Machine learning applications in drug development
Type de document :
Compte-rendu et recension critique d'ouvrage
Titre :
Machine learning applications in drug development
Auteur(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)]
Titre de la revue :
Computational and Structural Biotechnology Journal
Pagination :
241-252
Éditeur :
Elsevier
Date de publication :
2020
ISSN :
2001-0370
Discipline(s) HAL :
Statistiques [stat]/Machine Learning [stat.ML]
Résumé en anglais : [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 ...
Lire la suite >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.Lire moins >
Lire la suite >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.Lire moins >
Langue :
Anglais
Vulgarisation :
Non
Projet ANR :
Collections :
Source :
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- https://doi.org/10.1016/j.csbj.2019.12.006
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