Robust Nonnegative Matrix Factorization via Joint Graph Laplacian and Discriminative Information for Identifying Differentially Expressed Genes

Dai, Lingyun; Feng, Chun-Mei; Liu, Jin-Xing; Zheng, Chun-Hou; Yu, Jie-Xiang; Hou, Mi-Xiao

doi:10.1155/2017/4216797

Cited by 17 publications

(6 citation statements)

References 44 publications

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“…In 2008, a study has shown that uncorrelated features will reduce the imputation efficiency as previous imputation method such as traditional KNN tends to bias toward outliers or uncorrelated features. As a consequence, the performance of KNN degrades, especially when the missing rate increases [9]. The paper proposes feature selection before imputation which is the modified KNN called KNN-based feature selection (KNN-FS) [10].…”

Section: Introductionmentioning

confidence: 99%

Missing data imputation with fuzzy feature selection for diabetes dataset

Dzulkalnine

Sallehuddin

2019

SN Appl. Sci.

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Missing data in datasets remain as a difficulty in terms of data analysis in various research fields, especially in the medical field, as it affects the treatment and diagnosis that the patient should receive. In this research, Fuzzy c-means (FCM) are used to impute the missing data. However, like in most data imputation methods, FCM do not consider the presence of irrelevant features. Irrelevant features can increase the computational time of the imputation process and decrease the accuracy of the prediction. Feature selection techniques can alleviate this problem by selecting the most relevant features and reducing the dataset size. Fuzzy principal component analysis (FPCA) is used as the feature selection method in this study as it considers the presence of outliers compared to classical PCA as outliers are the main reason some features renders irrelevant. Therefore, an improved hybrid imputation model of FPCA-Support vector machines-FCM (FPCA-SVM-FCM) has been proposed and employed in this study. The efficiency of the proposed model is investigated on one dataset which is Pima Indians Diabetes dataset. Experimental results showed that the proposed hybrid imputation model is better than the existing methods by producing a more accurate estimation in terms of accuracy, RMSE and MAE. The proposed method was also validated by using Wilcoxon rank sum and Theil's U test and obtained good results compared to SVM-FCM. Therefore, it can be used as an alternative tool for handling missing data in order to obtain a better quality dataset.

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Section: Introductionmentioning

confidence: 99%

Missing data imputation with fuzzy feature selection for diabetes dataset

Dzulkalnine

Sallehuddin

2019

SN Appl. Sci.

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“…In graph theory, the “manifold assumption” is that data points near local geometrical structures should keep their proximity under a new basis [35]. If we map the adjacent data points x i and x j in the high-dimensional space to the low-dimensional space, their mapping data points z i and z j should be close in the low-dimensional space.…”

Section: Methodsmentioning

confidence: 99%

Multi-cancer samples clustering via graph regularized low-rank representation method under sparse and symmetric constraints

Wang

Liu

et al. 2019

BMC Bioinformatics

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BackgroundIdentifying different types of cancer based on gene expression data has become hotspot in bioinformatics research. Clustering cancer gene expression data from multiple cancers to their own class is a significance solution. However, the characteristics of high-dimensional and small samples of gene expression data and the noise of the data make data mining and research difficult. Although there are many effective and feasible methods to deal with this problem, the possibility remains that these methods are flawed.ResultsIn this paper, we propose the graph regularized low-rank representation under symmetric and sparse constraints (sgLRR) method in which we introduce graph regularization based on manifold learning and symmetric sparse constraints into the traditional low-rank representation (LRR). For the sgLRR method, by means of symmetric constraint and sparse constraint, the effect of raw data noise on low-rank representation is alleviated. Further, sgLRR method preserves the important intrinsic local geometrical structures of the raw data by introducing graph regularization. We apply this method to cluster multi-cancer samples based on gene expression data, which improves the clustering quality. First, the gene expression data are decomposed by sgLRR method. And, a lowest rank representation matrix is obtained, which is symmetric and sparse. Then, an affinity matrix is constructed to perform the multi-cancer sample clustering by using a spectral clustering algorithm, i.e., normalized cuts (Ncuts). Finally, the multi-cancer samples clustering is completed.ConclusionsA series of comparative experiments demonstrate that the sgLRR method based on low rank representation has a great advantage and remarkable performance in the clustering of multi-cancer samples.

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“…Different from Ref. [5], it is a feature selection method. Then, we used k-means to cluster the samples based on the new data representations V .…”

Section: Experimental Configurationsmentioning

confidence: 99%

Robust Semi‐nonnegative Matrix Factorization with Adaptive Graph Regularization for Gene Representation

Jiang¹,

Ma²,

Feng³

et al. 2020

Chin. j. electron.

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Robust Nonnegative Matrix Factorization via Joint Graph Laplacian and Discriminative Information for Identifying Differentially Expressed Genes

Cited by 17 publications

References 44 publications

Missing data imputation with fuzzy feature selection for diabetes dataset

Missing data imputation with fuzzy feature selection for diabetes dataset

Multi-cancer samples clustering via graph regularized low-rank representation method under sparse and symmetric constraints

Robust Semi‐nonnegative Matrix Factorization with Adaptive Graph Regularization for Gene Representation

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