Sparse group selection and analysis of function‐related residue for protein‐state recognition
Sign Up to like & getrecommendations! Published in 2022 at "Journal of Computational Chemistry"
DOI: 10.1002/jcc.26937
Abstract: Machine learning methods have helped to advance wide range of scientific and technological field in recent years, including computational chemistry. As the chemical systems could become complex with high dimension, feature selection could be critical… read more here.
Keywords: selected features; chemistry; feature; sparse group ... See more keywords
SGL-SVM: a novel method for tumor classification via support vector machine with sparse group Lasso.
Sign Up to like & getrecommendations! Published in 2019 at "Journal of theoretical biology"
DOI: 10.1016/j.jtbi.2019.110098
Abstract: At present, with the in-depth study of gene expression data, the significant role of tumor classification in clinical medicine has become more apparent. In particular, the sparse characteristics of gene expression data within and between… read more here.
Keywords: classification; sgl svm; group lasso; tumor classification ... See more keywords
A link-free sparse group variable selection method for single-index model
Sign Up to like & getrecommendations! Published in 2017 at "Journal of Applied Statistics"
DOI: 10.1080/02664763.2016.1254731
Abstract: ABSTRACT For regression problems with grouped covariates, we adapt the idea of sparse group lasso (SGL) [10] to the framework of the sufficient dimension reduction. Assuming that the regression falls into a single-index structure, we… read more here.
Keywords: group; method; group variable; single index ... See more keywords
Overcoming the inadaptability of sparse group lasso for data with various group structures by stacking
Sign Up to like & getrecommendations! Published in 2022 at "Bioinformatics"
DOI: 10.1093/bioinformatics/btab848
Abstract: MOTIVATION Efficiently identifying genes based on gene expression level have been studied to help to classify different cancer types and improve the prediction performance. Logistic regression model based on regularization technique is often one of… read more here.
Keywords: group; inadaptability sparse; feature; selection ... See more keywords
Sparse Group Representation Model for Motor Imagery EEG Classification
Sign Up to like & getrecommendations! Published in 2019 at "IEEE Journal of Biomedical and Health Informatics"
DOI: 10.1109/jbhi.2018.2832538
Abstract: A potential limitation of a motor imagery (MI) based brain-computer interface (BCI) is that it usually requires a relatively long time to record sufficient electroencephalogram (EEG) data for robust classifier training. The calibration burden during… read more here.
Keywords: representation; sparse group; model; classification ... See more keywords
A Fitted Sparse-Group Lasso for Genome-Based Evaluations
Sign Up to like & getrecommendations! Published in 2022 at "IEEE/ACM Transactions on Computational Biology and Bioinformatics"
DOI: 10.1109/tcbb.2022.3156805
Abstract: In life sciences, high-throughput techniques typically lead to high-dimensional data and often the number of covariates is much larger than the number of observations. This inherently comes with multicollinearity challenging a statistical analysis in a… read more here.
Keywords: group lasso; lasso genome; genome based; group ... See more keywords
Temporally Constrained Sparse Group Spatial Patterns for Motor Imagery BCI
Sign Up to like & getrecommendations! Published in 2019 at "IEEE Transactions on Cybernetics"
DOI: 10.1109/tcyb.2018.2841847
Abstract: Common spatial pattern (CSP)-based spatial filtering has been most popularly applied to electroencephalogram (EEG) feature extraction for motor imagery (MI) classification in brain–computer interface (BCI) application. The effectiveness of CSP is highly affected by the… read more here.
Keywords: motor imagery; time; constrained sparse; group spatial ... See more keywords
Sparse-Group Lasso for Graph Learning From Multi-Attribute Data
Sign Up to like & getrecommendations! Published in 2021 at "IEEE Transactions on Signal Processing"
DOI: 10.1109/tsp.2021.3057699
Abstract: We consider the problem of inferring the conditional independence graph (CIG) of high-dimensional Gaussian vectors from multi-attribute data. Most existing methods for graph estimation are based on single-attribute models where one associates a scalar random… read more here.
Keywords: multi attribute; sparse group; attribute data; graph ... See more keywords
Genetic Variants Detection Based on Weighted Sparse Group Lasso
Sign Up to like & getrecommendations! Published in 2020 at "Frontiers in Genetics"
DOI: 10.3389/fgene.2020.00155
Abstract: Identification of genetic variants associated with complex traits is a critical step for improving plant resistance and breeding. Although the majority of existing methods for variants detection have good predictive performance in the average case,… read more here.
Keywords: variants detection; genetic variants; group; group lasso ... See more keywords