Update bibliography

docs/Bibliography.org

@@ -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

docs/bibliography.bib

@@ -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.}
+}