NETWORK MODELING FOR DRUG TOXICITY PREDICTION

• 1. 基于胶囊网络的药物分子药代动力学性质和毒性预测方法 [P] . 中国专利： CN109979541B . 2021.06.22
• 2. 基于胶囊网络的药物分子药代动力学性质和毒性预测方法 [P] . 中国专利： CN109979541A . 2019-07-05
• 3. Microarray sampling and network modeling for drug toxicity prediction [P] . 欧洲知识产权局专利： EP2600269A3 . 2013-12-04

  机译： 用于药物毒性预测的微阵列采样和网络建模

• 4. Microarray sampling and network modeling for drug toxicity prediction [P] . 欧洲知识产权局专利： EP2600269A2 . 2013-06-05

  机译： 用于药物毒性预测的微阵列采样和网络建模

• 5. NETWORK MODELING FOR DRUG TOXICITY PREDICTION [P] . 美国专利： US2013144584A1 . 2013-06-06

  机译： 药物毒性预测的网络建模

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• 3. DTHMM based delay modeling and prediction for networked control systems [J] . Shuang Cong, Yuan Ge, Qigong Chen, 系统工程与电子技术（英文版） . 2010,第006期
• 4. Hepatoprotective potential of ethanolic extract ofZiziphus oenoplia (L.) Mill roots against antitubercular drugs induced hepatotoxicity in experimental models [J] . Ch V Rao, AKS Rawat, Anil P Singh, 亚太热带医药杂志（英文版） . 2012,第004期
• 5. Physiologically‐Based Pharmacokinetic Models for CYP1A2 Drug–Drug Interaction Prediction: A Modeling Network of Fluvoxamine, Theophylline, Caffeine, Rifampicin, and Midazolam [J] . Hannah Britz, Nina Hanke, Anke‐Katrin Volz, CPT: Pharmacometrics & Systems Pharmacology . 2019,第5期
• 6. Physiologically Based Pharmacokinetic Models for Prediction of Complex CYP2C8 and OATP1B1 (SLCO1B1) Drug-Drug-Gene Interactions: A Modeling Network of Gemfibrozil, Repaglinide, Pioglitazone, Rifampicin, Clarithromycin and Itraconazole [J] . Tuerk Denise, Hanke Nina, Wolf Sarah, Clinical pharmacokinetics . 2019,第12期
• 7. Counter-propagation artificial neural network models in read-across predictions of toxicity [J] . Drgan Viktor, Zuperl Spela, Venko Katja, Toxicology Letters: An International Journal Providing a Forum for Original and Pertinent Contributions in Toxicology Research . 2017,第S1期
• 8. Drug-Target Interaction Network Predictions for Drug Repurposing Using LASSO-Based Regularized Linear Classification Model [C] . Jiaying You, Md. Mohaiminul Islam, Liam Grenier, Canadian conference on artificial intelligence . 2018
• 9. The Era of Precision Medicine: Individual Specific Predictions of Targeted Cancer Therapeutics Induced Cardiotoxicity Using Transcriptomics based Mathematical Modeling [D] . Shim, Jaehee . 2019
• 10. Beyond adverse outcome pathways: making toxicity predictions from event networks SAR models data and knowledge [O] . Thomas Ball, Christopher G Barber, Alex Cayley, 2021
• 11. Modeling and prediction of cytotoxicity of artemisinin for treatment of the breast cancer by using artificial neural networks [O] . Abdolhossein Qaderi, Neda Dadgar, Hamidreza Mansouri, 2013

• 1. Prediction of Enthalpies of Fusion for Divalent Rare Earth Halides Based on Modeling by Artificial Neural Networks and Pattern Recognition [J] . Yimin Sun ,Zhiyu Qiao ,Minghong He(Applied Science School University of Science Technology Beijing Beijing 100083 China)(National Natural Science Foundation of China Beijing 100083 China) . 矿物冶金与材料学报：英文版 . 1999,第1期
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• 3. 基于机器学习预测药物诱导的肝毒性 [A] . 吴慧辰 . 2019