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README.md

@@ -253,17 +253,13 @@ docker-compose down # stop the container
 Please consider citing graphein if it proves useful in your work.
 
 ```bibtex
-@article {Jamasb2020.07.15.204701,
- author = {Jamasb, Arian R. and Vi{\~n}as, Ramon and Ma, Eric J. and Harris, Charlie and Huang, Kexin and Hall, Dominic and Li{\'o}, Pietro and Blundell, Tom L.},
- title = {Graphein - a Python Library for Geometric Deep Learning and Network Analysis on Protein Structures and Interaction Networks},
- elocation-id = {2020.07.15.204701},
- year = {2021},
- doi = {10.1101/2020.07.15.204701},
- publisher = {Cold Spring Harbor Laboratory},
- abstract = {Geometric deep learning has well-motivated applications in the context of biology, a domain where relational structure in datasets can be meaningfully leveraged. Currently, efforts in both geometric deep learning and, more broadly, deep learning applied to biomolecular tasks have been hampered by a scarcity of appropriate datasets accessible to domain specialists and machine learning researchers alike. However, there has been little exploration of how to best to integrate and construct geometric representations of these datatypes. To address this, we introduce Graphein as a turn-key tool for transforming raw data from widely-used bioinformatics databases into machine learning-ready datasets in a high-throughput and flexible manner. Graphein is a Python library for constructing graph and surface-mesh representations of protein structures and biological interaction networks for computational analysis. Graphein provides utilities for data retrieval from widely-used bioinformatics databases for structural data, including the Protein Data Bank, the recently-released AlphaFold Structure Database, and for biomolecular interaction networks from STRINGdb, BioGrid, TRRUST and RegNetwork. The library interfaces with popular geometric deep learning libraries: DGL, PyTorch Geometric and PyTorch3D though remains framework agnostic as it is built on top of the PyData ecosystem to enable inter-operability with scientific computing tools and libraries. Graphein is designed to be highly flexible, allowing the user to specify each step of the data preparation, scalable to facilitate working with large protein complexes and interaction graphs, and contains useful pre-processing tools for preparing experimental files. Graphein facilitates network-based, graph-theoretic and topological analyses of structural and interaction datasets in a high-throughput manner. As example workflows, we make available two new protein structure-related datasets, previously unused by the geometric deep learning community. We envision that Graphein will facilitate developments in computational biology, graph representation learning and drug discovery.Availability and implementation Graphein is written in Python. Source code, example usage and tutorials, datasets, and documentation are made freely available under the MIT License at the following URL: graphein.aiCompeting Interest StatementThe authors have declared no competing interest.},
- URL = {https://www.biorxiv.org/content/early/2021/10/12/2020.07.15.204701},
- eprint = {https://www.biorxiv.org/content/early/2021/10/12/2020.07.15.204701.full.pdf},
- journal = {bioRxiv}
+@inproceedings{jamasb2022graphein,
+  title={Graphein - a Python Library for Geometric Deep Learning and Network Analysis on Biomolecular Structures and Interaction Networks},
+  author={Arian Rokkum Jamasb and Ramon Vi{\~n}as Torn{\'e} and Eric J Ma and Yuanqi Du and Charles Harris and Kexin Huang and Dominic Hall and Pietro Lio and Tom Leon Blundell},
+  booktitle={Advances in Neural Information Processing Systems},
+  editor={Alice H. Oh and Alekh Agarwal and Danielle Belgrave and Kyunghyun Cho},
+  year={2022},
+  url={https://openreview.net/forum?id=9xRZlV6GfOX}
 }
 
 ```

citation.bib

@@ -1,12 +1,8 @@
-@article {Jamasb2020.07.15.204701,
-	author = {Jamasb, Arian R. and Vi{\~n}as, Ramon and Ma, Eric J. and Harris, Charlie and Huang, Kexin and Hall, Dominic and Li{\'o}, Pietro and Blundell, Tom L.},
-	title = {Graphein - a Python Library for Geometric Deep Learning and Network Analysis on Protein Structures and Interaction Networks},
-	elocation-id = {2020.07.15.204701},
-	year = {2021},
-	doi = {10.1101/2020.07.15.204701},
-	publisher = {Cold Spring Harbor Laboratory},
-	abstract = {Geometric deep learning has well-motivated applications in the context of biology, a domain where relational structure in datasets can be meaningfully leveraged. Currently, efforts in both geometric deep learning and, more broadly, deep learning applied to biomolecular tasks have been hampered by a scarcity of appropriate datasets accessible to domain specialists and machine learning researchers alike. However, there has been little exploration of how to best to integrate and construct geometric representations of these datatypes. To address this, we introduce Graphein as a turn-key tool for transforming raw data from widely-used bioinformatics databases into machine learning-ready datasets in a high-throughput and flexible manner. Graphein is a Python library for constructing graph and surface-mesh representations of protein structures and biological interaction networks for computational analysis. Graphein provides utilities for data retrieval from widely-used bioinformatics databases for structural data, including the Protein Data Bank, the recently-released AlphaFold Structure Database, and for biomolecular interaction networks from STRINGdb, BioGrid, TRRUST and RegNetwork. The library interfaces with popular geometric deep learning libraries: DGL, PyTorch Geometric and PyTorch3D though remains framework agnostic as it is built on top of the PyData ecosystem to enable inter-operability with scientific computing tools and libraries. Graphein is designed to be highly flexible, allowing the user to specify each step of the data preparation, scalable to facilitate working with large protein complexes and interaction graphs, and contains useful pre-processing tools for preparing experimental files. Graphein facilitates network-based, graph-theoretic and topological analyses of structural and interaction datasets in a high-throughput manner. As example workflows, we make available two new protein structure-related datasets, previously unused by the geometric deep learning community. We envision that Graphein will facilitate developments in computational biology, graph representation learning and drug discovery.Availability and implementation Graphein is written in Python. Source code, example usage and tutorials, datasets, and documentation are made freely available under the MIT License at the following URL: graphein.aiCompeting Interest StatementThe authors have declared no competing interest.},
-	URL = {https://www.biorxiv.org/content/early/2021/10/12/2020.07.15.204701},
-	eprint = {https://www.biorxiv.org/content/early/2021/10/12/2020.07.15.204701.full.pdf},
-	journal = {bioRxiv}
+@inproceedings{jamasb2022graphein,
+	title={Graphein - a Python Library for Geometric Deep Learning and Network Analysis on Biomolecular Structures and Interaction Networks},
+	author={Arian Rokkum Jamasb and Ramon Vi{\~n}as Torn{\'e} and Eric J Ma and Yuanqi Du and Charles Harris and Kexin Huang and Dominic Hall and Pietro Lio and Tom Leon Blundell},
+	booktitle={Advances in Neural Information Processing Systems},
+	editor={Alice H. Oh and Alekh Agarwal and Danielle Belgrave and Kyunghyun Cho},
+	year={2022},
+	url={https://openreview.net/forum?id=9xRZlV6GfOX}
 }

docs/source/index.rst

@@ -79,21 +79,16 @@ If Graphein proves useful to your work, please consider citing:
 
 .. code-block:: latex
 
-   @article {Jamasb2020.07.15.204701,
-      author = {Jamasb, Arian R. and Vi{\~n}as, Ramon and Ma, Eric J. and Harris, Charlie and Huang, Kexin and Hall, Dominic and Li{\'o}, Pietro and Blundell, Tom L.},
-      title = {Graphein - a Python Library for Geometric Deep Learning and Network Analysis on Protein Structures and Interaction Networks},
-      elocation-id = {2020.07.15.204701},
-      year = {2021},
-      doi = {10.1101/2020.07.15.204701},
-      publisher = {Cold Spring Harbor Laboratory},
-      abstract = {Geometric deep learning has well-motivated applications in the context of biology, a domain where relational structure in datasets can be meaningfully leveraged. Currently, efforts in both geometric deep learning and, more broadly, deep learning applied to biomolecular tasks have been hampered by a scarcity of appropriate datasets accessible to domain specialists and machine learning researchers alike. However, there has been little exploration of how to best to integrate and construct geometric representations of these datatypes. To address this, we introduce Graphein as a turn-key tool for transforming raw data from widely-used bioinformatics databases into machine learning-ready datasets in a high-throughput and flexible manner. Graphein is a Python library for constructing graph and surface-mesh representations of protein structures and biological interaction networks for computational analysis. Graphein provides utilities for data retrieval from widely-used bioinformatics databases for structural data, including the Protein Data Bank, the recently-released AlphaFold Structure Database, and for biomolecular interaction networks from STRINGdb, BioGrid, TRRUST and RegNetwork. The library interfaces with popular geometric deep learning libraries: DGL, PyTorch Geometric and PyTorch3D though remains framework agnostic as it is built on top of the PyData ecosystem to enable inter-operability with scientific computing tools and libraries. Graphein is designed to be highly flexible, allowing the user to specify each step of the data preparation, scalable to facilitate working with large protein complexes and interaction graphs, and contains useful pre-processing tools for preparing experimental files. Graphein facilitates network-based, graph-theoretic and topological analyses of structural and interaction datasets in a high-throughput manner. As example workflows, we make available two new protein structure-related datasets, previously unused by the geometric deep learning community. We envision that Graphein will facilitate developments in computational biology, graph representation learning and drug discovery.Availability and implementation Graphein is written in Python. Source code, example usage and tutorials, datasets, and documentation are made freely available under the MIT License at the following URL: graphein.aiCompeting Interest StatementThe authors have declared no competing interest.},
-      URL = {https://www.biorxiv.org/content/early/2021/10/12/2020.07.15.204701},
-      eprint = {https://www.biorxiv.org/content/early/2021/10/12/2020.07.15.204701.full.pdf},
-      journal = {bioRxiv}
+   @inproceedings{jamasb2022graphein,
+      title={Graphein - a Python Library for Geometric Deep Learning and Network Analysis on Biomolecular Structures and Interaction Networks},
+      author={Arian Rokkum Jamasb and Ramon Vi{\~n}as Torn{\'e} and Eric J Ma and Yuanqi Du and Charles Harris and Kexin Huang and Dominic Hall and Pietro Lio and Tom Leon Blundell},
+      booktitle={Advances in Neural Information Processing Systems},
+      editor={Alice H. Oh and Alekh Agarwal and Danielle Belgrave and Kyunghyun Cho},
+      year={2022},
+      url={https://openreview.net/forum?id=9xRZlV6GfOX}
    }
 
 
-
 Indices and tables
 ==================