A1 Alkuperäisartikkeli tieteellisessä aikakauslehdessä

Neural Network and Random Forest Models in Protein Function Prediction




Julkaisun tekijät: Hakala Kai, Kaewphan Suwisa, Björne Jari, Mehryary Farrokh, Moen Hans, Tolvanen Martti, Salakoski Tapi, Ginter Filip

Kustantaja: Institute of Electrical and Electronics Engineers Inc.

Julkaisuvuosi: 2020

Journal: IEEE/ACM Transactions on Computational Biology and Bioinformatics

Tietokannassa oleva lehden nimi: IEEE/ACM Transactions on Computational Biology and Bioinformatics

eISSN: 1557-9964

DOI: http://dx.doi.org/10.1109/TCBB.2020.3044230


Tiivistelmä

Over the past decade, the demand for automated protein function prediction has increased due to the volume of newly sequenced proteins. In this paper, we address the function prediction task by developing an ensemble system automatically assigning Gene Ontology (GO) terms to the given input protein sequence. We develop an ensemble system which combines the GO predictions made by random forest (RF) and neural network (NN) classifiers. Both RF and NN models rely on features derived from BLAST sequence alignments, taxonomy and protein signature analysis tools. In addition, we report on experiments with a NN model that directly analyzes the amino acid sequence as its sole input, using a convolutional layer. The Swiss-Prot database is used as the training and evaluation data. In the CAFA3 evaluation, which relies on experimental verification of the functional predictions, our submitted ensemble model demonstrates competitive performance ranking among top-10 best-performing systems out of over 100 submitted systems. In this paper, we evaluate and further improve the CAFA3-submitted system. Our machine learning models together with the data pre-processing and feature generation tools are publicly available as an open source software at https://github.com/TurkuNLP/CAFA3.


Ladattava julkaisu

This is an electronic reprint of the original article.
This reprint may differ from the original in pagination and typographic detail. Please cite the original version.




Last updated on 2021-24-06 at 08:09