Lung cancer lesion detection in histopathology images using graph‐based sparse PCA network [PDF]
Neoplasia: An International Journal for Oncology Research, 2023 Early detection of lung cancer is critical for improvement of patient survival. To address the clinical need for efficacious treatments, genetically engineered mouse models (GEMM) have become integral in identifying and evaluating the molecular ...
Sundaresh Ram +11 more
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Dynamic Meta-data Network Sparse PCA for Cancer Subtype Biomarker Screening [PDF]
Frontiers in Genetics, 2022 Previous research shows that each type of cancer can be divided into multiple subtypes, which is one of the key reasons that make cancer difficult to cure.
Rui Miao +10 more
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A Guide for Sparse PCA: Model Comparison and Applications [PDF]
Psychometrika, 2021 PCA is a popular tool for exploring and summarizing multivariate data, especially those consisting of many variables. PCA, however, is often not simple to interpret, as the components are a linear combination of the variables. To address this issue, numerous methods have been proposed to sparsify the nonzero coefficients in the components, including ...
Rosember Guerra-Urzola +2 more
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Predicting DDI-induced pregnancy and neonatal ADRs using sparse PCA and stacking ensemble approach [PDF]
Journal of Integrative BioinformaticsPredicting Drug-Drug interaction (DDI)-induced adverse drug reactions (ADRs) using computational methods is challenging due to the availability of limited data samples, data sparsity, and high dimensionality.
Chaurasia Anushka, Kumar Deepak, Yogita
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MINIMAX BOUNDS FOR SPARSE PCA WITH NOISY HIGH-DIMENSIONAL DATA. [PDF]
Ann Stat, 2013 We study the problem of estimating the leading eigenvectors of a high-dimensional population covariance matrix based on independent Gaussian observations.
Birnbaum A +3 more
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Visual Object Tracking Using Structured Sparse PCA-Based Appearance Representation and Online Learning [PDF]
Sensors, 2018 Visual object tracking is a fundamental research area in the field of computer vision and pattern recognition because it can be utilized by various intelligent systems.
Gang-Joon Yoon +2 more
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AWGE-ESPCA: An edge sparse PCA model based on adaptive noise elimination regularization and weighted gene network for Hermetia illucens genomic data analysis. [PDF]
PLoS Computational BiologyHermetia illucens is an important insect resource. Studies have shown that exploring the effects of Cu2+-stressed on the growth and development of the Hermetia illucens genome holds significant scientific importance.
Rui Miao +4 more
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randPedPCA: rapid approximation of principal components from large pedigrees [PDF]
Genetics Selection EvolutionBackground Pedigrees continue to be extremely important in agriculture and conservation genetics, with the pedigrees of modern breeding programmes easily comprising millions of records.
Hanbin Lee +3 more
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SuSiE PCA: A scalable Bayesian variable selection technique for principal component analysis
iScience, 2023 Summary: Latent factor models, like principal component analysis (PCA), provide a statistical framework to infer low-rank representation in various biological contexts. However, feature selection is challenging when this low-rank structure manifests from
Dong Yuan, Nicholas Mancuso
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Hyperspectral Image Denoising via Nonlocal Spectral Sparse Subspace Representation
IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 2023 Hyperspectral image (HSI) denoising based on nonlocal subspace representation has attracted a lot of attention recently. However, most of the existing works mainly focus on refining the representation coefficient images (RCIs) using certain nonlocal ...
Hailin Wang +5 more
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