Deep Latent Factor Model for Predicting Drug Target Interactions

Aanchal Mongia (1) , Vidit Jain (1, 2) , Emilie Chouzenoux (3, 4) , Angshul Majumdar (1)

1 IIIT-Delhi - Indraprastha Institute of Information Technology [New Delhi]
2 Yahoo Labs
3 OPIS - OPtimisation Imagerie et Santé
4 LIGM - Laboratoire d'Informatique Gaspard-Monge

In drug target interaction (DTI) the interactions of some (a subset) drugs on some (a subset) targets are known. The goal is to predict the interactions of all drugs on all targets. One approach is to formulate this as a matrix completion problem, where the matrix of interactions having drugs along the rows and targets along the columns is partially filled. So far standard matrix completion approaches such as nuclear norm minimization and matrix factorization have been used to address the problem. In this work, we propose a deep matrix factorization approach to improve the prediction results. Experiments have been performed on benchmark databases and comparison carried out with some state-of-the-art algorithms. Empirically our proposed deep method, outperforms all the techniques compared against.

Mots clés: Matrix completion; Deep learning; Matrix factorization; Drug target interaction

Domaines: Traitement du signal et de l'image [eess.SP]

Aanchal Mongia, Vidit Jain, Emilie Chouzenoux, Angshul Majumdar. Deep Latent Factor Model for Predicting Drug Target Interactions. ICASSP 2019 - IEEE International Conference on Acoustics, Speech and Signal Processing, May 2019, Brighton, United Kingdom. ⟨10.1109/icassp.2019.8683123⟩. ⟨hal-02406989⟩