PCA via joint graph Laplacian and sparse constraint: Identification of differentially expressed genes and sample clustering on gene expression data

Feng, Chun-Mei; Xu, Yong; Hou, Mi-Xiao; Dai, Lingyun; Shang, Junliang

doi:10.1186/s12859-019-3229-z

Cited by 9 publications

(10 citation statements)

References 33 publications

(32 reference statements)

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“…Performance Comparison with Other Related Methods. In this section, we will compare the performance of RLSDSPCA with other related methods (PCA, 6 gLPCA, 16 gLSPCA, 17 RgLPCA, 16 and SDSPCA 12 ) in characteristic gene selection and tumor classification to further verify the effectiveness of the proposed method.…”

Section: Robustmentioning

confidence: 99%

“…At the same time, they have also developed a robust version of gLPCA, called robust gLPCA (RgLPCA), which applies the L2,1 norm instead of the Frobenius norm to the reconstruction term to reduce the influence of outliers and noise. 16 Feng et al added L2,1 sparse constraints on the basis of gLPCA and proposed gLSPCA, 17 which can obtain low-dimensional data structural information and improve the interpretability of principal components simultaneously.…”

Section: ■ Introductionmentioning

confidence: 99%

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Enhancing Characteristic Gene Selection and Tumor Classification by the Robust Laplacian Supervised Discriminative Sparse PCA

Zhang

Liu

et al. 2022

J. Chem. Inf. Model.

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Characteristic gene selection and tumor classification of gene expression data play major roles in genomic research. Due to the characteristics of a small sample size and high dimensionality of gene expression data, it is a common practice to perform dimensionality reduction prior to the use of machine learning-based methods to analyze the expression data. In this context, classical principal component analysis (PCA) and its improved versions have been widely used. Recently, methods based on supervised discriminative sparse PCA have been developed to improve the performance of data dimensionality reduction. However, such methods still have limitations: most of them have not taken into consideration the improvement of robustness to outliers and noise, label information, sparsity, as well as capturing intrinsic geometrical structures in one objective function. To address this drawback, in this study, we propose a novel PCA-based method, known as the robust Laplacian supervised discriminative sparse PCA, termed RLSDSPCA, which enforces the L2,1 norm on the error function and incorporates the graph Laplacian into supervised discriminative sparse PCA. To evaluate the efficacy of the proposed RLSDSPCA, we applied it to the problems of characteristic gene selection and tumor classification problems using gene expression data. The results demonstrate that the proposed RLSDSPCA method, when used in combination with other related methods, can effectively identify new pathogenic genes associated with diseases. In addition, RLSDSPCA has also achieved the best performance compared with the state-of-the-art methods on tumor classification in terms of major performance metrics. The codes and data sets used in the study are freely available at .

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Section: Robustmentioning

confidence: 99%

Section: ■ Introductionmentioning

confidence: 99%

Enhancing Characteristic Gene Selection and Tumor Classification by the Robust Laplacian Supervised Discriminative Sparse PCA

Zhang

Liu

et al. 2022

J. Chem. Inf. Model.

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“…Secondly, because it is unclear which feature is the best, many features could be extracted for the target and the drug in the same time [ 16 – 18 ], and then some dimensional reduction methods have been proposed for DTI [ 19 – 23 ]. Ezzat et al proposed a framework for DTI prediction by leveraging both feature dimensionality reduction and ensemble learning [ 19 ].…”

Section: Introductionmentioning

confidence: 99%

“…Mahmud et al predicted DTI based on protein features with under sampling and feature selection techniques with boosting [ 21 ]. Feng et al proposed a supervised discriminative sparse principal component analysis [ 22 ] and a graph Laplacian sparse principal component analysis for dimensional reduction [ 23 ]…”

Section: Introductionmentioning

confidence: 99%

Drug–target interaction prediction via multiple classification strategies

Zhang

Lin

2021

BMC Bioinformatics

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Background Computational prediction of the interaction between drugs and protein targets is very important for the new drug discovery, as the experimental determination of drug-target interaction (DTI) is expensive and time-consuming. However, different protein targets are with very different numbers of interactions. Specifically, most interactions focus on only a few targets. As a result, targets with larger numbers of interactions could own enough positive samples for predicting their interactions but the positive samples for targets with smaller numbers of interactions could be not enough. Only using a classification strategy may not be able to deal with the above two cases at the same time. To overcome the above problem, in this paper, a drug-target interaction prediction method based on multiple classification strategies (MCSDTI) is proposed. In MCSDTI, targets are firstly divided into two parts according to the number of interactions of the targets, where one part contains targets with smaller numbers of interactions (TWSNI) and another part contains targets with larger numbers of interactions (TWLNI). And then different classification strategies are respectively designed for TWSNI and TWLNI to predict the interaction. Furthermore, TWSNI and TWLNI are evaluated independently, which can overcome the problem that result could be mainly determined by targets with large numbers of interactions when all targets are evaluated together. Results We propose a new drug-target interaction (MCSDTI) prediction method, which uses multiple classification strategies. MCSDTI is tested on five DTI datasets, such as nuclear receptors (NR), ion channels (IC), G protein coupled receptors (GPCR), enzymes (E), and drug bank (DB). Experiments show that the AUCs of our method are respectively 3.31%, 1.27%, 2.02%, 2.02% and 1.04% higher than that of the second best methods on NR, IC, GPCR and E for TWLNI; And AUCs of our method are respectively 1.00%, 3.20% and 2.70% higher than the second best methods on NR, IC, and E for TWSNI. Conclusion MCSDTI is a competitive method compared to the previous methods for all target parts on most datasets, which administrates that different classification strategies for different target parts is an effective way to improve the effectiveness of DTI prediction.

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“…Most studies (e.g., SENSE (Pruessmann et al 1999), GRAPPA (Griswold et al 2002), SPIRiT (Lustig and Pauly 2010)) take advantage of spatial sensitivity and gradient coding to reduce the amount of data required for reconstruction, thereby shortening the imaging time. Moreover, compressed sensing (CS) is an important technique for fast MR image reconstruction (Feng et al 2019), which recovers the desired signal from k-space data sampled below the Nyquist rate. Typical CS-based approaches adopt a sparsity prior (Liu et al 2019), low-rank sparse sampling (He et al 2016;Haldar and Zhuo 2016), or manifold learning (Nakarmi et al 2017) for reconstruction.…”

Section: Introductionmentioning

confidence: 99%

Dual-Octave Convolution for Accelerated Parallel MR Image Reconstruction

Feng

Yang

Chen

et al. 2021

AAAI

Self Cite

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Magnetic resonance (MR) image acquisition is an inherently prolonged process, whose acceleration by obtaining multiple undersampled images simultaneously through parallel imaging has always been the subject of research. In this paper, we propose the Dual-Octave Convolution (Dual-OctConv), which is capable of learning multi-scale spatial-frequency features from both real and imaginary components, for fast parallel MR image reconstruction. By reformulating the complex operations using octave convolutions, our model shows a strong ability to capture richer representations of MR images, while at the same time greatly reducing the spatial redundancy. More specifically, the input feature maps and convolutional kernels are first split into two components (i.e., real and imaginary), which are then divided into four groups according to their spatial frequencies. Then, our Dual-OctConv conducts intra-group information updating and inter-group information exchange to aggregate the contextual information across different groups. Our framework provides two appealing benefits: (i) it encourages interactions between real and imaginary components at various spatial frequencies to achieve richer representational capacity, and (ii) it enlarges the receptive field by learning multiple spatial-frequency features of both the real and imaginary components. We evaluate the performance of the proposed model on the acceleration of multi-coil MR image reconstruction. Extensive experiments are conducted on an {in vivo} knee dataset under different undersampling patterns and acceleration factors. The experimental results demonstrate the superiority of our model in accelerated parallel MR image reconstruction. Our code is available at: github.com/chunmeifeng/Dual-OctConv.

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PCA via joint graph Laplacian and sparse constraint: Identification of differentially expressed genes and sample clustering on gene expression data

Cited by 9 publications

References 33 publications

Enhancing Characteristic Gene Selection and Tumor Classification by the Robust Laplacian Supervised Discriminative Sparse PCA

Enhancing Characteristic Gene Selection and Tumor Classification by the Robust Laplacian Supervised Discriminative Sparse PCA

Drug–target interaction prediction via multiple classification strategies

Dual-Octave Convolution for Accelerated Parallel MR Image Reconstruction

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