A Discriminative Feature Extraction Approach for Tumor Classification Using Gene Expression Data

Mei, Qinglin; Zhang, Huaxiang; Cheng, Lijuan

doi:10.2174/1574893611666160728114747

Cited by 20 publications

(13 citation statements)

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“…Meanwhile, the permutation of the EEG signals will be taken into consideration. It is notable that the proposed approach relates to the following areas including network security [44]- [58], optimization [59]- [65], graphics [66]- [71], recommendation system [72]- [74], multimedia [75]- [82], automation control [83]- [87], networking [88]- [101], classification [102]- [106], image fusion [107], [108], image retrieval [109]- [114], and simulation [115]- [117]. WEIKUAN JIA is currently an Associate Professor with Shandong Normal University.…”

Section: Discussionmentioning

confidence: 99%

Pair-Wise Matching of EEG Signals for Epileptic Identification via Convolutional Neural Network

et al. 2020

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Electroencephalogram (EEG) have been extensively analyzed to identify the characteristics of epileptic seizures in the literature. However, most of these studies focus on the properties of single channel EEG data while neglecting the association between signals from diverse channels. To bridge this gap, we propose an EEG instance matching-based epilepsy classification approach by introducing one convolutional neural network (CNN). First of all, each pair of EEG signals are exploited to form one 2 dimensional matrix, which could be used to reveal the interaction between them. Secondly, the generated matrices are fed into the proposed CNN that would discriminate the input representations. To evaluate the performance of the presented approach, the comparison experiments between the state-of-the-art techniques and our work are conducted on publicly available epilepsy EEG benchmark database. Experimental results indicate that the proposed algorithm could yield the performance with an average accuracy of 99.3%, average sensitivity of 99.5%, and average specificity 99.6%.

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Section: Discussionmentioning

confidence: 99%

Pair-Wise Matching of EEG Signals for Epileptic Identification via Convolutional Neural Network

et al. 2020

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“…Mei, Zhang and Liang [12], suggested a dimensionality reduction method for classifying tumor gene expression data. They used a discriminant hybrid structure to enhance data separability and improving tumor classification accuracy.…”

Section: Related Workmentioning

confidence: 99%

A Hybrid Heuristic Dimensionality Reduction Methods for Classifying Malaria Vector Gene Expression Data

Arowolo

Adebiyi

et al. 2020

IEEE Access

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Malaria is the world's leading cause of death, spread by Anopheles mosquitoes. Gene expression is a fundamental level where the effects of unseen vital revealing genes and developmental systems can be evident for detection of distinctions in malaria infections, to recognize the biological processes in human. Ribonucleic acid sequencing offers a large-scale measurable generated profiling transcriptional data results that help a variety of applications such as scientific and clinical condition studies. A fundamental limitation of ribonucleic acid sequencing consists of high dimensional, infrequent and noises, making classification of genes challenging. Several approaches have proposed enhancing the problem of the curse of dimensionality problem, requiring more improvement, yet it is critical to obtain accurate results. In this study, a hybrid dimensionality reduction technique proposes an optimized Genetic algorithm to pick pertinent subset features from the data. Features chosen is passed into principal component analysis and independent component analysis methods grounded on their class variants, to help transform the selected elements into a lower dimension separately. Support vector machine kernel classifiers used the reduced malaria vector dataset to assess the classification performance of the experiment.

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“…The process involves obtaining the standard atlas of positive drugs and the atlas of prepared identification (query) drugs, and then analyzing and comparing the differences between the two. To obtain the two maps, one must first extract DNA from the corresponding samples, and then let them hybridize with DNA chips [ 24 , 25 , 26 , 27 ]. The detection steps of gene chip technology are shown in Figure 1 .…”

Section: Biochip Technologymentioning

confidence: 99%

Application of Molecular Methods in the Identification of Ingredients in Chinese Herbal Medicines

et al. 2018

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There are several kinds of Chinese herbal medicines originating from diverse sources. However, the rapid taxonomic identification of large quantities of Chinese herbal medicines is difficult using traditional methods, and the process of identification itself is prone to error. Therefore, the traditional methods of Chinese herbal medicine identification must meet higher standards of accuracy. With the rapid development of bioinformatics, methods relying on bioinformatics strategies offer advantages with respect to the speed and accuracy of the identification of Chinese herbal medicine ingredients. This article reviews the applicability and limitations of biochip and DNA barcoding technology in the identification of Chinese herbal medicines. Furthermore, the future development of the two technologies of interest is discussed.

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A Discriminative Feature Extraction Approach for Tumor Classification Using Gene Expression Data

Cited by 20 publications

References 0 publications

Pair-Wise Matching of EEG Signals for Epileptic Identification via Convolutional Neural Network

Pair-Wise Matching of EEG Signals for Epileptic Identification via Convolutional Neural Network

A Hybrid Heuristic Dimensionality Reduction Methods for Classifying Malaria Vector Gene Expression Data

Application of Molecular Methods in the Identification of Ingredients in Chinese Herbal Medicines

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