利用多视角数据预测药物副作用发生频率的邻域正则化方法。

IF 4.4 3区 生物学 Q1 BIOCHEMICAL RESEARCH METHODS Bioinformatics Pub Date : 2023-09-02 DOI:10.1093/bioinformatics/btad532
Lin Wang, Chenhao Sun, Xianyu Xu, Jia Li, Wenjuan Zhang
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引用次数: 1

摘要

动机:药物获益-风险评估的一个关键问题是确定副作用的频率,这是通过随机对照试验进行的。计算预测药物副作用的频率可以有效地指导随机对照试验。然而,预测药物副作用频率更具挑战性,因此只有少数研究涉及这一问题。在这项工作中,我们提出了一种邻域正则化方法(NRFSE),该方法利用药物和副作用的多视图数据来预测副作用的频率。首先,我们采用类加权非负矩阵分解法分解毒副作用频率矩阵,其中使用高斯似然对未知毒副作用对建模。其次,我们设计了一个多视图邻域正则化,分别整合三个药物属性和两个副作用属性,使得大多数相似的药物和大多数相似的副作用具有相似的潜在特征。正则化可以自适应地确定不同属性的权重。我们在一个基准数据集上进行了广泛的实验,与五种最先进的方法相比,NRFSE提高了预测性能。上市后副作用的独立测试集进一步验证了NRFSE的有效性。可用性和实现:源代码和数据集可从https://github.com/linwang1982/NRFSE或https://codeocean.com/capsule/4741497/tree/v1获得。
本文章由计算机程序翻译,如有差异,请以英文原文为准。

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A neighborhood-regularization method leveraging multiview data for predicting the frequency of drug-side effects.

Motivation: A critical issue in drug benefit-risk assessment is to determine the frequency of side effects, which is performed by randomized controlled trails. Computationally predicted frequencies of drug side effects can be used to effectively guide the randomized controlled trails. However, it is more challenging to predict drug side effect frequencies, and thus only a few studies cope with this problem.

Results: In this work, we propose a neighborhood-regularization method (NRFSE) that leverages multiview data on drugs and side effects to predict the frequency of side effects. First, we adopt a class-weighted non-negative matrix factorization to decompose the drug-side effect frequency matrix, in which Gaussian likelihood is used to model unknown drug-side effect pairs. Second, we design a multiview neighborhood regularization to integrate three drug attributes and two side effect attributes, respectively, which makes most similar drugs and most similar side effects have similar latent signatures. The regularization can adaptively determine the weights of different attributes. We conduct extensive experiments on one benchmark dataset, and NRFSE improves the prediction performance compared with five state-of-the-art approaches. Independent test set of post-marketing side effects further validate the effectiveness of NRFSE.

Availability and implementation: Source code and datasets are available at https://github.com/linwang1982/NRFSE or https://codeocean.com/capsule/4741497/tree/v1.

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来源期刊
Bioinformatics
Bioinformatics 生物-生化研究方法
CiteScore
11.20
自引率
5.20%
发文量
753
审稿时长
2.1 months
期刊介绍: The leading journal in its field, Bioinformatics publishes the highest quality scientific papers and review articles of interest to academic and industrial researchers. Its main focus is on new developments in genome bioinformatics and computational biology. Two distinct sections within the journal - Discovery Notes and Application Notes- focus on shorter papers; the former reporting biologically interesting discoveries using computational methods, the latter exploring the applications used for experiments.
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