On Tower and Checkerboard Neural Network Architectures for Gene Expression Inference

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F68407700%3A21230%2F20%3A00340463" target="_blank" >RIV/68407700:21230/20:00340463 - isvavai.cz</a>

Výsledek na webu

<a href="https://doi.org/10.1186/s12864-020-06821-6" target="_blank" >https://doi.org/10.1186/s12864-020-06821-6</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1186/s12864-020-06821-6" target="_blank" >10.1186/s12864-020-06821-6</a>

Jazyk výsledku

angličtina

Název v původním jazyce

On Tower and Checkerboard Neural Network Architectures for Gene Expression Inference

Popis výsledku v původním jazyce

Background: One possible approach how to economically facilitate gene expression profiling is to use the L1000 platform which measures the expression of ~1,000 landmark genes and uses a computational method to infer the expression of another ~10,000 genes. One such method for the gene expression inference is a D-GEX which employs neural networks. Results: We propose two novel D-GEX architectures that significantly improve the quality of the inference by increasing the capacity of a network without any increase in the number of trained parameters. The architectures partition the network into individual towers. Our best proposed architecture - a checkerboard architecture with a skip connection and five towers - together with minor changes in the training protocol improves the average mean absolute error of the inference from 0.134 to 0.128. Conclusions: Our proposed approach increases the gene expression inference accuracy without increasing the number of weights of the model and thus without increasing the memory footprint of the model that is limiting its usage.

Název v anglickém jazyce

On Tower and Checkerboard Neural Network Architectures for Gene Expression Inference

Popis výsledku anglicky

Background: One possible approach how to economically facilitate gene expression profiling is to use the L1000 platform which measures the expression of ~1,000 landmark genes and uses a computational method to infer the expression of another ~10,000 genes. One such method for the gene expression inference is a D-GEX which employs neural networks. Results: We propose two novel D-GEX architectures that significantly improve the quality of the inference by increasing the capacity of a network without any increase in the number of trained parameters. The architectures partition the network into individual towers. Our best proposed architecture - a checkerboard architecture with a skip connection and five towers - together with minor changes in the training protocol improves the average mean absolute error of the inference from 0.134 to 0.128. Conclusions: Our proposed approach increases the gene expression inference accuracy without increasing the number of weights of the model and thus without increasing the memory footprint of the model that is limiting its usage.

Druh

J_imp - Článek v periodiku v databázi Web of Science

CEP obor

—

OECD FORD obor

10201 - Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8)

Projekt

<a href="/cs/project/EF16_019%2F0000765" target="_blank" >EF16_019/0000765: Výzkumné centrum informatiky</a><br>

Návaznosti

P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)<br>S - Specificky vyzkum na vysokych skolach

Rok uplatnění

2020

Kód důvěrnosti údajů

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Název periodika

BMC Genomics

ISSN

1471-2164

e-ISSN

1471-2164

Svazek periodika

21

Číslo periodika v rámci svazku

5

Stát vydavatele periodika

GB - Spojené království Velké Británie a Severního Irska

Počet stran výsledku

11

Strana od-do

—

Kód UT WoS článku

000601211700003

EID výsledku v databázi Scopus

2-s2.0-85097603076