scholarly journals Massively parallel amplitude-only Fourier neural network

2021 ◽  
Author(s):  
Volker J. Sorger
Keyword(s):  
Neural Network ◽  
Massively Parallel

Neural network implementations and speed-up on Massively Parallel machines

1992 ◽  
Vol 35 (1-5) ◽  
pp. 747-754
Author(s):  
M.E. Azema-Barac ◽  
A.N. Refenes
Keyword(s):  
Neural Network ◽  
Parallel Machines ◽  
Massively Parallel ◽  
Speed Up

Massively parallel architectures for large scale neural network simulations

1992 ◽  
Vol 3 (6) ◽  
pp. 876-888 ◽  
Author(s):  
Y. Fujimoto ◽  
N. Fukuda ◽  
T. Akabane
Keyword(s):  
Neural Network ◽  
Large Scale ◽  
Parallel Architectures ◽  
Massively Parallel ◽  
Massively Parallel Architectures ◽  
Network Simulations

scholarly journals Deciphering regulatory DNA sequences and noncoding genetic variants using neural network models of massively parallel reporter assays

PLoS ONE ◽  
2019 ◽  
Vol 14 (6) ◽  
pp. e0218073 ◽  
Author(s):  
Rajiv Movva ◽  
Peyton Greenside ◽  
Georgi K. Marinov ◽  
Surag Nair ◽  
Avanti Shrikumar ◽  
...  
Keyword(s):  
Neural Network ◽  
Dna Sequences ◽  
Genetic Variants ◽  
Network Models ◽  
Massively Parallel ◽  
Neural Network Models ◽  
Regulatory Dna Sequences ◽  
Reporter Assays ◽  
Regulatory Dna

scholarly journals SpiNNaker: A 1-W 18-Core System-on-Chip for Massively-Parallel Neural Network Simulation

2013 ◽  
Vol 48 (8) ◽  
pp. 1943-1953 ◽  
Author(s):  
Eustace Painkras ◽  
Luis A. Plana ◽  
Jim Garside ◽  
Steve Temple ◽  
Francesco Galluppi ◽  
...  
Keyword(s):  
Neural Network ◽  
Network Simulation ◽  
System On Chip ◽  
Massively Parallel ◽  
Core System ◽  
Neural Network Simulation ◽  
On Chip

Mapping of neural network models onto massively parallel hierarchical computer systems

1999 ◽  
Vol 45 (11) ◽  
pp. 919-929 ◽  
Author(s):  
S Mahapatra ◽  
R.N Mahapatra ◽  
B.N Chatterji
Keyword(s):  
Neural Network ◽  
Computer Systems ◽  
Network Models ◽  
Massively Parallel ◽  
Neural Network Models

scholarly journals Deciphering regulatory DNA sequences and noncoding genetic variants using neural network models of massively parallel reporter assays

2018 ◽  
Author(s):  
Rajiv Movva ◽  
Peyton Greenside ◽  
Georgi K. Marinov ◽  
Surag Nair ◽  
Avanti Shrikumar ◽  
...  
Keyword(s):  
Neural Network ◽  
Dna Sequences ◽  
Genetic Variants ◽  
Massively Parallel ◽  
Regulatory Sequence ◽  
Regulatory Activity ◽  
Neural Network Models ◽  
Regulatory Dna Sequences ◽  
Reporter Assays ◽  
Regulatory Dna

AbstractThe relationship between noncoding DNA sequence and gene expression is not well-understood. Massively parallel reporter assays (MPRAs), which quantify the regulatory activity of large libraries of DNA sequences in parallel, are a powerful approach to characterize this relationship. We present MPRA-DragoNN, a convolutional neural network (CNN)-based framework to predict and interpret the regulatory activity of DNA sequences as measured by MPRAs. While our method is generally applicable to a variety of MPRA designs, here we trained our model on the Sharpr-MPRA dataset that measures the activity of ~500,000 constructs tiling 15,720 regulatory regions in human K562 and HepG2 cell lines. MPRA-DragoNN predictions were moderately correlated (Spearman ρ = 0.28) with measured activity and were within range of replicate concordance of the assay. State-of-the-art model interpretation methods revealed high-resolution predictive regulatory sequence features that overlapped transcription factor (TF) binding motifs. We used the model to investigate the cell type and chromatin state preferences of predictive TF motifs. We explored the ability of our model to predict the allelic effects of regulatory variants in an independent MPRA experiment and fine map putative functional SNPs in loci associated with lipid traits. Our results suggest that interpretable deep learning models trained on MPRA data have the potential to reveal meaningful patterns in regulatory DNA sequences and prioritize regulatory genetic variants, especially as larger, higher-quality datasets are produced.

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