493 lines
27 KiB
Org Mode
493 lines
27 KiB
Org Mode
#+PROPERTY: header-args :exports none :tangle "./bibliography.bib"
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#+LATEX_CLASS_OPTIONS: [12pt]
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#+LATEX_HEADER: \usepackage[natbib=true]{biblatex} \DeclareFieldFormat{apacase}{#1} \addbibresource{./bibliography.bib}
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#+LATEX_HEADER: \usepackage{parskip}
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#+OPTIONS: <:nil c:nil todo:nil H:5
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#+auto_tangle: t
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* Deep Learning
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** Attention is All You Need
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#+begin_src bibtex
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@article{Vaswani2017,
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doi = {10.48550/ARXIV.1706.03762},
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url = {https://arxiv.org/abs/1706.03762},
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author = {Vaswani, Ashish and Shazeer, Noam and Parmar, Niki and
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Uszkoreit, Jakob and Jones, Llion and Gomez, Aidan N. and
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Kaiser, Lukasz and Polosukhin, Illia},
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keywords = {Computation and Language (cs.CL), Machine Learning (cs.LG),
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FOS: Computer and information sciences, FOS: Computer and
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information sciences},
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title = {Attention Is All You Need},
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publisher = {arXiv},
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year = 2017,
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copyright = {arXiv.org perpetual, non-exclusive license}
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}
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#+end_src
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#+LaTeX: \printbibliography[heading=none]
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** Axial Attention in Multidimensional Transformers
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#+begin_src bibtex
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@article{https://doi.org/10.48550/arxiv.1912.12180,
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doi = {10.48550/ARXIV.1912.12180},
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url = {https://arxiv.org/abs/1912.12180},
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author = {Ho, Jonathan and Kalchbrenner, Nal and Weissenborn, Dirk
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and Salimans, Tim},
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keywords = {Computer Vision and Pattern Recognition (cs.CV), FOS:
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Computer and information sciences, FOS: Computer and
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information sciences},
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title = {Axial Attention in Multidimensional Transformers},
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publisher = {arXiv},
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year = 2019,
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copyright = {arXiv.org perpetual, non-exclusive license}
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}
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#+end_src
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** Longformer: The Long-Document Transformer
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#+begin_src bibtex
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@article{https://doi.org/10.48550/arxiv.2004.05150,
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doi = {10.48550/ARXIV.2004.05150},
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url = {https://arxiv.org/abs/2004.05150},
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author = {Beltagy, Iz and Peters, Matthew E. and Cohan, Arman},
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keywords = {Computation and Language (cs.CL), FOS: Computer and
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information sciences, FOS: Computer and information sciences},
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title = {Longformer: The Long-Document Transformer},
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publisher = {arXiv},
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year = 2020,
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copyright = {arXiv.org perpetual, non-exclusive license}
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}
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#+end_src
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** Transformer-XL: Attentive Language Models Beyond a Fixed-Length Context
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#+begin_src bibtex
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@article{https://doi.org/10.48550/arxiv.1901.02860,
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doi = {10.48550/ARXIV.1901.02860},
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url = {https://arxiv.org/abs/1901.02860},
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author = {Dai, Zihang and Yang, Zhilin and Yang, Yiming and
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Carbonell, Jaime and Le, Quoc V. and Salakhutdinov, Ruslan},
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keywords = {Machine Learning (cs.LG), Computation and Language (cs.CL),
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Machine Learning (stat.ML), FOS: Computer and information
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sciences, FOS: Computer and information sciences},
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title = {Transformer-XL: Attentive Language Models Beyond a
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Fixed-Length Context},
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publisher = {arXiv},
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year = 2019,
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copyright = {Creative Commons Attribution Non Commercial Share Alike 4.0
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International}
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}
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#+end_src
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** BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding
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#+begin_src bibtex
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@inproceedings{devlin-etal-2019-bert,
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title = "{BERT}: Pre-training of Deep Bidirectional Transformers for
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Language Understanding",
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author = "Devlin, Jacob and Chang, Ming-Wei and Lee, Kenton and
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Toutanova, Kristina",
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booktitle = "Proceedings of the 2019 Conference of the North {A}merican
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Chapter of the Association for Computational Linguistics:
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Human Language Technologies, Volume 1 (Long and Short Papers)",
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month = jun,
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year = 2019,
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address = "Minneapolis, Minnesota",
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publisher = "Association for Computational Linguistics",
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url = "https://aclanthology.org/N19-1423",
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doi = "10.18653/v1/N19-1423",
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pages = "4171--4186",
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abstract = "We introduce a new language representation model called
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BERT, which stands for Bidirectional Encoder Representations
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from Transformers. Unlike recent language representation
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models (Peters et al., 2018a; Radford et al., 2018), BERT is
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designed to pre-train deep bidirectional representations from
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unlabeled text by jointly conditioning on both left and right
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context in all layers. As a result, the pre-trained BERT model
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can be fine-tuned with just one additional output layer to
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create state-of-the-art models for a wide range of tasks, such
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as question answering and language inference, without
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substantial task-specific architecture modifications. BERT is
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conceptually simple and empirically powerful. It obtains new
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state-of-the-art results on eleven natural language processing
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tasks, including pushing the GLUE score to 80.5 (7.7 point
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absolute improvement), MultiNLI accuracy to 86.7{\%} (4.6{\%}
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absolute improvement), SQuAD v1.1 question answering Test F1
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to 93.2 (1.5 point absolute improvement) and SQuAD v2.0 Test
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F1 to 83.1 (5.1 point absolute improvement).",
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}
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#+end_src
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A masked language model (MLM) randomly masks some of the tokens from the input, and the objective is to predict the original input based only on its context.
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** Fast Transformers with Clustered Attention
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#+begin_src bibtex
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@article{https://doi.org/10.48550/arxiv.2007.04825,
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doi = {10.48550/ARXIV.2007.04825},
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url = {https://arxiv.org/abs/2007.04825},
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author = {Vyas, Apoorv and Katharopoulos, Angelos and Fleuret,
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François},
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keywords = {Machine Learning (cs.LG), Machine Learning (stat.ML), FOS:
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Computer and information sciences, FOS: Computer and
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information sciences},
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title = {Fast Transformers with Clustered Attention},
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publisher = {arXiv},
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year = 2020,
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copyright = {arXiv.org perpetual, non-exclusive license}
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}
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#+end_src
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** The elephant in the interpretability room: Why use attention as explanation when we have saliency methods?
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#+begin_src bibtex
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@inproceedings{bastings-filippova-2020-elephant,
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title = "The elephant in the interpretability room: Why use
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attention as explanation when we have saliency methods?",
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author = "Bastings, Jasmijn and Filippova, Katja",
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booktitle = "Proceedings of the Third BlackboxNLP Workshop on Analyzing
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and Interpreting Neural Networks for NLP",
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month = nov,
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year = 2020,
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address = "Online",
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publisher = "Association for Computational Linguistics",
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url = "https://aclanthology.org/2020.blackboxnlp-1.14",
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doi = "10.18653/v1/2020.blackboxnlp-1.14",
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pages = "149--155",
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abstract = "There is a recent surge of interest in using attention as
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explanation of model predictions, with mixed evidence on
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whether attention can be used as such. While attention
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conveniently gives us one weight per input token and is easily
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extracted, it is often unclear toward what goal it is used as
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explanation. We find that often that goal, whether explicitly
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stated or not, is to find out what input tokens are the most
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relevant to a prediction, and that the implied user for the
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explanation is a model developer. For this goal and user, we
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argue that input saliency methods are better suited, and that
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there are no compelling reasons to use attention, despite the
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coincidence that it provides a weight for each input. With
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this position paper, we hope to shift some of the recent focus
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on attention to saliency methods, and for authors to clearly
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state the goal and user for their explanations.",
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}
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#+end_src
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** MultiMAE: Multi-modal Multi-task Masked Autoencoders
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#+begin_src bibtex
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@article{https://doi.org/10.48550/arxiv.2204.01678,
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doi = {10.48550/ARXIV.2204.01678},
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url = {https://arxiv.org/abs/2204.01678},
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author = {Bachmann, Roman and Mizrahi, David and Atanov, Andrei and
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Zamir, Amir},
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keywords = {Computer Vision and Pattern Recognition (cs.CV), Machine
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Learning (cs.LG), FOS: Computer and information sciences, FOS:
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Computer and information sciences},
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title = {MultiMAE: Multi-modal Multi-task Masked Autoencoders},
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publisher = {arXiv},
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year = 2022,
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copyright = {arXiv.org perpetual, non-exclusive license}
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}
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#+end_src
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* Deep Learning + Biology
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** CpG Transformer for imputation of single-cell methylomes
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#+begin_src bibtex
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@article{DeWaele2021,
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author = {De Waele, Gaetan and Clauwaert, Jim and Menschaert, Gerben
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and Waegeman, Willem},
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title = "{CpG Transformer for imputation of single-cell methylomes}",
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journal = {Bioinformatics},
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volume = 38,
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number = 3,
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pages = {597-603},
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year = 2021,
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month = 10,
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abstract = "{The adoption of current single-cell DNA methylation
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sequencing protocols is hindered by incomplete coverage,
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outlining the need for effective imputation techniques. The
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task of imputing single-cell (methylation) data requires
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models to build an understanding of underlying biological
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processes.We adapt the transformer neural network architecture
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to operate on methylation matrices through combining axial
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attention with sliding window self-attention. The obtained CpG
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Transformer displays state-of-the-art performances on a wide
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range of scBS-seq and scRRBS-seq datasets. Furthermore, we
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demonstrate the interpretability of CpG Transformer and
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illustrate its rapid transfer learning properties, allowing
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practitioners to train models on new datasets with a limited
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computational and time budget.CpG Transformer is freely
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available at
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https://github.com/gdewael/cpg-transformer.Supplementary data
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are available at Bioinformatics online.}",
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issn = {1367-4803},
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doi = {10.1093/bioinformatics/btab746},
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url = {https://doi.org/10.1093/bioinformatics/btab746},
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eprint =
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{https://academic.oup.com/bioinformatics/article-pdf/38/3/597/42167564/btab746.pdf},
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}
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#+end_src
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** MSA Transformer
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#+begin_src bibtex
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@article {Rao2021,
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author = {Rao, Roshan and Liu, Jason and Verkuil, Robert and Meier,
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Joshua and Canny, John F. and Abbeel, Pieter and Sercu, Tom
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and Rives, Alexander},
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title = {MSA Transformer},
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elocation-id = {2021.02.12.430858},
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year = 2021,
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doi = {10.1101/2021.02.12.430858},
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publisher = {Cold Spring Harbor Laboratory},
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abstract = {Unsupervised protein language models trained across
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millions of diverse sequences learn structure and function of
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proteins. Protein language models studied to date have been
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trained to perform inference from individual sequences. The
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longstanding approach in computational biology has been to
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make inferences from a family of evo lutionarily related
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sequences by fitting a model to each family independently. In
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this work we combine the two paradigms. We introduce a protein
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language model which takes as input a set of sequences in the
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form of a multiple sequence alignment. The model interleaves
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row and column attention across the input sequences and is
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trained with a variant of the masked language modeling
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objective across many protein families. The performance of the
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model surpasses current state-of-the-art unsupervised
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structure learning methods by a wide margin, with far greater
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parameter efficiency than prior state-of-the-art protein
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language models.Competing Interest StatementThe authors have
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declared no competing interest.},
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URL =
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{https://www.biorxiv.org/content/early/2021/08/27/2021.02.12.430858},
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eprint =
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{https://www.biorxiv.org/content/early/2021/08/27/2021.02.12.430858.full.pdf},
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journal = {bioRxiv}
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}
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#+end_src
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** Highly accurate protein structure prediction with AlphaFold
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#+begin_src bibtex
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@article{Jumper2021,
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author = {Jumper, John and Evans, Richard and Pritzel, Alexander and
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Green, Tim and Figurnov, Michael and Ronneberger, Olaf and
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Tunyasuvunakool, Kathryn and Bates, Russ and {\v{Z}}{\'i}dek,
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Augustin and Potapenko, Anna and Bridgland, Alex and Meyer,
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Clemens and Kohl, Simon A. A. and Ballard, Andrew J. and
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Cowie, Andrew and Romera-Paredes, Bernardino and Nikolov,
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Stanislav and Jain, Rishub and Adler, Jonas and Back, Trevor
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and Petersen, Stig and Reiman, David and Clancy, Ellen and
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Zielinski, Michal and Steinegger, Martin and Pacholska,
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Michalina and Berghammer, Tamas and Bodenstein, Sebastian and
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Silver, David and Vinyals, Oriol and Senior, Andrew W. and
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Kavukcuoglu, Koray and Kohli, Pushmeet and Hassabis, Demis},
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title = {Highly accurate protein structure prediction with
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AlphaFold},
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journal = {Nature},
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year = 2021,
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month = {Aug},
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day = 01,
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volume = 596,
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number = 7873,
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pages = {583-589},
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abstract = {Proteins are essential to life, and understanding their
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structure can facilitate a mechanistic understanding of their
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function. Through an enormous experimental effort1--4, the
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structures of around 100,000 unique proteins have been
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determined5, but this represents a small fraction of the
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billions of known protein sequences6,7. Structural coverage is
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bottlenecked by the months to years of painstaking effort
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required to determine a single protein structure. Accurate
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computational approaches are needed to address this gap and to
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enable large-scale structural bioinformatics. Predicting the
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three-dimensional structure that a protein will adopt based
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solely on its amino acid sequence---the structure prediction
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component of the `protein folding problem'8---has been an
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important open research problem for more than 50 years9.
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Despite recent progress10--14, existing methods fall far short
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of atomic accuracy, especially when no homologous structure is
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available. Here we provide the first computational method that
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can regularly predict protein structures with atomic accuracy
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even in cases in which no similar structure is known. We
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validated an entirely redesigned version of our neural
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network-based model, AlphaFold, in the challenging 14th
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Critical Assessment of protein Structure Prediction
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(CASP14)15, demonstrating accuracy competitive with
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experimental structures in a majority of cases and greatly
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outperforming other methods. Underpinning the latest version
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of AlphaFold is a novel machine learning approach that
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incorporates physical and biological knowledge about protein
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structure, leveraging multi-sequence alignments, into the
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design of the deep learning algorithm.},
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issn = {1476-4687},
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doi = {10.1038/s41586-021-03819-2},
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url = {https://doi.org/10.1038/s41586-021-03819-2}
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}
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#+end_src
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** MultiVI: deep generative model for the integration of multi-modal data
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#+begin_src bibtex
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@article {Ashuach2021.08.20.457057,
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author = {Ashuach, Tal and Gabitto, Mariano I. and Jordan, Michael I.
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and Yosef, Nir},
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title = {MultiVI: deep generative model for the integration of
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multi-modal data},
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elocation-id = {2021.08.20.457057},
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year = 2021,
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doi = {10.1101/2021.08.20.457057},
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publisher = {Cold Spring Harbor Laboratory},
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abstract = {Jointly profiling the transcriptional and chromatin
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accessibility landscapes of single-cells is a powerful
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technique to characterize cellular populations. Here we
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present MultiVI, a probabilistic model to analyze such
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multiomic data and integrate it with single modality datasets.
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MultiVI creates a joint representation that accurately
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reflects both chromatin and transcriptional properties of the
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cells even when one modality is missing. It also imputes
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missing data, corrects for batch effects and is available in
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the scvi-tools framework:
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https://docs.scvi-tools.org/.Competing Interest StatementThe
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authors have declared no competing interest.},
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URL =
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{https://www.biorxiv.org/content/early/2021/09/07/2021.08.20.457057},
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eprint =
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{https://www.biorxiv.org/content/early/2021/09/07/2021.08.20.457057.full.pdf},
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journal = {bioRxiv}
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}
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#+end_src
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* Biology
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** Cobolt: integrative analysis of multimodal single-cell sequencing data
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#+begin_src bibtex
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@article{Gong2021,
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author = {Gong, Boying and Zhou, Yun and Purdom, Elizabeth},
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title = {Cobolt: integrative analysis of multimodal single-cell
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sequencing data},
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journal = {Genome Biology},
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year = 2021,
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month = {Dec},
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day = 28,
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volume = 22,
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number = 1,
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pages = 351,
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abstract = {A growing number of single-cell sequencing platforms enable
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joint profiling of multiple omics from the same cells. We
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present Cobolt, a novel method that not only allows for
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analyzing the data from joint-modality platforms, but provides
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a coherent framework for the integration of multiple datasets
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measured on different modalities. We demonstrate its
|
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performance on multi-modality data of gene expression and
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chromatin accessibility and illustrate the integration
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||
abilities of Cobolt by jointly analyzing this multi-modality
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data with single-cell RNA-seq and ATAC-seq datasets.},
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issn = {1474-760X},
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||
doi = {10.1186/s13059-021-02556-z},
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||
url = {https://doi.org/10.1186/s13059-021-02556-z}
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}
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||
#+end_src
|
||
** MUON: multimodal omics analysis framework
|
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#+begin_src bibtex
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||
@article{Bredikhin2022,
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||
author = {Bredikhin, Danila and Kats, Ilia and Stegle, Oliver},
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||
title = {MUON: multimodal omics analysis framework},
|
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journal = {Genome Biology},
|
||
year = 2022,
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||
month = {Feb},
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||
day = 01,
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||
volume = 23,
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||
number = 1,
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||
pages = 42,
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||
abstract = {Advances in multi-omics have led to an explosion of
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||
multimodal datasets to address questions from basic biology to
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translation. While these data provide novel opportunities for
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discovery, they also pose management and analysis challenges,
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thus motivating the development of tailored computational
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solutions. Here, we present a data standard and an analysis
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framework for multi-omics, MUON, designed to organise,
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analyse, visualise, and exchange multimodal data. MUON stores
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multimodal data in an efficient yet flexible and interoperable
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data structure. MUON enables a versatile range of analyses,
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from data preprocessing to flexible multi-omics alignment.},
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issn = {1474-760X},
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||
doi = {10.1186/s13059-021-02577-8},
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url = {https://doi.org/10.1186/s13059-021-02577-8}
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}
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#+end_src
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** Multimodal single cell data integration challenge: Results and lessons learned
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||
#+begin_src bibtex
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||
@inproceedings{pmlr-v176-lance22a,
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||
title = {Multimodal single cell data integration challenge: Results
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||
and lessons learned},
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||
author = {Lance, Christopher and Luecken, Malte D. and Burkhardt,
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||
Daniel B. and Cannoodt, Robrecht and Rautenstrauch, Pia and
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||
Laddach, Anna and Ubingazhibov, Aidyn and Cao, Zhi-Jie and
|
||
Deng, Kaiwen and Khan, Sumeer and Liu, Qiao and Russkikh,
|
||
Nikolay and Ryazantsev, Gleb and Ohler, Uwe and data
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integration competition participants, NeurIPS 2021 Multimodal
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||
and Pisco, Angela Oliveira and Bloom, Jonathan and
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||
Krishnaswamy, Smita and Theis, Fabian J.},
|
||
booktitle = {Proceedings of the NeurIPS 2021 Competitions and
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||
Demonstrations Track},
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||
pages = {162--176},
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||
year = 2022,
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||
editor = {Kiela, Douwe and Ciccone, Marco and Caputo, Barbara},
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||
volume = 176,
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||
series = {Proceedings of Machine Learning Research},
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||
month = {06--14 Dec},
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||
publisher = {PMLR},
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||
pdf = {https://proceedings.mlr.press/v176/lance22a/lance22a.pdf},
|
||
url = {https://proceedings.mlr.press/v176/lance22a.html},
|
||
abstract = {Biology has become a data-intensive science. Recent
|
||
technological advances in single-cell genomics have enabled
|
||
the measurement of multiple facets of cellular state,
|
||
producing datasets with millions of single-cell observations.
|
||
While these data hold great promise for understanding
|
||
molecular mechanisms in health and disease, analysis
|
||
challenges arising from sparsity, technical and biological
|
||
variability, and high dimensionality of the data hinder the
|
||
derivation of such mechanistic insights. To promote the
|
||
innovation of algorithms for analysis of multimodal
|
||
single-cell data, we organized a competition at NeurIPS 2021
|
||
applying the Common Task Framework to multimodal single-cell
|
||
data integration. For this competition we generated the first
|
||
multimodal benchmarking dataset for single-cell biology and
|
||
defined three tasks in this domain: prediction of missing
|
||
modalities, aligning modalities, and learning a joint
|
||
representation across modalities. We further specified
|
||
evaluation metrics and developed a cloud-based algorithm
|
||
evaluation pipeline. Using this setup, 280 competitors
|
||
submitted over 2600 proposed solutions within a 3 month
|
||
period, showcasing substantial innovation especially in the
|
||
modality alignment task. Here, we present the results,
|
||
describe trends of well performing approaches, and discuss
|
||
challenges associated with running the competition.}
|
||
}
|
||
#+end_src
|
||
** Eleven grand challenges in single-cell data science
|
||
#+begin_src bibtex
|
||
@article{Lähnemann2020,
|
||
author = {L{\"a}hnemann, David and K{\"o}ster, Johannes and Szczurek,
|
||
Ewa and McCarthy, Davis J. and Hicks, Stephanie C. and
|
||
Robinson, Mark D. and Vallejos, Catalina A. and Campbell,
|
||
Kieran R. and Beerenwinkel, Niko and Mahfouz, Ahmed and
|
||
Pinello, Luca and Skums, Pavel and Stamatakis, Alexandros and
|
||
Attolini, Camille Stephan-Otto and Aparicio, Samuel and
|
||
Baaijens, Jasmijn and Balvert, Marleen and Barbanson, Buys de
|
||
and Cappuccio, Antonio and Corleone, Giacomo and Dutilh, Bas
|
||
E. and Florescu, Maria and Guryev, Victor and Holmer, Rens and
|
||
Jahn, Katharina and Lobo, Thamar Jessurun and Keizer, Emma M.
|
||
and Khatri, Indu and Kielbasa, Szymon M. and Korbel, Jan O.
|
||
and Kozlov, Alexey M. and Kuo, Tzu-Hao and Lelieveldt,
|
||
Boudewijn P.F. and Mandoiu, Ion I. and Marioni, John C. and
|
||
Marschall, Tobias and M{\"o}lder, Felix and Niknejad, Amir and
|
||
Raczkowski, Lukasz and Reinders, Marcel and Ridder, Jeroen de
|
||
and Saliba, Antoine-Emmanuel and Somarakis, Antonios and
|
||
Stegle, Oliver and Theis, Fabian J. and Yang, Huan and
|
||
Zelikovsky, Alex and McHardy, Alice C. and Raphael, Benjamin
|
||
J. and Shah, Sohrab P. and Sch{\"o}nhuth, Alexander},
|
||
title = {Eleven grand challenges in single-cell data science},
|
||
journal = {Genome Biology},
|
||
year = 2020,
|
||
month = {Feb},
|
||
day = 07,
|
||
volume = 21,
|
||
number = 1,
|
||
pages = 31,
|
||
abstract = {The recent boom in microfluidics and combinatorial indexing
|
||
strategies, combined with low sequencing costs, has empowered
|
||
single-cell sequencing technology. Thousands---or even
|
||
millions---of cells analyzed in a single experiment amount to
|
||
a data revolution in single-cell biology and pose unique data
|
||
science problems. Here, we outline eleven challenges that will
|
||
be central to bringing this emerging field of single-cell data
|
||
science forward. For each challenge, we highlight motivating
|
||
research questions, review prior work, and formulate open
|
||
problems. This compendium is for established researchers,
|
||
newcomers, and students alike, highlighting interesting and
|
||
rewarding problems for the coming years.},
|
||
issn = {1474-760X},
|
||
doi = {10.1186/s13059-020-1926-6},
|
||
url = {https://doi.org/10.1186/s13059-020-1926-6}
|
||
}
|
||
#+end_src
|