Tumor Classification Based on Non-Negative Matrix Factorization Using Gene Expression Data

Zheng, Chun-Hou; Ng, To-Yee; Zhang, Lei; Shiu, Simon; Wang, Hongqiang

doi:10.1109/tnb.2011.2144998

Cited by 51 publications

(13 citation statements)

References 35 publications

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“…These constraints lead to a part-based representation because only additive, not subtractive, combinations of the original data are allowed [ 19 ]. In general, NMF can be used to describe hundreds to thousands of features in a dataset in terms of a small number of metafeatures, particularly in gene expression profiles analysis [ 20 – 22 ].…”

Section: Methodsmentioning

confidence: 99%

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NMFBFS: A NMF-Based Feature Selection Method in Identifying Pivotal Clinical Symptoms of Hepatocellular Carcinoma

Meng

Huang

et al. 2015

Computational and Mathematical Methods in Medicine

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Background. Hepatocellular carcinoma (HCC) is a highly aggressive malignancy. Traditional Chinese Medicine (TCM), with the characteristics of syndrome differentiation, plays an important role in the comprehensive treatment of HCC. This study aims to develop a nonnegative matrix factorization- (NMF-) based feature selection approach (NMFBFS) to identify potential clinical symptoms for HCC patient stratification. Methods. The NMFBFS approach consisted of three major steps. Firstly, statistics-based preliminary feature screening was designed to detect and remove irrelevant symptoms. Secondly, NMF was employed to infer redundant symptoms. Based on NMF-derived basis matrix, we defined a novel similarity measurement of intersymptoms. Finally, we converted each group of redundant symptoms to a new single feature so that the dimension was further reduced. Results. Based on a clinical dataset consisting of 407 patient samples of HCC with 57 symptoms, NMFBFS approach detected 8 irrelevant symptoms and then identified 16 redundant symptoms within 6 groups. Finally, an optimal feature subset with 39 clinical features was generated after compressing the redundant symptoms by groups. The validation of classification performance shows that these 39 features obviously improve the prediction accuracy of HCC patients. Conclusions. Compared with other methods, NMFBFS has obvious advantages in identifying important clinical features of HCC.

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Section: Methodsmentioning

confidence: 99%

“…The matrix H has the same number of samples but much smaller number of features rather than matrix X . Therefore, the metafeature expression patterns in H usually provide a robust clustering of samples [ 22 ].…”

Section: Methodsmentioning

confidence: 99%

NMFBFS: A NMF-Based Feature Selection Method in Identifying Pivotal Clinical Symptoms of Hepatocellular Carcinoma

Meng

Huang

et al. 2015

Computational and Mathematical Methods in Medicine

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“…In recent years, LRR [16–18] and neural networks have been widely used in feature extraction and classification of gene expression profile. Reference [19] used NMF for gene feature extraction and achieved more satisfactory results. Ref.…”

Section: Introductionmentioning

confidence: 99%

Gene Feature Extraction Based on Nonnegative Dual Graph Regularized Latent Low-Rank Representation

Yang

2017

BioMed Research International

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Aiming at the problem of gene expression profile's high redundancy and heavy noise, a new feature extraction model based on nonnegative dual graph regularized latent low-rank representation (NNDGLLRR) is presented on the basis of latent low-rank representation (Lat-LRR). By introducing dual graph manifold regularized constraint, the NNDGLLRR can keep the internal spatial structure of the original data effectively and improve the final clustering accuracy while segmenting the subspace. The introduction of nonnegative constraints makes the computation with some sparsity, which enhances the robustness of the algorithm. Different from Lat-LRR, a new solution model is adopted to simplify the computational complexity. The experimental results show that the proposed algorithm has good feature extraction performance for the heavy redundancy and noise gene expression profile, which, compared with LRR and Lat-LRR, can achieve better clustering accuracy.

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“…With advances in DNA microarray technology, it is now possible to monitor the expression levels of a large number of genes at the same time. There have been a variety of studies on analyzing DNA microarray data for cancer class discovery [3-5]. Such methods are demonstrated to outperform the traditional, morphological appearance-based cancer classification methods.…”

Section: Introductionmentioning

confidence: 99%

“…Carmona et al [9] presented a methodology that was able to cluster closely related genes and conditions in sub-portions of the data based on non-smooth non-negative matrix factorization (nsNMF), which was able to identify localized patterns in large datasets. Zheng et al [5,7] applied penalized matrix decomposition (PMD) to extract meta-samples from gene expression data, which could captured the inherent structures of samples that belonged to the same class.…”

Section: Introductionmentioning

confidence: 99%

Non-negative matrix factorization by maximizing correntropy for cancer clustering

2013

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BackgroundNon-negative matrix factorization (NMF) has been shown to be a powerful tool for clustering gene expression data, which are widely used to classify cancers. NMF aims to find two non-negative matrices whose product closely approximates the original matrix. Traditional NMF methods minimize either the l2 norm or the Kullback-Leibler distance between the product of the two matrices and the original matrix. Correntropy was recently shown to be an effective similarity measurement due to its stability to outliers or noise.ResultsWe propose a maximum correntropy criterion (MCC)-based NMF method (NMF-MCC) for gene expression data-based cancer clustering. Instead of minimizing the l2 norm or the Kullback-Leibler distance, NMF-MCC maximizes the correntropy between the product of the two matrices and the original matrix. The optimization problem can be solved by an expectation conditional maximization algorithm.ConclusionsExtensive experiments on six cancer benchmark sets demonstrate that the proposed method is significantly more accurate than the state-of-the-art methods in cancer clustering.

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Tumor Classification Based on Non-Negative Matrix Factorization Using Gene Expression Data

Cited by 51 publications

References 35 publications

NMFBFS: A NMF-Based Feature Selection Method in Identifying Pivotal Clinical Symptoms of Hepatocellular Carcinoma

NMFBFS: A NMF-Based Feature Selection Method in Identifying Pivotal Clinical Symptoms of Hepatocellular Carcinoma

Gene Feature Extraction Based on Nonnegative Dual Graph Regularized Latent Low-Rank Representation

Non-negative matrix factorization by maximizing correntropy for cancer clustering

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