Using Simple Gaussian Mixture Model for Multi-Class Classification Based on Tumor Gene Expression Data

2008 ◽  
Author(s):  
Wenlong Xu ◽  
Xianghua Zhang ◽  
Huanqing Feng
Keyword(s):  
Gene Expression ◽  
Gaussian Mixture Model ◽  
Mixture Model ◽  
Gene Expression Data ◽  
Gaussian Mixture ◽  
Expression Data ◽  
Multi Class Classification ◽  
Tumor Gene Expression ◽  
Tumor Gene

scholarly journals A Constrained Gaussian Mixture Model for Correlation-Based Cluster Analysis of Gene Expression Data

2009 ◽  
Vol 2 ◽  
pp. 47-62
Author(s):  
Naoto Yukinawa ◽  
Taku Yoshioka ◽  
Kazuo Kobayashi ◽  
Naotake Ogasawara ◽  
Shin Ishii
Keyword(s):  
Gene Expression ◽  
Cluster Analysis ◽  
Gaussian Mixture Model ◽  
Mixture Model ◽  
Gene Expression Data ◽  
Gaussian Mixture ◽  
Expression Data

Dual Hypergraph Regularized PCA for Biclustering of Tumor Gene Expression Data

2019 ◽  
Vol 31 (12) ◽  
pp. 2292-2303 ◽  
Author(s):  
Xuesong Wang ◽  
Jian Liu ◽  
Yuhu Cheng ◽  
Aiping Liu ◽  
Enhong Chen
Keyword(s):  
Gene Expression ◽  
Gene Expression Data ◽  
Expression Data ◽  
Tumor Gene Expression ◽  
Tumor Gene

scholarly journals Simultaneous enumeration of cancer and immune cell types from bulk tumor gene expression data

eLife ◽  
2017 ◽  
Vol 6 ◽  
Author(s):  
Julien Racle ◽  
Kaat de Jonge ◽  
Petra Baumgaertner ◽  
Daniel E Speiser ◽  
David Gfeller
Keyword(s):  
Gene Expression ◽  
Gene Expression Data ◽  
Immune Cell ◽  
Expression Profiles ◽  
Cell Types ◽  
Response To Therapy ◽  
Expression Data ◽  
Cell Type ◽  
Tumor Gene Expression ◽  
Tumor Gene

Immune cells infiltrating tumors can have important impact on tumor progression and response to therapy. We present an efficient algorithm to simultaneously estimate the fraction of cancer and immune cell types from bulk tumor gene expression data. Our method integrates novel gene expression profiles from each major non-malignant cell type found in tumors, renormalization based on cell-type-specific mRNA content, and the ability to consider uncharacterized and possibly highly variable cell types. Feasibility is demonstrated by validation with flow cytometry, immunohistochemistry and single-cell RNA-Seq analyses of human melanoma and colorectal tumor specimens. Altogether, our work not only improves accuracy but also broadens the scope of absolute cell fraction predictions from tumor gene expression data, and provides a unique novel experimental benchmark for immunogenomics analyses in cancer research (http://epic.gfellerlab.org).

scholarly journals Tumor gene expression data classification via sample expansion-based deep learning

Oncotarget ◽  
2017 ◽  
Vol 8 (65) ◽  
pp. 109646-109660 ◽  
Author(s):  
Jian Liu ◽  
Xuesong Wang ◽  
Yuhu Cheng ◽  
Lin Zhang
Keyword(s):  
Gene Expression ◽  
Deep Learning ◽  
Gene Expression Data ◽  
Data Classification ◽  
Expression Data ◽  
Tumor Gene Expression ◽  
Tumor Gene
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