Update README.md

README.md

@@ -8,23 +8,22 @@
 ## Citation:
 
 ```
-@article {Mohseni Behbahani2022.04.05.487134,
-	author = {Mohseni Behbahani, Yasser and Crouzet, Simon and Laine, {\'E}lodie and Carbone, Alessandra},
-	title = {Deep Local Analysis evaluates protein docking conformations with locally oriented cubes},
-	elocation-id = {2022.04.05.487134},
-	year = {2022},
-	doi = {10.1101/2022.04.05.487134},
-	publisher = {Cold Spring Harbor Laboratory},
-	URL = {https://www.biorxiv.org/content/early/2022/04/06/2022.04.05.487134},
-	eprint = {https://www.biorxiv.org/content/early/2022/04/06/2022.04.05.487134.full.pdf},
-	journal = {bioRxiv}
+@article{10.1093/bioinformatics/btac551,
+    author = {Mohseni Behbahani, Yasser and Crouzet, Simon and Laine, Elodie and Carbone, Alessandra},
+    title = "{Deep Local Analysis evaluates protein docking conformations with locally oriented cubes}",
+    journal = {Bioinformatics},
+    year = {2022},
+    month = {08},
+    issn = {1367-4803},
+    doi = {10.1093/bioinformatics/btac551},
+    url = {https://doi.org/10.1093/bioinformatics/btac551}
 }
 ```
 
 ## Overview
 ![](Images/method5.svg.png?raw=true "DLA-Ranker")
 
-Deep Local Analysis (DLA)-Ranker is a deep learning framework applying 3D convolutions to a set of locally oriented cubes representing the protein interface. It explicitly considers the local geometry of
+Deep Local Analysis [(DLA)-Ranker](https://academic.oup.com/bioinformatics/advance-article/doi/10.1093/bioinformatics/btac551/6665900) is a deep learning framework applying 3D convolutions to a set of locally oriented cubes representing the protein interface. It explicitly considers the local geometry of
 the interfacial residues along with their neighboring atoms and the regions of the interface with different solvent accessibility. DLA-Ranker identifies near-native conformations and discovers alternative interfaces from ensembles generated by molecular docking.
 
 #### Features: