17 research outputs found

    Linear decision fusions in multilayer perceptrons for breast cancer diagnosis

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    We introduce a non-parametric linear decision fusion called perceptron average (PA) for breast cancer diagnosis. We concretely compare the accuracy between both two fusion strategies for breast cancer diagnosis. The PA fusion demonstrates a higher overall diagnostic accuracy versus the weighted average fusion, and the PA fusion method also exhibits a better capability of generalization when a casualty of training data sizes. Moreover, the PA fusion gains a larger area covered by its receiver operating characteristic curv

    Breast cancer diagnosis using neural-based linear fusion strategies

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    Breast cancer is one of the leading causes of mortality among women, and the early diagnosis is of significant clinical importance. In this paper, we describe several linear fusion strategies, in particular the Majority Vote, Simple Average, Weighted Average, and Perceptron Average, which are used to combine a group of component multilayer perceptrons with optimal architecture for the classification of breast lesions. In our experiments, we utilize the criteria of mean squared error, absolute classification error, relative error ratio, and Receiver Operating Characteristic (ROC) curve to concretely evaluate and compare the performances of the four fusion strategies. The experimental results demonstrate that the Weighted Average and Perceptron Average strategies can achieve better diagnostic performance compared to the Majority Vote and Simple Average methods

    A bootstrap-based linear classifier fusion system for protein subcellular location prediction

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    The subcellular location plays a pivotal role in the functionality of proteins. In this paper we develop a multi-stage linear classifier fusion system based on Efron's bootstrap sampling for predicting subcellular locations of yeast proteins. Three different types of classifiers, i.e. the Naive Bayes (NB) classifier, Radial Basis Function (RBF) network, and Multilayer Perceptron (MLP), are utilized to construct the component modules in the fusion system. Ten bootstrapped instance sets are generated for training each type of component classifiers respectively. The linear fusion models, updated by the Least-Mean-Square (LMS) algorithm, are used to integrate the local decisions of the component classifiers and derive the final predictions. The empirical results show that the RBF classifiers can reach at slightly higher accuracy and better precision versus the NB or MLP ones. The linear fusion system consistently improves the overall prediction accuracy, in particular 6.65%, 1.77%, and 3.21%, superior to the NB, REF, and MLP component classifiers, respectively

    BaggingLMS: A bagging-based linear fusion with least-mean-square error update for regression

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    The merits of linear decision fusion in multiple learner systems have been widely accepted, and their practical applications are rich in literature. In this paper we present a new linear decision fusion strategy named Bagging.LMS, which takes advantage of the least-mean-square (LMS) algorithm to update the fusion parameters in the Bagging ensemble systems. In the regression experiments on four synthetic and two benchmark data sets, we compared this method with the Bagging-based Simple Average and Adaptive Mixture of Experts ensemble methods. The empirical results show that the Bagging.LMS method may significantly reduce the regression errors versus the other two types of Bagging ensembles, which indicates the superiority of the suggested Bagging.LMS method

    Breast tissue classification based on unbiased linear fusion of neural networks with normalized weighted average algorithm

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    The diagnosis of breast cancer is performed based on informed interpretation of representative histological tissue sections. Tissue distribution detected from cytologic examinations is useful for tumor staging and appropriate treatment. In this paper, we propose a normalized weighted average (Normwave) algorithm for the unbiased linear fusion, and also construct the multiple classifier system that includes a group of Radial Basis Function (RBF) neural classifiers for the classification of breast tissue samples. The empirical results show that the proposed Normwave algorithm may improve the performance of the RBF-based multiple classifier system, and also reliably outperforms some widely used fusion methods, in particular the simple average and adaptive mixture of experts

    Combining Neural-Based Regression Predictors Using an Unbiased and Normalized Linear Ensemble Model

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    In this paper, we combined a group of local regression predictors using a novel unbiased and normalized linear ensemble model (UNLEM) for the design of multiple predictor systems. In the UNLEM, the optimization of the ensemble weights is formulated equivalently to a constrained quadratic programming problem, which can be solved with the Lagrange multiplier. In our simulation experiments of data regression, the proposed multiple predictor system is composed of three different types of local regression predictors, and the effectiveness evaluation of the UNLEM was carried out on eight synthetic and four benchmark data sets. Results of the UNLEM's performance in terms of mean-squared error am significantly lower, in comparison with the popular simple average ensemble method. Moreover, the UNLEM is able to provide the regression predictions with a relatively higher normalized correlation coefficient than the results obtained with the simple average approach

    Artificial Intelligence Analysis of Gene Expression Data Predicted the Prognosis of Patients with Diffuse Large B-Cell Lymphoma

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    OBJECTIVE: We aimed to identify new biomarkers in Diffuse Large B-cell Lymphoma (DLBCL) using the deep learning technique. METHODS AND RESULTS: The multilayer perceptron (MLP) analysis was performed in the GSE10846 series, divided into discovery (n = 100) and validation (n = 414) sets. The top 25 gene-probes from a total of 54,614 were selected based on their normalized importance for outcome prediction (dead/alive). By Gene Set Enrichment Analysis (GSEA) the association to unfavorable prognosis was confirmed. In the validation set, by univariate Cox regression analysis, high expression of ARHGAP19, MESD, WDCP, DIP2A, CACNA1B, TNFAIP8, POLR3H, ENO3, SERPINB8, SZRD1, KIF23 and GGA3 associated to poor, and high SFTPC, ZSCAN12, LPXN and METTL21A to favorable outcome. A multivariate analysis confirmed MESD, TNFAIP8 and ENO3 as risk factors and ZSCAN12 and LPXN as protective factors. Using a risk score formula, the 25 genes identified two groups of patients with different survival that was independent to the cell-of-origin molecular classification (5-year OS, low vs. high risk): 65% vs. 24%, respectively (Hazard Risk = 3.2, P < 0.000001). Finally, correlation with known DLBCL markers showed that high expression of all MYC, BCL2 and ENO3 associated to the worst outcome. CONCLUSION: By artificial intelligence we identified a set of genes with prognostic relevance

    NOVEL APPLICATIONS OF MACHINE LEARNING IN BIOINFORMATICS

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    Technological advances in next-generation sequencing and biomedical imaging have led to a rapid increase in biomedical data dimension and acquisition rate, which is challenging the conventional data analysis strategies. Modern machine learning techniques promise to leverage large data sets for finding hidden patterns within them, and for making accurate predictions. This dissertation aims to design novel machine learning-based models to transform biomedical big data into valuable biological insights. The research presented in this dissertation focuses on three bioinformatics domains: splice junction classification, gene regulatory network reconstruction, and lesion detection in mammograms. A critical step in defining gene structures and mRNA transcript variants is to accurately identify splice junctions. In the first work, we built the first deep learning-based splice junction classifier, DeepSplice. It outperforms the state-of-the-art classification tools in terms of both classification accuracy and computational efficiency. To uncover transcription factors governing metabolic reprogramming in non-small-cell lung cancer patients, we developed TFmeta, a machine learning approach to reconstruct relationships between transcription factors and their target genes in the second work. Our approach achieves the best performance on benchmark data sets. In the third work, we designed deep learning-based architectures to perform lesion detection in both 2D and 3D whole mammogram images
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