SECLAF: a webserver and deep neural network design tool for hierarchical biological sequence classification

Bioinformatics
Balázs Szalkai, Vince Grolmusz

Abstract

Artificial intelligence tools are gaining more and more ground each year in bioinformatics. Learning algorithms can be taught for specific tasks by using the existing enormous biological databases, and the resulting models can be used for the high-quality classification of novel, un-categorized data in numerous areas, including biological sequence analysis. Here, we introduce SECLAF, a webserver that uses deep neural networks for hierarchical biological sequence classification. By applying SECLAF for residue-sequences, we have reported [Methods (2018), https://doi.org/10.1016/j.ymeth.2017.06.034] the most accurate multi-label protein classifier to date (UniProt-into 698 classes-AUC 99.99%; Gene Ontology-into 983 classes-AUC 99.45%). Our framework SECLAF can be applied for other sequence classification tasks, as we describe in the present contribution. The program SECLAF is implemented in Python, and is available for download, with example datasets at the website https://pitgroup.org/seclaf/. For Gene Ontology and UniProt based classifications a webserver is also available at the address above.

References

Oct 7, 2008·Nucleic Acids Research·UNKNOWN UniProt Consortium
Nov 28, 2014·Nucleic Acids Research·UNKNOWN Gene Ontology Consortium
May 3, 2016·Cell Systems·Ladislav Rampasek, Anna Goldenberg
Jul 8, 2017·Methods : a Companion to Methods in Enzymology·Balázs Szalkai, Vince Grolmusz

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Citations

May 16, 2020·Parasite : Journal De La Société Française De Parasitologie·Vivek Bhakta MathemaMallika Imwong
Feb 17, 2019·Journal of Neuroengineering and Rehabilitation·Francesca StivalManfredo Atzori
May 16, 2019·Scientific Reports·Ahmet Sureyya RifaiogluVolkan Atalay
Apr 13, 2021·Frontiers in Microbiology·Ze-Gang WeiShao-Wu Zhang
Aug 14, 2020·Journal of Chemical Information and Modeling·Runyu JingJiesi Luo

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