Machine learning applications in drug development

Réda, Clémence; Kaufmann, Emilie; Delahaye-Duriez, Andrée

Document type :

Compte-rendu et recension critique d'ouvrage

DOI :

10.1016/j.csbj.2019.12.006

Title :

Machine learning applications in drug development

Author(s) :

Réda, Clémence [Auteur]
Maladies neurodéveloppementales et neurovasculaires [NeuroDiderot (UMR_S_1141 / U1141)]
Kaufmann, Emilie [Auteur]

Centre National de la Recherche Scientifique [CNRS]
Centre de Recherche en Informatique, Signal et Automatique de Lille - UMR 9189 [CRIStAL]
Sequential Learning [SEQUEL]
Scool [Scool]
Delahaye-Duriez, Andrée [Auteur]
Hôpital Jean Verdier [AP-HP]
Maladies neurodéveloppementales et neurovasculaires [NeuroDiderot (UMR_S_1141 / U1141)]

Journal title :

Computational and Structural Biotechnology Journal

Pages :

241-252

Publisher :

Elsevier

Publication date :

2020

ISSN :

2001-0370

HAL domain(s) :

Statistiques [stat]/Machine Learning [stat.ML]

English abstract : [en]

Due to the huge amount of biological and medical data available today, along with well-established machine learning algorithms, the design of largely automated drug development pipelines can now be envisioned. These pipelines ...
Show more >Due to the huge amount of biological and medical data available today, along with well-established machine learning algorithms, the design of largely automated drug development pipelines can now be envisioned. These pipelines may guide, or speed up, drug discovery; provide a better understanding of diseases and associated biological phenomena; help planning preclinical wet-lab experiments, and even future clinical trials. This automation of the drug development process might be key to the current issue of low productivity rate that pharmaceutical companies currently face. In this survey, we will particularly focus on two classes of methods: sequential learning and recommender systems, which are active biomedical fields of research.Show less >

Language :

Anglais

Popular science :

Non

ANR Project :

Identification des régulateurs clés d'un réseau de gènes associé à l'épilepsie
MicroARNs et réseaux de gènes co-exprimés associés aux traumatismes dans l'enfance: de la cicatrice moléculaire aux pistes thérapeutiques dans les troubles bipolaires

Collections :