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On Tower and Checkerboard Neural Network Architectures for Gene Expression Inference

The result's identifiers

  • Result code in 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>

  • Result on the web

    <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>

Alternative languages

  • Result language

    angličtina

  • Original language name

    On Tower and Checkerboard Neural Network Architectures for Gene Expression Inference

  • Original language description

    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.

  • Czech name

  • Czech description

Classification

  • Type

    J<sub>imp</sub> - Article in a specialist periodical, which is included in the Web of Science database

  • CEP classification

  • OECD FORD branch

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

Result continuities

  • Project

    <a href="/en/project/EF16_019%2F0000765" target="_blank" >EF16_019/0000765: Research Center for Informatics</a><br>

  • Continuities

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

Others

  • Publication year

    2020

  • Confidentiality

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

Data specific for result type

  • Name of the periodical

    BMC Genomics

  • ISSN

    1471-2164

  • e-ISSN

    1471-2164

  • Volume of the periodical

    21

  • Issue of the periodical within the volume

    5

  • Country of publishing house

    GB - UNITED KINGDOM

  • Number of pages

    11

  • Pages from-to

  • UT code for WoS article

    000601211700003

  • EID of the result in the Scopus database

    2-s2.0-85097603076