Predicting the frequencies of drug side effects

被引:76
|
作者
Galeano, Diego [1 ,2 ]
Li, Shantao [3 ,4 ]
Gerstein, Mark [5 ,6 ,7 ]
Paccanaro, Alberto [1 ,2 ]
机构
[1] Royal Holloway Univ London, Dept Comp Sci, Ctr Syst & Synthet Biol, Egham Hill, Egham, Surrey, England
[2] Fundacao Getulio Vargas, Sch Appl Math, Rio De Janeiro, Brazil
[3] Stanford Univ, Dept Comp Sci, Stanford, CA 94305 USA
[4] Stanford Univ, Dept Biomed Data Sci, Stanford, CA 94305 USA
[5] Yale Univ, Dept Mol Biophys & Biochem, New Haven, CT 06520 USA
[6] Yale Univ, Dept Comp Sci, New Haven, CT 06520 USA
[7] Yale Univ, Dept Stat & Data Sci, New Haven, CT 06520 USA
基金
美国国家科学基金会; 英国生物技术与生命科学研究理事会;
关键词
GAMMA-SECRETASE INHIBITOR; CLINICAL-TRIALS; HOSPITALIZED-PATIENTS; MUTATIONAL PROCESSES; SAFETY; ASSOCIATIONS; ALGORITHMS; SIGNATURES; EVENTS;
D O I
10.1038/s41467-020-18305-y
中图分类号
O [数理科学和化学]; P [天文学、地球科学]; Q [生物科学]; N [自然科学总论];
学科分类号
07 ; 0710 ; 09 ;
摘要
A central issue in drug risk-benefit assessment is identifying frequencies of side effects in humans. Currently, frequencies are experimentally determined in randomised controlled clinical trials. We present a machine learning framework for computationally predicting frequencies of drug side effects. Our matrix decomposition algorithm learns latent signatures of drugs and side effects that are both reproducible and biologically interpretable. We show the usefulness of our approach on 759 structurally and therapeutically diverse drugs and 994 side effects from all human physiological systems. Our approach can be applied to any drug for which a small number of side effect frequencies have been identified, in order to predict the frequencies of further, yet unidentified, side effects. We show that our model is informative of the biology underlying drug activity: individual components of the drug signatures are related to the distinct anatomical categories of the drugs and to the specific drug routes of administration.
引用
收藏
页数:14
相关论文
共 50 条
  • [31] Drug Side Effects in Gastroenterology
    不详
    ZEITSCHRIFT FUR GASTROENTEROLOGIE, 2020, 58 (05): : 476 - 477
  • [32] Similarity-Based Method with Multiple-Feature Sampling for Predicting Drug Side Effects
    Wu, Zixin
    Chen, Lei
    COMPUTATIONAL AND MATHEMATICAL METHODS IN MEDICINE, 2022, 2022
  • [33] PISTON: Predicting drug indications and side effects using topic modeling and natural language processing
    Jang, Giup
    Lee, Taekeon
    Hwang, Soyoun
    Park, Chihyun
    Ahn, Jaegyoon
    Seo, Sukyung
    Hwang, Youhyeon
    Yoon, Youngmi
    JOURNAL OF BIOMEDICAL INFORMATICS, 2018, 87 : 96 - 107
  • [34] Predicting success and side effects with imiquimod therapy
    Freeman, Andrew
    Freeman, Michael
    JOURNAL OF THE AMERICAN ACADEMY OF DERMATOLOGY, 2012, 66 (04) : AB157 - AB157
  • [35] Predicting Side-effects of Chemotherapy Treatment
    不详
    ATLA-ALTERNATIVES TO LABORATORY ANIMALS, 2013, 41 (02): : 146 - 146
  • [36] Induced drug uveitis and drug side effects in ophthalmology
    Trad, S.
    Bonnet, C.
    Monnet, D.
    REVUE DE MEDECINE INTERNE, 2018, 39 (09): : 699 - 710
  • [37] An Algorithmic Framework for Predicting Side Effects of Drugs
    Atias, Nir
    Sharan, Roded
    JOURNAL OF COMPUTATIONAL BIOLOGY, 2011, 18 (03) : 207 - 218
  • [38] Predicting multiple drugs side effects with a general drug-target interaction thermodynamic Markov model
    González-Díaz, H
    Cruz-Monteagudo, M
    Molina, R
    Tenorio, E
    Uriarte, E
    BIOORGANIC & MEDICINAL CHEMISTRY, 2005, 13 (04) : 1119 - 1129
  • [39] Predicting drug combination side effects based on a metapath-based heterogeneous graph neural network
    Tian, Leixia
    Wang, Qi
    Zhou, Zhiheng
    Liu, Xiya
    Zhang, Ming
    Yan, Guiying
    BMC BIOINFORMATICS, 2025, 26 (01):
  • [40] A neighborhood-regularization method leveraging multiview data for predicting the frequency of drug-side effects
    Wang, Lin
    Sun, Chenhao
    Xu, Xianyu
    Li, Jia
    Zhang, Wenjuan
    BIOINFORMATICS, 2023, 39 (09)