scholarly journals RAMRSGL: A Robust Adaptive Multinomial Regression Model for Multicancer Classification

2021 ◽  
Vol 2021 ◽  
pp. 1-13
Author(s):  
Lei Wang ◽  
Juntao Li ◽  
Juanfang Liu ◽  
Mingming Chang
Keyword(s):  
Gene Selection ◽  
Likelihood Function ◽  
Group Structure ◽  
Group Lasso ◽  
Microarray Data Analysis ◽  
Multinomial Regression ◽  
Sparse Group Lasso ◽  
Lasso Penalty ◽  
Robust Adaptive ◽  
Leukemia Data

In view of the challenges of the group Lasso penalty methods for multicancer microarray data analysis, e.g., dividing genes into groups in advance and biological interpretability, we propose a robust adaptive multinomial regression with sparse group Lasso penalty (RAMRSGL) model. By adopting the overlapping clustering strategy, affinity propagation clustering is employed to obtain each cancer gene subtype, which explores the group structure of each cancer subtype and merges the groups of all subtypes. In addition, the data-driven weights based on noise are added to the sparse group Lasso penalty, combining with the multinomial log-likelihood function to perform multiclassification and adaptive group gene selection simultaneously. The experimental results on acute leukemia data verify the effectiveness of the proposed method.

scholarly journals Group Guided Fused Laplacian Sparse Group Lasso for Modeling Alzheimer’s Disease Progression

2020 ◽  
Vol 2020 ◽  
pp. 1-23
Author(s):  
Xiaoli Liu ◽  
Jianzhong Wang ◽  
Fulong Ren ◽  
Jun Kong
Keyword(s):  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Disease Progression ◽  
Group Structure ◽  
Group Lasso ◽  
Imaging Biomarkers ◽  
Compact Sets ◽  
Time Points ◽  
Sparse Group Lasso ◽  
Mri Features

As the largest cause of dementia, Alzheimer’s disease (AD) has brought serious burdens to patients and their families, mostly in the financial, psychological, and emotional aspects. In order to assess the progression of AD and develop new treatment methods for the disease, it is essential to infer the trajectories of patients’ cognitive performance over time to identify biomarkers that connect the patterns of brain atrophy and AD progression. In this article, a structured regularized regression approach termed group guided fused Laplacian sparse group Lasso (GFL-SGL) is proposed to infer disease progression by considering multiple prediction of the same cognitive scores at different time points (longitudinal analysis). The proposed GFL-SGL simultaneously exploits the interrelated structures within the MRI features and among the tasks with sparse group Lasso (SGL) norm and presents a novel group guided fused Laplacian (GFL) regularization. This combination effectively incorporates both the relatedness among multiple longitudinal time points with a general weighted (undirected) dependency graphs and useful inherent group structure in features. Furthermore, an alternating direction method of multipliers- (ADMM-) based algorithm is also derived to optimize the nonsmooth objective function of the proposed approach. Experiments on the dataset from Alzheimer’s Disease Neuroimaging Initiative (ADNI) show that the proposed GFL-SGL outperformed some other state-of-the-art algorithms and effectively fused the multimodality data. The compact sets of cognition-relevant imaging biomarkers identified by our approach are consistent with the results of clinical studies.

Lung Cancer Classification and Gene Selection by Combining Affinity Propagation Clustering and Sparse Group Lasso

2020 ◽  
Vol 15 (7) ◽  
pp. 703-712
Author(s):  
Juntao Li ◽  
Mingming Chang ◽  
Qinghui Gao ◽  
Xuekun Song ◽  
Zhiyu Gao
Keyword(s):  
Lung Cancer ◽  
Gene Selection ◽  
Similarity Score ◽  
Group Lasso ◽  
Affinity Propagation ◽  
Normal Lung ◽  
Cancer Subtypes ◽  
Sparse Group Lasso ◽  
Pathogenic Genes ◽  
Ap Clustering

Background: Cancer threatens human health seriously. Diagnosing cancer via gene expression analysis is a hot topic in cancer research. Objective: The study aimed to diagnose the accurate type of lung cancer and discover the pathogenic genes. Methods: In this study, Affinity Propagation (AP) clustering with similarity score was employed to each type of lung cancer and normal lung. After grouping genes, sparse group lasso was adopted to construct four binary classifiers and the voting strategy was used to integrate them. Results: This study screened six gene groups that may associate with different lung cancer subtypes among 73 genes groups, and identified three possible key pathogenic genes, KRAS, BRAF and VDR. Furthermore, this study achieved improved classification accuracies at minority classes SQ and COID in comparison with other four methods. Conclusion: We propose the AP clustering based sparse group lasso (AP-SGL), which provides an alternative for simultaneous diagnosis and gene selection for lung cancer.

Cancer Diagnosis and Disease Gene Identification via Statistical Machine Learning

2020 ◽  
Vol 15 ◽  
Author(s):  
Liuyuan Chen ◽  
Juntao Li ◽  
Mingming Chang
Keyword(s):  
Machine Learning ◽  
Support Vector Machine ◽  
Cancer Diagnosis ◽  
Gene Selection ◽  
Disease Gene ◽  
Group Lasso ◽  
Elastic Net ◽  
Support Vector ◽  
Multinomial Regression ◽  
Statistical Machine Learning

: Diagnosing cancer and identifying the disease gene by using DNA microarray gene expression data are the hot topics in current bioinformatics. This paper is devoted to the latest development of cancer diagnosis and gene selection via statistical machine learning. Support vector machine is firstly introduced for the binary cancer diagnosis. Then, 1_norm support vector machine, doubly regularized support vector machine, adaptive huberized support vector machine and other extensions are presented to improve the performance of gene selection. Lasso, elastic net, partly adaptive elastic net, group lasso, sparse group lasso, adaptive sparse group lasso and other sparse regression methods are also introduced for performing simultaneous binary cancer classification and gene selection. In addition to introducing three strategies for reducing multiclass to binary, methods of directly considering all classes of data in a learning model (multi_class support vector, sparse multinomial regression, adaptive multinomial regression and so on) are presented for performing multiple cancer diagnosis. Limitations and promising directions are also discussed.

Inverse Sparse Group Lasso Model for Robust Object Tracking

2017 ◽  
Vol 19 (8) ◽  
pp. 1798-1810 ◽  
Author(s):  
Yun Zhou ◽  
Jianghong Han ◽  
Xiaohui Yuan ◽  
Zhenchun Wei ◽  
Richang Hong
Keyword(s):  
Object Tracking ◽  
Group Lasso ◽  
Sparse Group Lasso

A Novel Method for Enhancing Bearing Fault Feature Based on Sparse Group Lasso

2021 ◽  
Author(s):  
Changkun Han ◽  
Wei Lu ◽  
Pengxin Wang ◽  
Liuyang Song ◽  
Huaqing Wang
Keyword(s):  
Group Lasso ◽  
Bearing Fault ◽  
Sparse Group Lasso ◽  
Feature Based ◽  
Novel Method
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