Imperial College London
Portrait Picture

ProfessorMichaelBronstein

Faculty of Engineering, Department of Computing

Visiting Professor
 
 
 
//

Contact

 

m.bronstein Website

 
 
//

Location

 

569Huxley BuildingSouth Kensington Campus

//

Summary

 

Publications

Citation

BibTex format

@unpublished{Frasca:2019,
author = {Frasca, F and Galeano, D and Gonzalez, G and Laponogov, I and Veselkov, K and Paccanaro, A and Bronstein, MM},
publisher = {arxiv},
title = {Learning interpretable disease self-representations for drug repositioning},
url = {http://arxiv.org/abs/1909.06609v2},
year = {2019}
}
Download

RIS format (EndNote, RefMan)

TY  - UNPB
AB  - Drug repositioning is an attractive cost-efficient strategy for thedevelopment of treatments for human diseases. Here, we propose an interpretablemodel that learns disease self-representations for drug repositioning. Ourself-representation model represents each disease as a linear combination of afew other diseases. We enforce proximity in the learnt representations in a wayto preserve the geometric structure of the human phenome network - adomain-specific knowledge that naturally adds relational inductive bias to thedisease self-representations. We prove that our method is globally optimal andshow results outperforming state-of-the-art drug repositioning approaches. Wefurther show that the disease self-representations are biologicallyinterpretable.
AU  - Frasca,F
AU  - Galeano,D
AU  - Gonzalez,G
AU  - Laponogov,I
AU  - Veselkov,K
AU  - Paccanaro,A
AU  - Bronstein,MM
PB  - arxiv
PY  - 2019///
TI  - Learning interpretable disease self-representations for drug repositioning
UR  - http://arxiv.org/abs/1909.06609v2
UR  - http://hdl.handle.net/10044/1/74930
ER  -
Download