Determination of eukaryotic protein coding regions using neural networks and information theory

1992 ◽  
Vol 226 (2) ◽  
pp. 471-479 ◽  
Author(s):  
Robert Farber ◽  
Alan Lapedes ◽  
Karl Sirotkin
Keyword(s):  
Neural Networks ◽  
Information Theory ◽  
Protein Coding ◽  
Eukaryotic Protein ◽  
Coding Regions

An efficient sliding window strategy for accurate location of eukaryotic protein coding regions

2009 ◽  
Vol 39 (4) ◽  
pp. 392-395 ◽  
Author(s):  
Nini Rao ◽  
Xu Lei ◽  
Jianxiu Guo ◽  
Hao Huang ◽  
Zhenglong Ren
Keyword(s):  
Sliding Window ◽  
Protein Coding ◽  
Eukaryotic Protein ◽  
Coding Regions

Operational Determination of the Activated Sludge Process Using Neural Networks

1992 ◽  
Vol 26 (9-11) ◽  
pp. 2461-2464 ◽  
Author(s):  
R. D. Tyagi ◽  
Y. G. Du
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Steady State ◽  
Activated Sludge ◽  
Feedforward Neural Network ◽  
Training Data ◽  
Activated Sludge Process

A steady-statemathematical model of an activated sludgeprocess with a secondary settler was developed. With a limited number of training data samples obtained from the simulation at steady state, a feedforward neural network was established which exhibits an excellent capability for the operational prediction and determination.

scholarly journals The open targets post-GWAS analysis pipeline

2020 ◽  
Vol 36 (9) ◽  
pp. 2936-2937 ◽  
Author(s):  
Gareth Peat ◽  
William Jones ◽  
Michael Nuhn ◽  
José Carlos Marugán ◽  
William Newell ◽  
...  
Keyword(s):  
Drug Targets ◽  
Gene Expression Regulation ◽  
Association Studies ◽  
Genome Wide Association Studies ◽  
Protein Coding ◽  
Data Resource ◽  
Coding Regions ◽  
Genome Wide ◽  
Causal Genes ◽  
Interactive Data

Abstract Motivation Genome-wide association studies (GWAS) are a powerful method to detect even weak associations between variants and phenotypes; however, many of the identified associated variants are in non-coding regions, and presumably influence gene expression regulation. Identifying potential drug targets, i.e. causal protein-coding genes, therefore, requires crossing the genetics results with functional data. Results We present a novel data integration pipeline that analyses GWAS results in the light of experimental epigenetic and cis-regulatory datasets, such as ChIP-Seq, Promoter-Capture Hi-C or eQTL, and presents them in a single report, which can be used for inferring likely causal genes. This pipeline was then fed into an interactive data resource. Availability and implementation The analysis code is available at www.github.com/Ensembl/postgap and the interactive data browser at postgwas.opentargets.io.

scholarly journals Determination of the geophysical model function of the ERS-1 scatterometer by the use of neural networks

1998 ◽  
Vol 103 (C6) ◽  
pp. 12853-12868 ◽  
Author(s):  
Carlos Mejia ◽  
Sylvie Thiria ◽  
Ngan Tran ◽  
Michel Crépon ◽  
Fouad Badran
Keyword(s):  
Neural Networks ◽  
Model Function ◽  
Geophysical Model ◽  
Geophysical Model Function
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