Safe Feature Screening for Generalized LASSO

Ren, Shaogang; Huang, Shuai; Ye, Jieping; Qian, Xiaoning

doi:10.1109/tpami.2017.2776267

Cited by 15 publications

(9 citation statements)

References 14 publications

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“…And this LASSO model method could decrease the characteristic dimension. Then, a multivariable Cox regression model to select driven genes that were most closely associated with survival was constructed and six methylation-driven genes were retained (22,23).…”

Section: Identification and Validation Of Methylation-driven Genes For Crcmentioning

confidence: 99%

Integrative Analysis of Identifying Methylation-Driven Genes Signature Predicts Prognosis in Colorectal Carcinoma

Huang

Zhang

et al. 2021

Front. Oncol.

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BackgroundAberrant DNA methylation is a critical regulator of gene expression and plays a crucial role in the occurrence, progression, and prognosis of colorectal cancer (CRC). We aimed to identify methylation-driven genes by integrative epigenetic and transcriptomic analysis to predict the prognosis of CRC patients.MethodsMethylation-driven genes were selected for CRC using a MethylMix algorithm and LASSO regression screening strategy, and were further used to construct a prognostic risk-assessment model. The Cancer Genome Atlas (TCGA) database was obtained as the training set for both the screening of methylation-driven genes and the effect of genes signature on CRC prognosis. Then, the prognostic genes signature was validated in three independent expression arrays of CRC data from Gene Expression Omnibus (GEO).ResultsWe identified 143 methylation-driven genes, of which the combination of BATF, PHYHIPL, RBP1, and PNPLA4 expression levels was screened as a better prognostic model with the best area under the curve (AUC) (AUC = 0.876). Compared with patients in the low-risk group, CRC patients in the high-risk group had significantly poorer overall survival in the training set (HR = 2.184, 95% CI: 1.404–3.396, P < 0.001). Similar results were observed in the validation set. Moreover, VanderWeele’s mediation analysis indicated that the effect of methylation on prognosis was mediated by the levels of their expression (HRindirect = 1.473, P = 0.001, Proportion mediated, 69.10%).ConclusionsWe identified a four-gene prognostic signature by integrative analysis and developed a risk-assessment model that is significantly associated with patients’ survival. Methylation-driven genes might be a potential prognostic signature for CRC patients.

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Section: Identification and Validation Of Methylation-driven Genes For Crcmentioning

confidence: 99%

Integrative Analysis of Identifying Methylation-Driven Genes Signature Predicts Prognosis in Colorectal Carcinoma

Huang

Zhang

et al. 2021

Front. Oncol.

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“…A strong correlation often exists between the variables, which is suggestive of high dimensionality and collinearity, and this method could decrease the characteristic dimension [20]. Then, we built a multivariate Cox regression model to select the genes that were most tightly associated with survival [21]. In addition, we validated this model in subgroups based on different characteristics.…”

Section: Methodsmentioning

confidence: 99%

A six-gene prognostic model predicts overall survival in bladder cancer patients

et al. 2019

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Background The fatality and recurrence rates of bladder cancer (BC) have progressively increased. DNA methylation is an influential regulator associated with gene transcription in the pathogenesis of BC. We describe a comprehensive epigenetic study performed to analyse DNA methylation-driven genes in BC. Methods Data related to DNA methylation, the gene transcriptome and survival in BC were downloaded from The Cancer Genome Atlas (TCGA). MethylMix was used to detect BC-specific hyper-/hypo-methylated genes. Metascape was used to carry out gene ontology (GO) enrichment and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analyses. A least absolute shrinkage and selection operator (LASSO)-penalized Cox regression was conducted to identify the characteristic dimension decrease and distinguish prognosis-related methylation-driven genes. Subsequently, we developed a six-gene risk evaluation model and a novel prognosis-related nomogram to predict overall survival (OS). A survival analysis was carried out to explore the individual prognostic significance of the six genes. Results In total, 167 methylation-driven genes were identified. Based on the LASSO Cox regression, six genes, i.e., ARHGDIB, LINC00526, IDH2, ARL14, GSTM2, and LURAP1, were selected for the development of a risk evaluation model. The Kaplan–Meier curve indicated that patients in the low-risk group had considerably better OS (P = 1.679e−05). The area under the curve (AUC) of this model was 0.698 at 3 years of OS. The verification performed in subgroups demonstrated the validity of the model. Then, we designed an OS-associated nomogram that included the risk score and clinical factors. The concordance index of the nomogram was 0.694. The methylation levels of IDH2 and ARL14 were appreciably related to the survival results. In addition, the methylation and gene expression-matched survival analysis revealed that ARHGDIB and ARL14 could be used as independent prognostic indicators. Among the six genes, 6 methylation sites in ARHGDIB, 3 in GSTM2, 1 in ARL14, 2 in LINC00526 and 2 in LURAP1 were meaningfully associated with BC prognosis. In addition, several abnormal methylated sites were identified as linked to gene expression. Conclusion We discovered differential methylation in BC patients with better and worse survival and provided a risk evaluation model by merging six gene markers with clinical characteristics.

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“…Zeng and Cheung [89] applied a sparse-promoting penalty in learning feature weights in an unsupervised setting. Ren et al [57] investigated feature selection using a generalized LASSO model. In [3] the idea of annealing was combined with feature selection using a sparsity favoring loss function.…”

Section: Feature Redundancymentioning

confidence: 99%

Estimating Feature-Label Dependence Using Gini Distance Statistics

Zhang

Dang

Nguyen

et al. 2021

IEEE Trans. Pattern Anal. Mach. Intell.

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Identifying statistical dependence between the features and the label is a fundamental problem in supervised learning. This paper presents a framework for estimating dependence between numerical features and a categorical label using generalized Gini distance, an energy distance in reproducing kernel Hilbert spaces (RKHS). Two Gini distance based dependence measures are explored: Gini distance covariance and Gini distance correlation. Unlike Pearson covariance and correlation, which do not characterize independence, the above Gini distance based measures define dependence as well as independence of random variables. The test statistics are simple to calculate and do not require probability density estimation. Uniform convergence bounds and asymptotic bounds are derived for the test statistics. Comparisons with distance covariance statistics are provided. It is shown that Gini distance statistics converge faster than distance covariance statistics in the uniform convergence bounds, hence tighter upper bounds on both Type I and Type II errors. Moreover, the probability of Gini distance covariance statistic under-performing the distance covariance statistic in Type II error decreases to 0 exponentially with the increase of the sample size. Extensive experimental results are presented to demonstrate the performance of the proposed method.

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Safe Feature Screening for Generalized LASSO

Cited by 15 publications

References 14 publications

Integrative Analysis of Identifying Methylation-Driven Genes Signature Predicts Prognosis in Colorectal Carcinoma

Integrative Analysis of Identifying Methylation-Driven Genes Signature Predicts Prognosis in Colorectal Carcinoma

A six-gene prognostic model predicts overall survival in bladder cancer patients

Estimating Feature-Label Dependence Using Gini Distance Statistics

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