Version 1
: Received: 4 April 2022 / Approved: 5 April 2022 / Online: 5 April 2022 (12:02:35 CEST)
How to cite:
Puccio, B.; Di Paola, L.; Lo Moio, U.; Veltri, P.; Guzzi, P.H. A Network Embedding Approach for Annotating Protein Structures. Preprints2022, 2022040027. https://doi.org/10.20944/preprints202204.0027.v1
Puccio, B.; Di Paola, L.; Lo Moio, U.; Veltri, P.; Guzzi, P.H. A Network Embedding Approach for Annotating Protein Structures. Preprints 2022, 2022040027. https://doi.org/10.20944/preprints202204.0027.v1
Puccio, B.; Di Paola, L.; Lo Moio, U.; Veltri, P.; Guzzi, P.H. A Network Embedding Approach for Annotating Protein Structures. Preprints2022, 2022040027. https://doi.org/10.20944/preprints202204.0027.v1
APA Style
Puccio, B., Di Paola, L., Lo Moio, U., Veltri, P., & Guzzi, P.H. (2022). A Network Embedding Approach for Annotating Protein Structures. Preprints. https://doi.org/10.20944/preprints202204.0027.v1
Chicago/Turabian Style
Puccio, B., Pierangelo Veltri and Pietro Hiram Guzzi. 2022 "A Network Embedding Approach for Annotating Protein Structures" Preprints. https://doi.org/10.20944/preprints202204.0027.v1
Abstract
Protein Contact Network (PCN) is an emerging paradigm for modelling protein structure. A common approach to interpreting such data is through network-based analyses. It has been shown that clustering analysis may discover allostery in PCN. Nevertheless Network Embedding has shown good performances in discovering hidden communities and structures in network. In this work, we compare some approaches for graph embedding with respect to some classical clustering approaches for annotating protein structures.
Keywords
Protein Contact Network
Subject
Computer Science and Mathematics, Computer Science
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
