稀疏PCA和叠加集合法预测ddi诱导妊娠和新生儿不良反应。

IF 1.8 Q3 MATHEMATICAL & COMPUTATIONAL BIOLOGY
Anushka Chaurasia, Deepak Kumar, Yogita
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引用次数: 0

摘要

由于数据样本有限,数据稀疏性和高维性,使用计算方法预测药物-药物相互作用(DDI)诱导的药物不良反应(adr)具有挑战性。类不平衡的问题进一步增加了预测的复杂性。已经提出了不同的计算技术来预测ddi在一般人群中引起的不良反应;然而,ddi诱导妊娠和新生儿不良反应的研究尚未充分。在本工作中,提出了一种基于稀疏集成的计算方法,该方法利用SMILES字符串作为特征,使用稀疏主成分分析(SPCA)处理高维和稀疏数据,使用多标签合成少数过采样技术(MLSMOTE)减轻类失衡,并通过堆叠集成模型预测妊娠和新生儿adr。SPCA在处理稀疏数据方面进行了评估,与PCA相比显示出2.67 %-5.45 %的改进。所提出的叠加集成模型在真阳性率(TPR)、F1分数、假阳性率(FPR)、精度、汉明损失和ROC-AUC分数的微观和宏观得分方面优于六个最先进的预测指标,得分为1.16 %-14.94 %。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Predicting DDI-induced pregnancy and neonatal ADRs using sparse PCA and stacking ensemble approach.

Predicting Drug-Drug interaction (DDI)-induced adverse drug reactions (ADRs) using computational methods is challenging due to the availability of limited data samples, data sparsity, and high dimensionality. The issue of class imbalance further increases the intricacy of prediction. Different computational techniques have been presented for predicting DDI-induced ADRs in the general population; however, the area of DDI-induced pregnancy and neonatal ADRs has been underexplored. In the present work, a sparse ensemble-based computational approach is proposed that leverages SMILES strings as features, addresses high-dimensional and sparse data using Sparse Principal Component Analysis (SPCA), mitigates class imbalance with the Multilabel Synthetic Minority Oversampling Technique (MLSMOTE), and predicts pregnancy and neonatal ADRs through a stacking ensemble model. The SPCA has been evaluated for handling sparse data and shown 2.67 %-5.45 % improvement compared to PCA. The proposed stacking ensemble model has outperformed six state-of-the-art predictors regarding micro and macro scores for True Positive Rate (TPR), F1 Score, False Positive Rate (FPR), Precision, Hamming Loss, and ROC-AUC Score with 1.16 %-14.94 %.

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来源期刊
Journal of Integrative Bioinformatics
Journal of Integrative Bioinformatics Medicine-Medicine (all)
CiteScore
3.10
自引率
5.30%
发文量
27
审稿时长
12 weeks
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