Privacy-preserving techniques for decentralized and secure machine learning in drug discovery
出版年份 2023 全文链接
标题
Privacy-preserving techniques for decentralized and secure machine learning in drug discovery
作者
关键词
-
出版物
DRUG DISCOVERY TODAY
Volume -, Issue -, Pages 103820
出版商
Elsevier BV
发表日期
2023-11-06
DOI
10.1016/j.drudis.2023.103820
参考文献
相关参考文献
注意:仅列出部分参考文献,下载原文获取全部文献信息。- Federated learning for molecular discovery
- (2023) Thierry Hanser CURRENT OPINION IN STRUCTURAL BIOLOGY
- Differential Private Deep Learning Models for Analyzing Breast Cancer Omics Data
- (2022) Md. Mohaiminul Islam et al. Frontiers in Oncology
- EasySMPC: a simple but powerful no-code tool for practical secure multiparty computation
- (2022) Felix Nikolaus Wirth et al. BMC BIOINFORMATICS
- Swarm Learning for decentralized and confidential clinical machine learning
- (2021) Stefanie Warnat-Herresthal et al. NATURE
- Privacy preservation in federated learning: An insightful survey from the GDPR perspective
- (2021) Nguyen Truong et al. COMPUTERS & SECURITY
- Secure multiparty computation for privacy-preserving drug discovery
- (2020) Rong Ma et al. BIOINFORMATICS
- Homomorphic Encryption for Machine Learning in Medicine and Bioinformatics
- (2020) Alexander Wood et al. ACM COMPUTING SURVEYS
- Machine Learning in Drug Discovery
- (2019) Günter Klambauer et al. Journal of Chemical Information and Modeling
- An Overview of Machine Learning and Big Data for Drug Toxicity Evaluation
- (2019) Andy H. Vo et al. CHEMICAL RESEARCH IN TOXICOLOGY
- Efficient differentially private learning improves drug sensitivity prediction
- (2018) Antti Honkela et al. Biology Direct
- Secure genome-wide association analysis using multiparty computation
- (2018) Hyunghoon Cho et al. NATURE BIOTECHNOLOGY
- Realizing private and practical pharmacological collaboration
- (2018) Brian Hie et al. SCIENCE
- Secure Multi-Party Computation: Theory, practice and applications
- (2018) Chuan Zhao et al. INFORMATION SCIENCES
- Deriving genomic diagnoses without revealing patient genomes
- (2017) Karthik A. Jagadeesh et al. SCIENCE
- A network integration approach for drug-target interaction prediction and computational drug repositioning from heterogeneous information
- (2017) Yunan Luo et al. Nature Communications
- Computing arbitrary functions of encrypted data
- (2010) Craig Gentry COMMUNICATIONS OF THE ACM
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