Supervised Discriminative Sparse PCA for Com-Characteristic Gene Selection and Tumor Classification on Multiview Biological Data

Feng, Chun-Mei; Xu, Yong; Liu, Jin-Xing; Gao, Ying-Lian; Zheng, Chun-Hou

doi:10.1109/tnnls.2019.2893190

Cited by 50 publications

(43 citation statements)

References 68 publications

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“…In this paper, the MCBFS method was tested on eight benchmark tumor data sets and compared with seven benchmark supervised feature selection methods [ 34 ], including Chi Square, Fisher Score, Information Gain, mRMR, Gini Index, Kruskal Wallis and Relief-F. In addition, to further evaluate the performance of MCBFS, we compared it with six state-of-the-art supervised feature selection methods, including supervised discriminative sparse PCA (SDSPCA) [ 2 ], infinite latent feature selection (ILFS) [ 14 ], Double Kernel-Based Clustering method for Gene Selection (DKBCGS) [ 3 ], Infinite Feature Selection (infFS) [ 6 ], Supervised Multi-Cluster Feature Selection (SMCFS) [ 9 ] and Spectral Feature Selection (SPEC) [ 10 ].…”

Section: Resultsmentioning

confidence: 99%

“…However, only a small subset of genes is suitable for tumor classification. To address these issues, some feature selection algorithms have recently been developed for identifying informative genes from genomic data of cancer [ 2 – 5 ].…”

Section: Introductionmentioning

confidence: 99%

“…Based on the availability of labels in data, feature selection methods can be broadly categorized into unsupervised and supervised [ 9 ]. For example, Feng et al recently developed a new supervised discriminative sparse PCA (SDSPCA) method for multiview biological data, which has been applied to cancer classification and informative gene selection [ 2 ]. Zhao et al presented supervised and unsupervised spectral feature selection methods for handling high-dimensional data [ 10 ].…”

Section: Introductionmentioning

confidence: 99%

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Multi-scale supervised clustering-based feature selection for tumor classification and identification of biomarkers and targets on genomic data

Zhang

et al. 2020

BMC Genomics

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Background The small number of samples and the curse of dimensionality hamper the better application of deep learning techniques for disease classification. Additionally, the performance of clustering-based feature selection algorithms is still far from being satisfactory due to their limitation in using unsupervised learning methods. To enhance interpretability and overcome this problem, we developed a novel feature selection algorithm. In the meantime, complex genomic data brought great challenges for the identification of biomarkers and therapeutic targets. The current some feature selection methods have the problem of low sensitivity and specificity in this field. Results In this article, we designed a multi-scale clustering-based feature selection algorithm named MCBFS which simultaneously performs feature selection and model learning for genomic data analysis. The experimental results demonstrated that MCBFS is robust and effective by comparing it with seven benchmark and six state-of-the-art supervised methods on eight data sets. The visualization results and the statistical test showed that MCBFS can capture the informative genes and improve the interpretability and visualization of tumor gene expression and single-cell sequencing data. Additionally, we developed a general framework named McbfsNW using gene expression data and protein interaction data to identify robust biomarkers and therapeutic targets for diagnosis and therapy of diseases. The framework incorporates the MCBFS algorithm, network recognition ensemble algorithm and feature selection wrapper. McbfsNW has been applied to the lung adenocarcinoma (LUAD) data sets. The preliminary results demonstrated that higher prediction results can be attained by identified biomarkers on the independent LUAD data set, and we also structured a drug-target network which may be good for LUAD therapy. Conclusions The proposed novel feature selection method is robust and effective for gene selection, classification, and visualization. The framework McbfsNW is practical and helpful for the identification of biomarkers and targets on genomic data. It is believed that the same methods and principles are extensible and applicable to other different kinds of data sets.

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Section: Resultsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Multi-scale supervised clustering-based feature selection for tumor classification and identification of biomarkers and targets on genomic data

Zhang

et al. 2020

BMC Genomics

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“…A high-quality sampling approach has been proposed for imbalanced cancer samples for pre-diagnosis [9]. Supervised discriminative sparse principal component analysis (SDSPCA) has been used to study the pathogenesis of diseases and gene selection [10].…”

Section: Introductionmentioning

confidence: 99%

Developing Sustainable Classification of Diseases via Deep Learning and Semi-Supervised Learning

Yin

Chen

2020

Healthcare

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Disease classification based on machine learning has become a crucial research topic in the fields of genetics and molecular biology. Generally, disease classification involves a supervised learning style; i.e., it requires a large number of labelled samples to achieve good classification performance. However, in the majority of the cases, labelled samples are hard to obtain, so the amount of training data are limited. However, many unclassified (unlabelled) sequences have been deposited in public databases, which may help the training procedure. This method is called semi-supervised learning and is very useful in many applications. Self-training can be implemented using high- to low-confidence samples to prevent noisy samples from affecting the robustness of semi-supervised learning in the training process. The deep forest method with the hyperparameter settings used in this paper can achieve excellent performance. Therefore, in this work, we propose a novel combined deep learning model and semi-supervised learning with self-training approach to improve the performance in disease classification, which utilizes unlabelled samples to update a mechanism designed to increase the number of high-confidence pseudo-labelled samples. The experimental results show that our proposed model can achieve good performance in disease classification and disease-causing gene identification.

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“…To efficiently preserve the local relations of the data and eliminate the negative effects of outliers in the PCA method, Zhou et al [23] proposed an improved sparse PCA method named robust sparse PCA. Feng et al [24] proposed a supervised discriminative sparse PCA (SDSPCA) method to study the pathogenesis of diseases, which incorporated discriminative information into the sparse PCA model. Then, Sun et al [25] presented a lateral-slice sparse tensor robust principal component analysis (LSSTRPCA) method to promote the performance of classification by removing gross errors or outliers from hyperspectral images.…”

Section: Introductionmentioning

confidence: 99%

Class-Specific Sparse Principal Component Analysis for Visual Classification

et al. 2020

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Extensive research has demonstrated that dictionary learning is active in improving the performance of the representation based classification. However, dictionary learning suffers from lacking an effective dictionary structure that can well tradeoff the reducing reconstruction error and enhancing the representative information. In this paper, we focus on designing capable dictionary learning architecture for the visual classification task with few-shot training samples. First, we propose a class-specific sparse PCA approach to extend the conventional dictionary learning to dictionary pair learning for visual classification. The orthogonal synthesis dictionary ensures fewer reconstruction errors with fixed dictionary size while the sparse analysis dictionary guarantees the representative features. Second, we develop the Alternating Direction Method of Multipliers algorithm to optimize the analysis dictionary to improve computational efficiency. Extensive experiments conducted on multiple image datasets demonstrate that the proposed class-specific PCA method performs favorably against the state-of-art methods on eight widely used image datasets.

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Supervised Discriminative Sparse PCA for Com-Characteristic Gene Selection and Tumor Classification on Multiview Biological Data

Cited by 50 publications

References 68 publications

Multi-scale supervised clustering-based feature selection for tumor classification and identification of biomarkers and targets on genomic data

Multi-scale supervised clustering-based feature selection for tumor classification and identification of biomarkers and targets on genomic data

Developing Sustainable Classification of Diseases via Deep Learning and Semi-Supervised Learning

Class-Specific Sparse Principal Component Analysis for Visual Classification

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