Update bibliography
This commit is contained in:
parent
c1fe9158ce
commit
59529f5142
GPG Key ID:
9893DA236405AF57
|
|
@ -5,8 +5,7 @@
|
|||
#+OPTIONS: <:nil c:nil todo:nil H:5
|
||||
#+auto_tangle: t
|
||||
* Deep Learning
|
||||
** Transformers
|
||||
*** Attention is All You Need
|
||||
** Attention is All You Need
|
||||
#+begin_src bibtex
|
||||
@article{https://doi.org/10.48550/arxiv.1706.03762,
|
||||
doi = {10.48550/ARXIV.1706.03762},
|
||||
|
|
@ -25,7 +24,7 @@
|
|||
#+end_src
|
||||
|
||||
#+LaTeX: \printbibliography[heading=none]
|
||||
*** Axial Attention in Multidimensional Transformers
|
||||
** Axial Attention in Multidimensional Transformers
|
||||
#+begin_src bibtex
|
||||
@article{https://doi.org/10.48550/arxiv.1912.12180,
|
||||
doi = {10.48550/ARXIV.1912.12180},
|
||||
|
|
@ -41,7 +40,7 @@
|
|||
copyright = {arXiv.org perpetual, non-exclusive license}
|
||||
}
|
||||
#+end_src
|
||||
*** Longformer: The Long-Document Transformer
|
||||
** Longformer: The Long-Document Transformer
|
||||
#+begin_src bibtex
|
||||
@article{https://doi.org/10.48550/arxiv.2004.05150,
|
||||
doi = {10.48550/ARXIV.2004.05150},
|
||||
|
|
@ -55,7 +54,7 @@
|
|||
copyright = {arXiv.org perpetual, non-exclusive license}
|
||||
}
|
||||
#+end_src
|
||||
*** Transformer-XL: Attentive Language Models Beyond a Fixed-Length Context
|
||||
** Transformer-XL: Attentive Language Models Beyond a Fixed-Length Context
|
||||
#+begin_src bibtex
|
||||
@article{https://doi.org/10.48550/arxiv.1901.02860,
|
||||
doi = {10.48550/ARXIV.1901.02860},
|
||||
|
|
@ -73,7 +72,7 @@
|
|||
International}
|
||||
}
|
||||
#+end_src
|
||||
*** BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding
|
||||
** BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding
|
||||
#+begin_src bibtex
|
||||
@inproceedings{devlin-etal-2019-bert,
|
||||
title = "{BERT}: Pre-training of Deep Bidirectional Transformers for
|
||||
|
|
@ -112,7 +111,7 @@
|
|||
#+end_src
|
||||
|
||||
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.
|
||||
*** Fast Transformers with Clustered Attention
|
||||
** Fast Transformers with Clustered Attention
|
||||
#+begin_src bibtex
|
||||
@article{https://doi.org/10.48550/arxiv.2007.04825,
|
||||
doi = {10.48550/ARXIV.2007.04825},
|
||||
|
|
@ -128,7 +127,7 @@ A masked language model (MLM) randomly masks some of the tokens from the input,
|
|||
copyright = {arXiv.org perpetual, non-exclusive license}
|
||||
}
|
||||
#+end_src
|
||||
*** The elephant in the interpretability room: Why use attention as explanation when we have saliency methods?
|
||||
** The elephant in the interpretability room: Why use attention as explanation when we have saliency methods?
|
||||
#+begin_src bibtex
|
||||
@inproceedings{bastings-filippova-2020-elephant,
|
||||
title = "The elephant in the interpretability room: Why use
|
||||
|
|
@ -160,7 +159,59 @@ A masked language model (MLM) randomly masks some of the tokens from the input,
|
|||
state the goal and user for their explanations.",
|
||||
}
|
||||
#+end_src
|
||||
** MultiMAE: Multi-modal Multi-task Masked Autoencoders
|
||||
#+begin_src bibtex
|
||||
@article{https://doi.org/10.48550/arxiv.2204.01678,
|
||||
doi = {10.48550/ARXIV.2204.01678},
|
||||
url = {https://arxiv.org/abs/2204.01678},
|
||||
author = {Bachmann, Roman and Mizrahi, David and Atanov, Andrei and
|
||||
Zamir, Amir},
|
||||
keywords = {Computer Vision and Pattern Recognition (cs.CV), Machine
|
||||
Learning (cs.LG), FOS: Computer and information sciences, FOS:
|
||||
Computer and information sciences},
|
||||
title = {MultiMAE: Multi-modal Multi-task Masked Autoencoders},
|
||||
publisher = {arXiv},
|
||||
year = 2022,
|
||||
copyright = {arXiv.org perpetual, non-exclusive license}
|
||||
}
|
||||
#+end_src
|
||||
* Deep Learning + Biology
|
||||
** CpG Transformer for imputation of single-cell methylomes
|
||||
#+begin_src bibtex
|
||||
@article{10.1093/bioinformatics/btab746,
|
||||
author = {De Waele, Gaetan and Clauwaert, Jim and Menschaert, Gerben
|
||||
and Waegeman, Willem},
|
||||
title = "{CpG Transformer for imputation of single-cell methylomes}",
|
||||
journal = {Bioinformatics},
|
||||
volume = 38,
|
||||
number = 3,
|
||||
pages = {597-603},
|
||||
year = 2021,
|
||||
month = 10,
|
||||
abstract = "{The adoption of current single-cell DNA methylation
|
||||
sequencing protocols is hindered by incomplete coverage,
|
||||
outlining the need for effective imputation techniques. The
|
||||
task of imputing single-cell (methylation) data requires
|
||||
models to build an understanding of underlying biological
|
||||
processes.We adapt the transformer neural network architecture
|
||||
to operate on methylation matrices through combining axial
|
||||
attention with sliding window self-attention. The obtained CpG
|
||||
Transformer displays state-of-the-art performances on a wide
|
||||
range of scBS-seq and scRRBS-seq datasets. Furthermore, we
|
||||
demonstrate the interpretability of CpG Transformer and
|
||||
illustrate its rapid transfer learning properties, allowing
|
||||
practitioners to train models on new datasets with a limited
|
||||
computational and time budget.CpG Transformer is freely
|
||||
available at
|
||||
https://github.com/gdewael/cpg-transformer.Supplementary data
|
||||
are available at Bioinformatics online.}",
|
||||
issn = {1367-4803},
|
||||
doi = {10.1093/bioinformatics/btab746},
|
||||
url = {https://doi.org/10.1093/bioinformatics/btab746},
|
||||
eprint =
|
||||
{https://academic.oup.com/bioinformatics/article-pdf/38/3/597/42167564/btab746.pdf},
|
||||
}
|
||||
#+end_src
|
||||
** MSA Transformer
|
||||
#+begin_src bibtex
|
||||
@article {Rao2021.02.12.430858,
|
||||
|
|
@ -255,4 +306,140 @@ A masked language model (MLM) randomly masks some of the tokens from the input,
|
|||
url = {https://doi.org/10.1038/s41586-021-03819-2}
|
||||
}
|
||||
#+end_src
|
||||
** MultiVI: deep generative model for the integration of multi-modal data
|
||||
#+begin_src bibtex
|
||||
@article {Ashuach2021.08.20.457057,
|
||||
author = {Ashuach, Tal and Gabitto, Mariano I. and Jordan, Michael I.
|
||||
and Yosef, Nir},
|
||||
title = {MultiVI: deep generative model for the integration of
|
||||
multi-modal data},
|
||||
elocation-id = {2021.08.20.457057},
|
||||
year = 2021,
|
||||
doi = {10.1101/2021.08.20.457057},
|
||||
publisher = {Cold Spring Harbor Laboratory},
|
||||
abstract = {Jointly profiling the transcriptional and chromatin
|
||||
accessibility landscapes of single-cells is a powerful
|
||||
technique to characterize cellular populations. Here we
|
||||
present MultiVI, a probabilistic model to analyze such
|
||||
multiomic data and integrate it with single modality datasets.
|
||||
MultiVI creates a joint representation that accurately
|
||||
reflects both chromatin and transcriptional properties of the
|
||||
cells even when one modality is missing. It also imputes
|
||||
missing data, corrects for batch effects and is available in
|
||||
the scvi-tools framework:
|
||||
https://docs.scvi-tools.org/.Competing Interest StatementThe
|
||||
authors have declared no competing interest.},
|
||||
URL =
|
||||
{https://www.biorxiv.org/content/early/2021/09/07/2021.08.20.457057},
|
||||
eprint =
|
||||
{https://www.biorxiv.org/content/early/2021/09/07/2021.08.20.457057.full.pdf},
|
||||
journal = {bioRxiv}
|
||||
}
|
||||
#+end_src
|
||||
* Biology
|
||||
** Cobolt: integrative analysis of multimodal single-cell sequencing data
|
||||
#+begin_src bibtex
|
||||
@article{Gong2021,
|
||||
author = {Gong, Boying and Zhou, Yun and Purdom, Elizabeth},
|
||||
title = {Cobolt: integrative analysis of multimodal single-cell
|
||||
sequencing data},
|
||||
journal = {Genome Biology},
|
||||
year = 2021,
|
||||
month = {Dec},
|
||||
day = 28,
|
||||
volume = 22,
|
||||
number = 1,
|
||||
pages = 351,
|
||||
abstract = {A growing number of single-cell sequencing platforms enable
|
||||
joint profiling of multiple omics from the same cells. We
|
||||
present Cobolt, a novel method that not only allows for
|
||||
analyzing the data from joint-modality platforms, but provides
|
||||
a coherent framework for the integration of multiple datasets
|
||||
measured on different modalities. We demonstrate its
|
||||
performance on multi-modality data of gene expression and
|
||||
chromatin accessibility and illustrate the integration
|
||||
abilities of Cobolt by jointly analyzing this multi-modality
|
||||
data with single-cell RNA-seq and ATAC-seq datasets.},
|
||||
issn = {1474-760X},
|
||||
doi = {10.1186/s13059-021-02556-z},
|
||||
url = {https://doi.org/10.1186/s13059-021-02556-z}
|
||||
}
|
||||
#+end_src
|
||||
** MUON: multimodal omics analysis framework
|
||||
#+begin_src bibtex
|
||||
@article{Bredikhin2022,
|
||||
author = {Bredikhin, Danila and Kats, Ilia and Stegle, Oliver},
|
||||
title = {MUON: multimodal omics analysis framework},
|
||||
journal = {Genome Biology},
|
||||
year = 2022,
|
||||
month = {Feb},
|
||||
day = 01,
|
||||
volume = 23,
|
||||
number = 1,
|
||||
pages = 42,
|
||||
abstract = {Advances in multi-omics have led to an explosion of
|
||||
multimodal datasets to address questions from basic biology to
|
||||
translation. While these data provide novel opportunities for
|
||||
discovery, they also pose management and analysis challenges,
|
||||
thus motivating the development of tailored computational
|
||||
solutions. Here, we present a data standard and an analysis
|
||||
framework for multi-omics, MUON, designed to organise,
|
||||
analyse, visualise, and exchange multimodal data. MUON stores
|
||||
multimodal data in an efficient yet flexible and interoperable
|
||||
data structure. MUON enables a versatile range of analyses,
|
||||
from data preprocessing to flexible multi-omics alignment.},
|
||||
issn = {1474-760X},
|
||||
doi = {10.1186/s13059-021-02577-8},
|
||||
url = {https://doi.org/10.1186/s13059-021-02577-8}
|
||||
}
|
||||
#+end_src
|
||||
** Multimodal single cell data integration challenge: Results and lessons learned
|
||||
#+begin_src bibtex
|
||||
@inproceedings{pmlr-v176-lance22a,
|
||||
title = {Multimodal single cell data integration challenge: Results
|
||||
and lessons learned},
|
||||
author = {Lance, Christopher and Luecken, Malte D. and Burkhardt,
|
||||
Daniel B. and Cannoodt, Robrecht and Rautenstrauch, Pia and
|
||||
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
|
||||
integration competition participants, NeurIPS 2021 Multimodal
|
||||
and Pisco, Angela Oliveira and Bloom, Jonathan and
|
||||
Krishnaswamy, Smita and Theis, Fabian J.},
|
||||
booktitle = {Proceedings of the NeurIPS 2021 Competitions and
|
||||
Demonstrations Track},
|
||||
pages = {162--176},
|
||||
year = 2022,
|
||||
editor = {Kiela, Douwe and Ciccone, Marco and Caputo, Barbara},
|
||||
volume = 176,
|
||||
series = {Proceedings of Machine Learning Research},
|
||||
month = {06--14 Dec},
|
||||
publisher = {PMLR},
|
||||
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
|
||||
|
|
|
|||
|
|
@ -134,6 +134,54 @@
|
|||
state the goal and user for their explanations.",
|
||||
}
|
||||
|
||||
@article{https://doi.org/10.48550/arxiv.2204.01678,
|
||||
doi = {10.48550/ARXIV.2204.01678},
|
||||
url = {https://arxiv.org/abs/2204.01678},
|
||||
author = {Bachmann, Roman and Mizrahi, David and Atanov, Andrei and
|
||||
Zamir, Amir},
|
||||
keywords = {Computer Vision and Pattern Recognition (cs.CV), Machine
|
||||
Learning (cs.LG), FOS: Computer and information sciences, FOS:
|
||||
Computer and information sciences},
|
||||
title = {MultiMAE: Multi-modal Multi-task Masked Autoencoders},
|
||||
publisher = {arXiv},
|
||||
year = 2022,
|
||||
copyright = {arXiv.org perpetual, non-exclusive license}
|
||||
}
|
||||
|
||||
@article{10.1093/bioinformatics/btab746,
|
||||
author = {De Waele, Gaetan and Clauwaert, Jim and Menschaert, Gerben
|
||||
and Waegeman, Willem},
|
||||
title = "{CpG Transformer for imputation of single-cell methylomes}",
|
||||
journal = {Bioinformatics},
|
||||
volume = 38,
|
||||
number = 3,
|
||||
pages = {597-603},
|
||||
year = 2021,
|
||||
month = 10,
|
||||
abstract = "{The adoption of current single-cell DNA methylation
|
||||
sequencing protocols is hindered by incomplete coverage,
|
||||
outlining the need for effective imputation techniques. The
|
||||
task of imputing single-cell (methylation) data requires
|
||||
models to build an understanding of underlying biological
|
||||
processes.We adapt the transformer neural network architecture
|
||||
to operate on methylation matrices through combining axial
|
||||
attention with sliding window self-attention. The obtained CpG
|
||||
Transformer displays state-of-the-art performances on a wide
|
||||
range of scBS-seq and scRRBS-seq datasets. Furthermore, we
|
||||
demonstrate the interpretability of CpG Transformer and
|
||||
illustrate its rapid transfer learning properties, allowing
|
||||
practitioners to train models on new datasets with a limited
|
||||
computational and time budget.CpG Transformer is freely
|
||||
available at
|
||||
https://github.com/gdewael/cpg-transformer.Supplementary data
|
||||
are available at Bioinformatics online.}",
|
||||
issn = {1367-4803},
|
||||
doi = {10.1093/bioinformatics/btab746},
|
||||
url = {https://doi.org/10.1093/bioinformatics/btab746},
|
||||
eprint =
|
||||
{https://academic.oup.com/bioinformatics/article-pdf/38/3/597/42167564/btab746.pdf},
|
||||
}
|
||||
|
||||
@article {Rao2021.02.12.430858,
|
||||
author = {Rao, Roshan and Liu, Jason and Verkuil, Robert and Meier,
|
||||
Joshua and Canny, John F. and Abbeel, Pieter and Sercu, Tom
|
||||
|
|
@ -223,3 +271,131 @@
|
|||
doi = {10.1038/s41586-021-03819-2},
|
||||
url = {https://doi.org/10.1038/s41586-021-03819-2}
|
||||
}
|
||||
|
||||
@article {Ashuach2021.08.20.457057,
|
||||
author = {Ashuach, Tal and Gabitto, Mariano I. and Jordan, Michael I.
|
||||
and Yosef, Nir},
|
||||
title = {MultiVI: deep generative model for the integration of
|
||||
multi-modal data},
|
||||
elocation-id = {2021.08.20.457057},
|
||||
year = 2021,
|
||||
doi = {10.1101/2021.08.20.457057},
|
||||
publisher = {Cold Spring Harbor Laboratory},
|
||||
abstract = {Jointly profiling the transcriptional and chromatin
|
||||
accessibility landscapes of single-cells is a powerful
|
||||
technique to characterize cellular populations. Here we
|
||||
present MultiVI, a probabilistic model to analyze such
|
||||
multiomic data and integrate it with single modality datasets.
|
||||
MultiVI creates a joint representation that accurately
|
||||
reflects both chromatin and transcriptional properties of the
|
||||
cells even when one modality is missing. It also imputes
|
||||
missing data, corrects for batch effects and is available in
|
||||
the scvi-tools framework:
|
||||
https://docs.scvi-tools.org/.Competing Interest StatementThe
|
||||
authors have declared no competing interest.},
|
||||
URL =
|
||||
{https://www.biorxiv.org/content/early/2021/09/07/2021.08.20.457057},
|
||||
eprint =
|
||||
{https://www.biorxiv.org/content/early/2021/09/07/2021.08.20.457057.full.pdf},
|
||||
journal = {bioRxiv}
|
||||
}
|
||||
|
||||
@article{Gong2021,
|
||||
author = {Gong, Boying and Zhou, Yun and Purdom, Elizabeth},
|
||||
title = {Cobolt: integrative analysis of multimodal single-cell
|
||||
sequencing data},
|
||||
journal = {Genome Biology},
|
||||
year = 2021,
|
||||
month = {Dec},
|
||||
day = 28,
|
||||
volume = 22,
|
||||
number = 1,
|
||||
pages = 351,
|
||||
abstract = {A growing number of single-cell sequencing platforms enable
|
||||
joint profiling of multiple omics from the same cells. We
|
||||
present Cobolt, a novel method that not only allows for
|
||||
analyzing the data from joint-modality platforms, but provides
|
||||
a coherent framework for the integration of multiple datasets
|
||||
measured on different modalities. We demonstrate its
|
||||
performance on multi-modality data of gene expression and
|
||||
chromatin accessibility and illustrate the integration
|
||||
abilities of Cobolt by jointly analyzing this multi-modality
|
||||
data with single-cell RNA-seq and ATAC-seq datasets.},
|
||||
issn = {1474-760X},
|
||||
doi = {10.1186/s13059-021-02556-z},
|
||||
url = {https://doi.org/10.1186/s13059-021-02556-z}
|
||||
}
|
||||
|
||||
@article{Bredikhin2022,
|
||||
author = {Bredikhin, Danila and Kats, Ilia and Stegle, Oliver},
|
||||
title = {MUON: multimodal omics analysis framework},
|
||||
journal = {Genome Biology},
|
||||
year = 2022,
|
||||
month = {Feb},
|
||||
day = 01,
|
||||
volume = 23,
|
||||
number = 1,
|
||||
pages = 42,
|
||||
abstract = {Advances in multi-omics have led to an explosion of
|
||||
multimodal datasets to address questions from basic biology to
|
||||
translation. While these data provide novel opportunities for
|
||||
discovery, they also pose management and analysis challenges,
|
||||
thus motivating the development of tailored computational
|
||||
solutions. Here, we present a data standard and an analysis
|
||||
framework for multi-omics, MUON, designed to organise,
|
||||
analyse, visualise, and exchange multimodal data. MUON stores
|
||||
multimodal data in an efficient yet flexible and interoperable
|
||||
data structure. MUON enables a versatile range of analyses,
|
||||
from data preprocessing to flexible multi-omics alignment.},
|
||||
issn = {1474-760X},
|
||||
doi = {10.1186/s13059-021-02577-8},
|
||||
url = {https://doi.org/10.1186/s13059-021-02577-8}
|
||||
}
|
||||
|
||||
@inproceedings{pmlr-v176-lance22a,
|
||||
title = {Multimodal single cell data integration challenge: Results
|
||||
and lessons learned},
|
||||
author = {Lance, Christopher and Luecken, Malte D. and Burkhardt,
|
||||
Daniel B. and Cannoodt, Robrecht and Rautenstrauch, Pia and
|
||||
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
|
||||
integration competition participants, NeurIPS 2021 Multimodal
|
||||
and Pisco, Angela Oliveira and Bloom, Jonathan and
|
||||
Krishnaswamy, Smita and Theis, Fabian J.},
|
||||
booktitle = {Proceedings of the NeurIPS 2021 Competitions and
|
||||
Demonstrations Track},
|
||||
pages = {162--176},
|
||||
year = 2022,
|
||||
editor = {Kiela, Douwe and Ciccone, Marco and Caputo, Barbara},
|
||||
volume = 176,
|
||||
series = {Proceedings of Machine Learning Research},
|
||||
month = {06--14 Dec},
|
||||
publisher = {PMLR},
|
||||
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.}
|
||||
}
|
||||
|
|
|
|||
Loading…
Reference in New Issue