图 书 馆
使用贝叶斯矩阵分解来预测来自化学和基因组核的药物 - 靶标相互作用Predicting drug-target interactions from chemical and genomic kernels using Bayesian matrix factorization |
|
| 课程网址: | http://videolectures.net/mlsb2012_gonen_target/ |
| 主讲教师: | Mehmet Gönen |
| 开课单位: | 阿尔托大学 |
| 开课时间: | 2012-10-23 |
| 课程语种: | 英语 |
| 中文简介: |  |
| 课程简介: | **Motivation:** Identifying interactions between drug compounds and target proteins has a great practical importance in the drug discovery process for known diseases. Existing databases contain very few experimentally validated drug-target interactions and formulating successful computational methods for predicting interactions remains challenging.\\ **Results:** In this study, we consider four different drug-target interaction networks from humans involving enzymes, ion channels, G-protein-coupled receptors, and nuclear receptors. We then propose a novel Bayesian formulation that combines dimensionality reduction, matrix factorization, and binary classification for predicting drug- target interaction networks using only chemical similarity between drug compounds and genomic similarity between target proteins. The novelty of our approach comes from the joint Bayesian formulation of projecting drug compounds and target proteins into a unified subspace using the similarities and estimating the interaction network in that subspace. We propose using a variational approximation in order to obtain an efficient inference scheme and give its detailed derivations. Lastly, we demonstrate the performance of our proposed method in three different scenarios: (a) exploratory data analysis using low-dimensional projections, (b) predicting interactions for the out-of-sample drug compounds, and (c) predicting unknown interactions of the given network.\\ |
| 关 键 词: | 预测交互; 靶标相互作用; 贝叶斯公式 |
| 课程来源: | 视频讲座网 |
| 最后编审: | 2021-12-22：liyy |
| 阅读次数: | 93 |