OmicPredict:一个使用ANOVA-Firefly算法进行特征选择的omics数据预测的框架
Parampreet Kaur1, Ashima Singh1, Inderveer Chana1
1Computer Science and Engineering Department, Thapar Institute of Engineering and Technology, Patiala, India.
Computer methods in biomechanics and biomedical engineering
|October 16, 2023
概括
OmicPredict使用机器学习和omics数据准确预测阿尔茨海默病,乳腺癌和COVID-19等疾病. 这个框架通过先进的深度神经网络来增强早期疾病检测和个性化医疗.
科学领域:
- 计算生物学是一种计算生物学.
- 生物信息学是一种生物信息学.
- 机器学习在医疗保健中的应用
背景情况:
- 高通量omics数据分析对于理解复杂疾病至关重要.
- 机器学习模型越来越多地用于早期疾病预测.
- 从OMIC数据开发强大的多种疾病预测模型仍然是一个挑战.
研究的目的:
- 提出和验证一个新的框架",OmicPredict",用于使用omics数据预测多种疾病.
- 将混合特征选择方法与深度神经网络 (DNN) 集成,以提高预测准确度.
- 为了证明该框架在包括阿尔茨海默氏症,乳腺癌和COVID-19在内的各种疾病中的有效性.
主要方法:
- 开发了一种混合特征选择方法,将差异分析 (ANOVA) 和火虫算法结合起来.
- 在OmicPredict框架内使用深度神经网络 (DNN) 模型.
- 将框架应用于三个不同的奥米克数据集:GSE33000/GSE44770 (阿尔茨海默氏症),METABRIC (乳腺癌 HER2+) 和GSE157103 (COVID-19).
主要成果:
- DNN模型实现了0.949的高曲线下面积 (AUC),用于阿尔茨海默病的预测.
- 该框架在乳腺癌 (AUC=0.987) 和COVID-19 (AUC=0.989) 中表现出色.
- OmicPredict的表现优于已有的模型,如随机森林和天真贝叶斯,以及现有的研究基准.
结论:
- OmicPredict框架提供了一种有效的方法,用于使用omics数据进行多种疾病的预测.
- 混合特征选择和DNN模型组合显著提高了预测准确性.
- 这项工作有助于推进早期疾病检测和个性化治疗策略.
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