Preprint Article Version 1 Preserved in Portico This version is not peer-reviewed

A Network Embedding Approach for Annotating Protein Structures

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. Preprints 2022, 2022040027 (doi: 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 (doi: 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

MATHEMATICS & COMPUTER SCIENCE, Other

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