Towards the network-based prediction of repurposed drugs using patient-specific metabolic models.
作者信息
Pacheco Maria Pires, Bintener Tamara, Sauter Thomas
机构信息
Life Sciences Research Unit, University of Luxembourg, Esch-Alzette, Luxembourg; Ludwig-Maximilians-Universität München, Department Biology I, Plant Evolutionary Cell Biology, Planegg-Martinsried, Germany.
Life Sciences Research Unit, University of Luxembourg, Esch-Alzette, Luxembourg.
出版信息
EBioMedicine. 2019 May;43:26-27. doi: 10.1016/j.ebiom.2019.04.017. Epub 2019 Apr 9.
Abstract
摘要
相似文献
1
Towards the network-based prediction of repurposed drugs using patient-specific metabolic models.基于患者特异性代谢模型的药物重新利用的网络预测研究
EBioMedicine. 2019 May;43:26-27. doi: 10.1016/j.ebiom.2019.04.017. Epub 2019 Apr 9.
2
Recommendation Techniques for Drug-Target Interaction Prediction and Drug Repositioning.药物-靶点相互作用预测与药物重新定位的推荐技术
Methods Mol Biol. 2016;1415:441-62. doi: 10.1007/978-1-4939-3572-7_23.
3
Network-Based Drug Repositioning: Approaches, Resources, and Research Directions.基于网络的药物重新定位：方法、资源及研究方向。
Methods Mol Biol. 2019;1903:97-113. doi: 10.1007/978-1-4939-8955-3_6.
4
Computational drug repositioning using low-rank matrix approximation and randomized algorithms.基于低秩矩阵逼近和随机算法的计算药物重定位。
Bioinformatics. 2018 Jun 1;34(11):1904-1912. doi: 10.1093/bioinformatics/bty013.
5
Computational biology: future challenges for the patenting of repurposed drugs.计算生物学：药物重新利用专利面临的未来挑战。
Pharm Pat Anal. 2017 Sep;6(5):201-203. doi: 10.4155/ppa-2017-0018. Epub 2017 Aug 18.
6
Drug repositioning based on comprehensive similarity measures and Bi-Random walk algorithm.基于综合相似性度量和双向随机游走算法的药物重新定位
Bioinformatics. 2016 Sep 1;32(17):2664-71. doi: 10.1093/bioinformatics/btw228. Epub 2016 May 5.
7
An Application of Computational Drug Repurposing Based on Transcriptomic Signatures.基于转录组特征的计算药物再利用应用
Methods Mol Biol. 2019;1903:149-177. doi: 10.1007/978-1-4939-8955-3_9.
8
MD-Miner: a network-based approach for personalized drug repositioning.MD-Miner：一种基于网络的个性化药物重新定位方法。
BMC Syst Biol. 2017 Oct 3;11(Suppl 5):86. doi: 10.1186/s12918-017-0462-9.
9
Prediction of drugs having opposite effects on disease genes in a directed network.在有向网络中对疾病基因具有相反作用的药物预测。
BMC Syst Biol. 2016 Jan 11;10 Suppl 1(Suppl 1):2. doi: 10.1186/s12918-015-0243-2.
10
Computational Drug Repositioning: A Lateral Approach to Traditional Drug Discovery?计算药物重新定位：传统药物发现的一种横向方法？
Curr Top Med Chem. 2016;16(19):2069-77. doi: 10.2174/1568026616666160216153249.
引用本文的文献
1
A Practical Guide to Integrating Multimodal Machine Learning and Metabolic Modeling.多模态机器学习与代谢建模整合实用指南
Methods Mol Biol. 2022;2399:87-122. doi: 10.1007/978-1-0716-1831-8_5.
2
On the Use of Topological Features of Metabolic Networks for the Classification of Cancer Samples.关于利用代谢网络的拓扑特征对癌症样本进行分类
Curr Genomics. 2021 Feb;22(2):88-97. doi: 10.2174/1389202922666210301084151.
3
A mechanism-aware and multiomic machine-learning pipeline characterizes yeast cell growth.一种基于机制感知和多组学机器学习的管道可对酵母细胞生长进行特征描述。
Proc Natl Acad Sci U S A. 2020 Aug 4;117(31):18869-18879. doi: 10.1073/pnas.2002959117. Epub 2020 Jul 16.
4
Towards the routine use of in silico screenings for drug discovery using metabolic modelling.迈向使用代谢建模进行药物发现的计算机筛选的常规应用。
Biochem Soc Trans. 2020 Jun 30;48(3):955-969. doi: 10.1042/BST20190867.
本文引用的文献
1
Discovery of therapeutic agents for prostate cancer using genome-scale metabolic modeling and drug repositioning.利用基因组规模代谢建模和药物重定位发现治疗前列腺癌的药物。
EBioMedicine. 2019 Apr;42:386-396. doi: 10.1016/j.ebiom.2019.03.009. Epub 2019 Mar 21.
2
Recon 2.2: from reconstruction to model of human metabolism.Recon 2.2：从重建到人类代谢模型
Metabolomics. 2016;12:109. doi: 10.1007/s11306-016-1051-4. Epub 2016 Jun 7.
3
Metabolic Network Prediction of Drug Side Effects.药物副作用的代谢网络预测。
Cell Syst. 2016 Mar 23;2(3):209-13. doi: 10.1016/j.cels.2016.03.001.
4
Benchmarking Procedures for High-Throughput Context Specific Reconstruction Algorithms.高通量特定上下文重建算法的基准测试程序
Front Physiol. 2016 Jan 22;6:410. doi: 10.3389/fphys.2015.00410. eCollection 2015.
5
Towards improved genome-scale metabolic network reconstructions: unification, transcript specificity and beyond.迈向改进的基因组规模代谢网络重建：统一、转录特异性及其他。
Brief Bioinform. 2016 Nov;17(6):1060-1069. doi: 10.1093/bib/bbv100. Epub 2015 Nov 28.
6
Integrated metabolic modelling reveals cell-type specific epigenetic control points of the macrophage metabolic network.整合代谢模型揭示了巨噬细胞代谢网络中细胞类型特异性的表观遗传控制点。
BMC Genomics. 2015 Oct 19;16:809. doi: 10.1186/s12864-015-1984-4.
7
Phenotype-based cell-specific metabolic modeling reveals metabolic liabilities of cancer.基于表型的细胞特异性代谢建模揭示了癌症的代谢缺陷。
Elife. 2014 Nov 21;3:e03641. doi: 10.7554/eLife.03641.
8
Identification of anticancer drugs for hepatocellular carcinoma through personalized genome-scale metabolic modeling.通过个性化全基因组代谢建模鉴定肝癌的抗癌药物。
Mol Syst Biol. 2014 Mar 19;10(3):721. doi: 10.1002/msb.145122.
9
Fast reconstruction of compact context-specific metabolic network models.紧凑的特定上下文代谢网络模型的快速重建。
PLoS Comput Biol. 2014 Jan;10(1):e1003424. doi: 10.1371/journal.pcbi.1003424. Epub 2014 Jan 16.
10
Predicting selective drug targets in cancer through metabolic networks.通过代谢网络预测癌症中的选择性药物靶标。
Mol Syst Biol. 2011 Jun 21;7:501. doi: 10.1038/msb.2011.35.
Zotero 插件