J Biomed Inform - A novel artificial neural network method for biomedical prediction based on matrix pseudo-inversion.

Tópicos

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{ featur(3375) classif(2383) classifi(1994) }
{ learn(2355) train(1041) set(1003) }
{ method(1557) propos(1049) approach(1037) }
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{ method(984) reconstruct(947) comput(926) }
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{ imag(2830) propos(1344) filter(1198) }
{ problem(2511) optim(1539) algorithm(950) }
{ patient(1821) servic(1111) care(1106) }
{ process(1125) use(805) approach(778) }
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Resumo

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.

Resumo Limpo

biomed predict base clinic genomewid data becom increas import diseas diagnosi classif solv predict problem effect manner improv clinic care develop novel artifici neural network ann method base matrix pseudoinvers mpi use biomed applic mpiann construct threelay ie input hidden output layer feedforward neural network weight connect hidden output layer direct determin base mpi without lengthi learn iter lasso least absolut shrinkag select oper method also present compar purpos singl nucleotid polymorph snp simul data real breast cancer data employ valid perform mpiann method via fold cross valid experiment result demonstr efficaci develop mpiann diseas classif predict view signific superior accuraci ie rate correct predict compar lasso result base real breast cancer data also show mpiann better perform machin learn method includ support vector machin svm logist regress lr iter ann addit experi demonstr mpiann use biomark select well

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