Predicting Antimicrobial Activity for Untested Peptide-Based Drugs Using Collaborative Filtering and Link Prediction
Predicting Antimicrobial Activity for Untested Peptide-Based Drugs Using Collaborative Filtering and Link Prediction
复制标题
使用协作过滤和链接预测预测未经测试的基于肽的药物的抗菌活性
DOI：
10.1021/acs.jcim.3c00137
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发表时间：
2023
影响因子：
5.6
通讯作者：
Kolomeisky, Anatoly B.
中科院分区：
文献类型：
--
作者：
Medvedeva, Angela;Teimouri, Hamid;Kolomeisky, Anatoly B.
细菌对现有抗生素耐药性的增加凸显了开发新的抗生素药物的迫切需要。抗菌肽（AMPs），单独或与其他肽和/或现有抗生素的组合，已成为这项任务的有希望的候选者。然而，由于已知的amp有数千种，而且可以合成的amp数量甚至更多，因此不可能使用标准的湿实验室实验方法对所有amp进行全面测试。这些观察结果激发了机器学习方法的应用，以识别有前途的amp。目前，机器学习研究结合了非常不同的细菌，而不考虑细菌的特定特征或与amp的相互作用。此外，当前AMP数据集的稀疏性使传统机器学习方法的应用不合格或使结果不可靠。在这里，我们提出了一种新的方法，以基于邻域的协同过滤为特征，基于细菌反应之间的相似性，高精度地预测给定细菌对未经测试的amp的反应。此外，我们还开发了一种互补的细菌特异性链接预测方法，可用于可视化amp -抗生素组合网络，使我们能够提出可能有效的新组合。
The increase of bacterial resistance to currently available antibiotics has underlined the urgent need to develop new antibiotic drugs. Antimicrobial peptides (AMPs), alone or in combination with other peptides and/or existing antibiotics, have emerged as promising candidates for this task. However, given that there are thousands of known AMPs and an even larger number can be synthesized, it is impossible to comprehensively test all of them using standard wet lab experimental methods. These observations stimulated an application of machine-learning methods to identify promising AMPs. Currently, machine learning studies combine very different bacteria without considering bacteria-specific features or interactions with AMPs. In addition, the sparsity of current AMP data sets disqualifies the application of traditional machine-learning methods or makes the results unreliable. Here, we present a new approach, featuring neighborhood-based collaborative filtering, to predict with high accuracy a given bacteria’s response to untested AMPs based on similarities between bacterial responses. Furthermore, we also developed a complementary bacteria-specific link prediction approach that can be used to visualize networks of AMP-antibiotic combinations, enabling us to propose new combinations that are likely to be effective.
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影响因子：
4.3
作者：
Soltaninejad H;Zare-Zardini H;Ordooei M;Ghelmani Y;Ghadiri-Anari A;Mojahedi S;Hamidieh AA
通讯作者：
Hamidieh AA
基于用户和基于项目的协同过滤的结合
影响因子：
1.8
作者：
M. Vozalis;K. Margaritis
通讯作者：
K. Margaritis
影响因子：
5.2
作者：
Zhao YH;Shaw JG
通讯作者：
Shaw JG
DOI：
10.1080/10934520701517689
发表时间：
2007-01-01
影响因子：
2.1
作者：
Silvestry-Rodriguez, Nadia;Bright, Kelly R.;Gerba, Charles P.
通讯作者：
Gerba, Charles P.
影响因子：
5.6
作者：
Vishnepolsky, Boris;Gabrielian, Andrei;Pirtskhalava, Malak
通讯作者：
Pirtskhalava, Malak