A novel artificial neural network method for biomedical prediction based on matrix pseudo-inversion.
A novel artificial neural network method for biomedical prediction based on matrix pseudo-inversion.
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DOI：
10.1016/j.jbi.2013.12.009
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发表时间：
2014-04
影响因子：
4.5
通讯作者：
Jiang, Xia
中科院分区：
文献类型：
--
作者：
Cai, Binghuang;Jiang, Xia
关键词：
基于临床和全基因组数据的生物医学预测在疾病诊断和分类中变得越来越重要。为了有效地解决预测问题，以改善临床护理，我们开发了一种新的人工神经网络（ANN）方法的基础上矩阵伪逆（MPI）用于生物医学应用。MPI-ANN被构造为三层（即，输入层、隐层和输出层）前馈神经网络，并且连接隐层和输出层的权重基于MPI直接确定，而无需冗长的学习迭代。LASSO（最小绝对收缩和选择算子）的方法也提出了比较的目的。单核苷酸多态性（SNP）模拟数据和真实的乳腺癌数据，通过5倍交叉验证的MPI-ANN方法的性能进行验证。实验结果表明，鉴于显着优越的上级准确性（即，正确的预测率），与LASSO相比。基于真实的乳腺癌数据的结果也表明，MPI-ANN具有更好的性能比其他机器学习方法（包括支持向量机（SVM），逻辑回归（LR），和迭代ANN）。此外，实验表明，我们的MPI-ANN可以用于生物标记的选择以及。
Biomedical prediction based on clinical and genome-wide data has become increasingly important in disease diagnosis and classification. To solve the prediction problem in an effective manner for the improvement of clinical care, we develop a novel Artificial Neural Network (ANN) method based on Matrix Pseudo-Inversion (MPI) for use in biomedical applications. The MPI-ANN is constructed as a three-layer (i.e., input, hidden, and output layers) feed-forward neural network, and the weights connecting the hidden and output layers are directly determined based on MPI without a lengthy learning iteration. The LASSO (Least Absolute Shrinkage and Selection Operator) method is also presented for comparative purposes. Single Nucleotide Polymorphism (SNP) simulated data and real breast cancer data are employed to validate the performance of the MPI-ANN method via 5-fold cross validation. Experimental results demonstrate the efficacy of the developed MPI-ANN for disease classification and prediction, in view of the significantly superior accuracy (i.e., the rate of correct predictions), as compared with LASSO. The results based on the real breast cancer data also show that the MPI-ANN has better performance than other machine learning methods (including support vector machine (SVM), logistic regression (LR), and an iterative ANN). In addition, experiments demonstrate that our MPI-ANN could be used for bio-marker selection as well.
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