song, D · 2020
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Cross-species regulatory sequence activity prediction
Kelley, D. R · 2020
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Effective gene expression prediction from sequence by integrating long-range interactions
Avsec, Ž., Agarwal, V., Visentin, D., Ledsam, J. R., Grabska-Barwinska, A., Taylor, K. R., Assael, Y., Jumper, J., Kohli, P., and Kelley, D. R · 2021
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Uncovering motif interactions from convolutional-attention networks for genomics
Ghotra, R. S., Lee, N. K., and Koo, P. K · 2021
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Swin transformer: Hierarchical vision transformer using shifted windows
Liu, Z., Lin, Y., Cao, Y., Hu, H., Wei, Y., Zhang, Z., Lin, S., and Guo, B · 2021
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Learning to deceive with attention-based explanations
Pruthi, D., Gupta, M., Dhingra, B., Neubig, G., and Lipton, Z. C · 2019
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Quantifying attention flow in transformers
Abnar, S. and Zuidema, W · 2020
Cited alongside, same era.
Longformer: The long-document transformer
Beltagy, I., Peters, M. E., and Cohan, A · 2020
Cited alongside, same era.
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., et al · 2020
Cited alongside, same era.
Yuan, L., Chen, D., Chen, Y.-L., Codella, N., Dai, X., Gao, J., Hu, H., Huang, X., Li, B., Li, C., et al · 2021
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Swin transformer v2: Scaling up capacity and resolution
Liu, Z., Hu, H., Lin, Y., Yao, Z., Xie, Z., Wei, Y., Ning, J., Cao, Y., Zhang, Z., Dong, L., et al · 2022
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The evolution, evolvability and engineering of gene regulatory dna
Vaishnav, E. D., de Boer, C. G., Molinet, J., Yassour, M., Fan, L., Adiconis, X., Thompson, D. A., Levin, J. Z., Cubillos, F. A., and Regev, A · 2022
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