Predictive neural networks for gene expression data analysis

Tan, Ah-Hwee; Hong, Pan

doi:10.1016/j.neunet.2005.01.003

Cited by 44 publications

(16 citation statements)

References 19 publications

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“…Due to space constraints only the basic steps of the algorithms are presented below, for more details see [13], [14]. A Fuzzy ARTMAP (FAM) neural network is built of two selforganizing Fuzzy ART modules, ART a and ART b ( Figure 1), which process inputs A and targets B respectively by adapting existing or creating new prototype nodes in their second layers F 2 .…”

Section: A Fam and Arammentioning

confidence: 99%

Multi-label classification by ART-based neural networks and hierarchy extraction

Benites

Brucker

Sapozhnikova

2010

The 2010 International Joint Conference on Neural Networks (IJCNN)

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This paper presents a data mining system for multi-label classification and hierarchy extraction from the predictions provided by a multi-label classifier. Classes in multilabel classification tasks are often hierarchically organized and the hierarchy is assumed to be known. A much less investigated approach and a more challenging task, however, is to suppose that the underlying class taxonomy is unknown and that a data mining system can infer it automatically. In our setting, the proposed system is trained with multi-label data and is subsequently able to produce multi-label predictions along with hierarchical relationships between classes. The hierarchy extraction algorithm is based on building association rules from label co-occurrences. Within the framework we examine the performance of two recently introduced multi-label extensions of Adaptive Resonance Theory (ART)-based neural networks: Multi-Label Fuzzy ARTMAP (ML-FAM) and MultiLabel Fuzzy Adaptive Resonance Associative Map (ML-ARAM) in comparison with two state-of-the-art classifiers Multi-Label k-Nearest Neighbors (ML-kNN) andBoosTexter, taking into account the quality of hierarchy extraction. We also develop a novel distance measure for the quantitative evaluation of the derived class hierarchies and compare it with two other distance measures. To demonstrate the effectiveness of the proposed approach, experiments on several benchmark datasets have been performed.

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Section: A Fam and Arammentioning

confidence: 99%

Multi-label classification by ART-based neural networks and hierarchy extraction

Benites

Brucker

Sapozhnikova

2010

The 2010 International Joint Conference on Neural Networks (IJCNN)

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“…The network input and output data were obtained from literature 1,2,[4][5][6][7][8][9][10][11][12][14][15][16][17][18][19][20][21][22][23][24][25]27,[29][30][31][32][33][34][35][36][37][38][39][40][41][42][43][44][46][47][48][49] for evaluation and prediction. Altogether 341 data were collected and summarized in Table 1.…”

Section: Network Input and Outputmentioning

confidence: 99%

“…Over the last few years, neural networks has been successfully applied in biology, microbiology, medicine, image manipulation and voice discrimination etc. 4,5,10,14,24,31,38,41,42,46 Therefore the neural network model may be a reasonable tool to setup the relationship between the expansion fold of HSCs and the influencing factors.…”

Section: Introductionmentioning

confidence: 99%

Neural Network Analysis of Ex-vivo Expansion of Hematopoietic Stem Cells

Fan

Liu

et al. 2007

Ann Biomed Eng

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The shortage of hematopoietic stem cells (HSCs) greatly limits their widespread clinical applications. Few studies however, investigated the relationship between the cellular expansion and the influencing factors although wide variety results of the ex-vivo expansion of HSCs existed in literature. Here, a back-propagation (BP) neural network model was employed to evaluate the ex-vivo expansions of nuclear cells (NCs), CD34(+) cells, and colony-forming units (CFU-Cs), where the output was the cellular expansion folds and the inputs include inoculated density, cytokines, resources, serum, stroma, culture time, and bioreactor types. Around 124, 86, and 90 samples were used to train the neural network for the expansion evaluations of NCs, CD34(+ )cells, and CFU-Cs, respectively, while 17, 14, and 10 samples were applied to predict respectively. The results show that for the training of network, the interval accuracy of the expansion folds for the different cells is 85.5, 86.1, and 86.7%, respectively, while the truth-value accuracy is still up to 59.7, 50.0, and 62.2%, respectively within a relative error (RE) of +/-20%. For the prediction of network, the interval accuracy can be up to 82.4, 71.4, and 70%, respectively, while the truth-value accuracy is only 29.4, 14.3, and 50.0%, respectively (RE = +/-20%). Moreover, six verification experiments were carried out based on our interval predicted values and the results proved that the five group predicted conditions lead to the correct expansion of the HSCs with the accuracy more than 80%. Considering the complexity of HSC expansion and complicated wide range of the experimental data, such relatively high interval accuracy for training and prediction as well as verification are satisfied. Therefore this nonlinear modeling makes it possible to describe quantitatively the effects of the culture conditions on the HSC expansion and to predict the optimal culture conditions for higher ex-vivo expansion of HSCs.

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“…produced by modern technology tools make classification harder than ever. How to display structures of high-dimensional data set and how to classify samples are difficult tasks that need be solved urgently [4].…”

Section: Introductionmentioning

confidence: 99%

A New Method for Dimensionality Reduction based on Multivariate Feature Fusion

Liu

Hui

Wang

et al. 2007

2007 IEEE International Conference on Integration Technology

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Dimensionality reduction is the process of mapping high-dimension patterns to a lower dimension subspace. When done prior to classification, estimates obtained in the lower dimension subspace are more reliable. We propose a novel method based on graphical multivariate feature fusion and use it to offer a visual representation of high dimensional data. The graphical processing method we propose, relies on using a multilayered structure of feature fusion which produces as output of the lower dimensional representation. We implement feature fusion by combining method of feature selection and feature extraction. Experiments on the data set of machine learning database indicate the novel method we propose provides better representation than Fisher's linear discriminant(FLD) and some other nonlinear methods of dimensionality reduction that are often used.

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Predictive neural networks for gene expression data analysis

Cited by 44 publications

References 19 publications

Multi-label classification by ART-based neural networks and hierarchy extraction

Multi-label classification by ART-based neural networks and hierarchy extraction

Neural Network Analysis of Ex-vivo Expansion of Hematopoietic Stem Cells

A New Method for Dimensionality Reduction based on Multivariate Feature Fusion

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