title: A novel artificial neural network method for biomedical prediction based on matrix pseudo-inversion.
abstract: 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.
authors.affiliation: Department of Biomedical Informatics, School of Medicine, University of Pittsburgh, Pittsburgh, PA 15206-3701, USA. Electronic address: bic9@pitt.edu.
authors.forename: Binghuang
authors.initials: B
authors.lastname: Cai
authors.affiliation: Department of Biomedical Informatics, School of Medicine, University of Pittsburgh, Pittsburgh, PA 15206-3701, USA. Electronic address: xij6@pitt.edu.
authors.forename: Xia
authors.initials: X
authors.lastname: Jiang
mesh_terms: D000465:Algorithms; D001185:Artificial Intelligence; D001943:Breast Neoplasms / Q000175:diagnosis / Q000235:genetics; D005260:Female; D023281:Genomics; D006801:Humans; D008490:Medical Informatics / Q000379:methods; D015233:Models, Statistical; D009369:Neoplasms / Q000175:diagnosis* / Q000235:genetics; D016571:Neural Networks, Computer; D020641:Polymorphism, Single Nucleotide; D012044:Regression Analysis; D015203:Reproducibility of Results; D060388:Support Vector Machine
keywords: Biomedical prediction and classification; Cancer; Least Absolute Shrinkage and Selection Operator (LASSO); Matrix pseudo-inversion; Neural networks; Single Nucleotide Polymorphism (SNP)