Grouped gene selection and multi-classification of acute leukemia via new regularized multinomial regression

Li, Juntao; Wang, Yanyan; Jiang, Tao; Xiao, Huimin; Song, Xuekun

doi:10.1016/j.gene.2018.05.012

Cited by 16 publications

(5 citation statements)

References 36 publications

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“…As a summary, both theories and applications of penalized regression models have been extensively investigated, and great advances on the related theories have been achieved over recent decades. Besides the mentioned works in this review, there are also many other related works [135][136][137][138], but due to space limitations and the limited knowledge of the authors, this review can not cover all of the works in this topic.…”

Section: Future Directions and Conclusionmentioning

confidence: 99%

Recent Advances on Penalized Regression Models for Biological Data

2022

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Increasingly amounts of biological data promote the development of various penalized regression models. This review discusses the recent advances in both linear and logistic regression models with penalization terms. This review is mainly focused on various penalized regression models, some of the corresponding optimization algorithms, and their applications in biological data. The pros and cons of different models in terms of response prediction, sample classification, network construction and feature selection are also reviewed. The performances of different models in a real-world RNA-seq dataset for breast cancer are explored. Finally, some future directions are discussed.

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Section: Future Directions and Conclusionmentioning

confidence: 99%

Recent Advances on Penalized Regression Models for Biological Data

2022

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“…For the first 4 data sets, we used the weighted gene co-expression networks (Langfelder and Horvath, 2008) to cluster gene expressions in different groups (modules), and an R package is available for this clustering. The group structures of the last 3 data sets, generated by a similar method, are provided in (Li et al, 2018a). More details about the group structures can be found in Appendix B.…”

Section: Gene Datamentioning

confidence: 99%

“…Acute leukemia data The raw acute leukemia data set includes 72 samples of 3571 gene expressions. Following (Li et al, 2018a), the grouping strategy with repeated genes was applied to get the data with 10713 gene expressions. The value of the approximate solution β (coordinates reordered by its groups) with CV (37) selected parameters in BALL, TALL, and AML data sets is presented in Figure 7.…”

Section: Appendix B Gene Data Setsmentioning

confidence: 99%

On Regularized Square-root Regression Problems: Distributionally Robust Interpretation and Fast Computations

Chu,

Toh,

Zhang

2021

Preprint

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Square-root (loss) regularized models have recently become popular in linear regression due to their nice statistical properties. Moreover, some of these models can be interpreted as the distributionally robust optimization counterparts of the traditional least-squares regularized models. In this paper, we give a unified proof to show that any square-root regularized model whose penalty function being the sum of a simple norm and a seminorm can be interpreted as the distributionally robust optimization (DRO) formulation of the corresponding least-squares problem. In particular, the optimal transport cost in the DRO formulation is given by a certain dual form of the penalty. To solve the resulting squareroot regularized model whose loss function and penalty function are both nonsmooth, we design a proximal point dual semismooth Newton algorithm and demonstrate its efficiency when the penalty is the sparse group Lasso penalty or the fused Lasso penalty. Extensive experiments demonstrate that our algorithm is highly efficient for solving the square-root sparse group Lasso problems and the square-root fused Lasso problems.

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“…During cell division and growth, abnormal changes often happen to genes, which results in varying cancers. With the rapid development of kinds of biomedical technologies [1], DNA microarray comes into being and lots of microarray data can be obtained for cancer prevention, diagnosis, and treatment [2][3][4][5][6][7][8][9][10][11][12]. For various microarray data, classifying the different types of tumors is an important task, but challenging due to the high dimensionality and small numbers of samples [13][14][15] since the small number of data samples with large number of genes can easily result in the "curse of dimensionality" and overfitting problems of data processing and learning models.…”

Section: Introductionmentioning

confidence: 99%

Gene Correlation Guided Gene Selection for Microarray Data Classification

Yang

Zhu

2021

BioMed Research International

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The microarray cancer data obtained by DNA microarray technology play an important role for cancer prevention, diagnosis, and treatment. However, predicting the different types of tumors is a challenging task since the sample size in microarray data is often small but the dimensionality is very high. Gene selection, which is an effective means, is aimed at mitigating the curse of dimensionality problem and can boost the classification accuracy of microarray data. However, many of previous gene selection methods focus on model design, but neglect the correlation between different genes. In this paper, we introduce a novel unsupervised gene selection method by taking the gene correlation into consideration, named gene correlation guided gene selection (G3CS). Specifically, we calculate the covariance of different gene dimension pairs and embed it into our unsupervised gene selection model to regularize the gene selection coefficient matrix. In such a manner, redundant genes can be effectively excluded. In addition, we utilize a matrix factorization term to exploit the cluster structure of original microarray data to assist the learning process. We design an iterative updating algorithm with convergence guarantee to solve the resultant optimization problem. Experimental results on six publicly available microarray datasets are conducted to validate the efficacy of our proposed method.

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Grouped gene selection and multi-classification of acute leukemia via new regularized multinomial regression

Cited by 16 publications

References 36 publications

Recent Advances on Penalized Regression Models for Biological Data

Recent Advances on Penalized Regression Models for Biological Data

On Regularized Square-root Regression Problems: Distributionally Robust Interpretation and Fast Computations

Gene Correlation Guided Gene Selection for Microarray Data Classification

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