scholarly journals Constructive Neural Networks to Predict Breast Cancer Outcome by Using Gene Expression Profiles

2010 ◽  
pp. 317-326 ◽  
Author(s):  
Daniel Urda ◽  
José Luis Subirats ◽  
Leo Franco ◽  
José Manuel Jerez
Keyword(s):  
Breast Cancer ◽  
Gene Expression ◽  
Neural Networks ◽  
Expression Profiles ◽  
Gene Expression Profiles ◽  
Constructive Neural Networks ◽  
Breast Cancer Outcome ◽  
Cancer Outcome

Identify Breast Cancer Subtypes by Gene Expression Profiles

2021 ◽  
Vol 2 (2) ◽  
pp. 165-175
Author(s):  
Grace S. Shieh ◽  
Chy-Huei Bai ◽  
Chih Lee
Keyword(s):  
Breast Cancer ◽  
Gene Expression ◽  
Expression Profiles ◽  
Gene Expression Profiles ◽  
Breast Cancer Subtypes ◽  
Cancer Subtypes

High-throughput single-cell RNA-seq data imputation and characterization with surrogate-assisted automated deep learning

2021 ◽  
Author(s):  
Xiangtao Li ◽  
Shaochuan Li ◽  
Lei Huang ◽  
Shixiong Zhang ◽  
Ka-chun Wong
Keyword(s):  
Gene Expression ◽  
Neural Networks ◽  
Single Cell ◽  
Deep Neural Networks ◽  
Expression Profiles ◽  
Marker Gene ◽  
Gene Expression Profiles ◽  
Underlying Mechanisms ◽  
Cell Data ◽  
Gene Expression Levels

Abstract Single-cell RNA sequencing (scRNA-seq) technologies have been heavily developed to probe gene expression profiles at single-cell resolution. Deep imputation methods have been proposed to address the related computational challenges (e.g. the gene sparsity in single-cell data). In particular, the neural architectures of those deep imputation models have been proven to be critical for performance. However, deep imputation architectures are difficult to design and tune for those without rich knowledge of deep neural networks and scRNA-seq. Therefore, Surrogate-assisted Evolutionary Deep Imputation Model (SEDIM) is proposed to automatically design the architectures of deep neural networks for imputing gene expression levels in scRNA-seq data without any manual tuning. Moreover, the proposed SEDIM constructs an offline surrogate model, which can accelerate the computational efficiency of the architectural search. Comprehensive studies show that SEDIM significantly improves the imputation and clustering performance compared with other benchmark methods. In addition, we also extensively explore the performance of SEDIM in other contexts and platforms including mass cytometry and metabolic profiling in a comprehensive manner. Marker gene detection, gene ontology enrichment and pathological analysis are conducted to provide novel insights into cell-type identification and the underlying mechanisms. The source code is available at https://github.com/li-shaochuan/SEDIM.

scholarly journals Cell Identity Codes: Understanding Cell Identity from Gene Expression Profiles using Deep Neural Networks

2019 ◽  
Vol 9 (1) ◽  
Author(s):  
Farzad Abdolhosseini ◽  
Behrooz Azarkhalili ◽  
Abbas Maazallahi ◽  
Aryan Kamal ◽  
Seyed Abolfazl Motahari ◽  
...  
Keyword(s):  
Gene Expression ◽  
Neural Networks ◽  
Deep Neural Networks ◽  
Expression Profiles ◽  
Gene Expression Profiles ◽  
Cell Identity

Gene expression profiles in premenopausal women with HR+ HER2− early breast cancer

2020 ◽  
Vol 138 ◽  
pp. S76
Author(s):  
H. Ni ◽  
A. Kurt ◽  
J. Kumbrink ◽  
A. Seiler ◽  
D. Mayr ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Gene Expression ◽  
Early Breast Cancer ◽  
Expression Profiles ◽  
Gene Expression Profiles ◽  
Premenopausal Women
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