Machine learning applications in drug development
Document type :
Compte-rendu et recension critique d'ouvrage
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)]
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 >
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 :
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