Internship on link prediction in protein interaction networks

30/05/2023 – 31/05/2023 all-day

Offre en lien avec l’Action/le Réseau : – — –/– — –

Laboratoire/Entreprise : LIP6 (Sorbonne Université / CNRS)
Durée : 6 months
Contact :
Date limite de publication : 2023-05-30

Contexte :
PPI (protein-protein interaction) networks represent interactions between proteins within a living organism. PPI network maps are incomplete because checking the existence of each relationship demands specific experiments, it is therefore desirable to have means to select the most probable interactions. Recent works brought to light the fact that link prediction approaches are relevant to detect interactions between proteins.

Sujet :
The approaches in question are unsupervised, however there exist supervised methods which have been designed for analogous problems in other contexts. We think that it is possible to adapt such methods to the context of PPI networks. By defining adequate graph features – particularly specific graph motifs – in order to achieve the learning, it would be possible to improve significantly the predictive power of these methods. The purpose of the internship is to design and apply such prediction methods.

The developed methods will be trained and validated using several networks comprised of 5 000 – 18 000 proteins (nodes) establishing between 20 000 and more than 2 million experimentally validated interactions (edges) coming from reference PPI resources, namely the STRING database, the BioGRID, and the Human Reference Interactome.

Profil du candidat :
This internship is preferably directed at Master 2 students with a background in computer science or bioinformatics.

Formation et compétences requises :
Good coding skills are requested for the internship, knowledge of a widely-used language in learning, such as python, is preferable but not mandatory. An open-mind to interdisciplinary applications is certainly a plus.

Adresse d’emploi :
LIP6, 4 Place Jussieu, 75005 Paris

Document attaché : 202302081543_Stage_Link_Pred.pdf