Diagnostic Prediction Based on Gene Expression Profiles and Artificial Neural Networks

2018 ◽  
pp. 13-22
Author(s):  
Eugene Lin ◽  
Shih-Jen Tsai
Keyword(s):  
Gene Expression ◽  
Neural Networks ◽  
Artificial Neural Networks ◽  
Expression Profiles ◽  
Gene Expression Profiles ◽  
Artificial Neural

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

scholarly journals Erratum to: “47glioblastoma Gene Expression Profile Diagnostics by the Artificial Neural Networks”

2010 ◽  
Vol 19 (4) ◽  
pp. 344-344
Author(s):  
Y. A. Kuperin ◽  
A. A. Mekler ◽  
I. Kniazeva ◽  
D. R. Schwartz ◽  
V. V. Dmitrenko ◽  
...  
Keyword(s):  
Gene Expression ◽  
Neural Networks ◽  
Artificial Neural Networks ◽  
Gene Expression Profile ◽  
Expression Profile ◽  
Artificial Neural

scholarly journals On transformative adaptive activation functions in neural networks for gene expression inference

PLoS ONE ◽  
2021 ◽  
Vol 16 (1) ◽  
pp. e0243915
Author(s):  
Vladimír Kunc ◽  
Jiří Kléma
Keyword(s):  
Gene Expression ◽  
Neural Networks ◽  
Mean Absolute Error ◽  
Expression Profiles ◽  
Gene Expression Profiles ◽  
Cost Effective ◽  
Absolute Error ◽  
Activation Function ◽  
Training Procedure ◽  
Activation Functions

Gene expression profiling was made more cost-effective by the NIH LINCS program that profiles only ∼1, 000 selected landmark genes and uses them to reconstruct the whole profile. The D–GEX method employs neural networks to infer the entire profile. However, the original D–GEX can be significantly improved. We propose a novel transformative adaptive activation function that improves the gene expression inference even further and which generalizes several existing adaptive activation functions. Our improved neural network achieves an average mean absolute error of 0.1340, which is a significant improvement over our reimplementation of the original D–GEX, which achieves an average mean absolute error of 0.1637. The proposed transformative adaptive function enables a significantly more accurate reconstruction of the full gene expression profiles with only a small increase in the complexity of the model and its training procedure compared to other methods.

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