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In the rapidly evolving landscape of genomics, deep learning has emerged as a useful tool for tackling complex computational challenges.
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“DanQ: a hybrid convolutional and recurrent deep neural network for quantifying the function of DNA sequences”, 2015, pp. 032821
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“DeeperBind: Enhancing Prediction of Sequence Specificities of DNA Binding Proteins”
Hamid Hassanzadeh and May Wang · 2016
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“A Unified Approach to Interpreting Model Predictions”, 2017
Scott Lundberg and Su-In Lee · 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
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“Deep learning sequence-based ab initio prediction of variant effects on expression and disease risk”
Jian Zhou et al · 2018
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“Hierarchical text-conditional image generation with clip latents”
Aditya Ramesh et al · 2022
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“Application of Deep Learning on Single-cell RNA Sequencing Data Analysis: A Review” AI in Omics
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Xuezhe Ma et al · 2022
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Gherman Novakovsky et al · 2022
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“Genomics enters the deep learning era”
Etienne Routhier and Julien Mozziconacci · 2022
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“Define: Deep convolutional neural networks accurately quantify intensities of transcription factor-DNA binding and facilitate evaluation of functional non-coding variants”
Meng Wang, Cheng Tai, Weinan E and Liping Wei · 2018
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“Sequential regulatory activity prediction across chromosomes with Convolutional Neural Networks”
David. Kelley et al · 2018
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Michael Wainberg, Daniele Merico, Andrew Delong and Brendan Frey · 2018
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“Group normalization”
Yuxin Wu and Kaiming He · 2018
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“Conceptual understanding of convolutional neural network-a deep learning approach”
Sakshi Indolia, Anil Goswami, Surya Mishra and Pooja Asopa · 2018
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“Language models are unsupervised multitask learners”
Alec Radford et al · 2019
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“A survey of transformers”
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“Sequence-based modeling of three-dimensional genome architecture from kilobase to chromosome scale”
Jian Zhou · 2022
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Thomas Wang et al · 2022
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Salman Khan et al · 2022
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“Are Pre-trained Convolutions Better than Pre-trained Transformers?”, 2022
Yi Tay et al · 2022
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“DNA language models are powerful zero-shot predictors of non-coding variant effects”, 2022
Gonzalo Benegas, Sanjit Batra and Yun. Song · 2022
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“FlashAttention: Fast and Memory-Efficient Exact Attention with IO-Awareness”, 2022
Tri Dao et al · 2022
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“Current sequence-based models capture gene expression determinants in promoters but mostly ignore distal enhancers”
Alexander Karollus, Thomas Mauermeier and Julien Gagneur · 2022
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“Train Short, Test Long: Attention with Linear Biases Enables Input Length Extrapolation”, 2022
Ofir Press, Noah. Smith and Mike Lewis · 2022
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“Convolutions are competitive with transformers for protein sequence pretraining”
Kevin Yang, Alex Lu and Nicolo Fusi · 2022
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“Predicting gene expression from histone modifications with self-attention based neural networks and transfer learning”
Yuchi Chen, Minzhu Xie and Jie Wen · 2022
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“WeightedSHAP: analyzing and improving Shapley based feature attributions”, 2022
Yongchan Kwon and James Zou · 2022
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“On the Opportunities and Risks of Foundation Models”, 2022
Rishi Bommasani et al · 2022
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“Dilated Neighborhood Attention Transformer”
Ali Hassani and Humphrey Shi · 2022
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“Token Merging: Your ViT But Faster”
Daniel Bolya et al · 2022
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“GPT-4 Technical Report”, 2023
OpenAI · 2023
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