Paper Info
Title
Neural Network and Random Forest Models in Protein Function Prediction
Abstract
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 <uri xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">https://github.com/TurkuNLP/CAFA3</uri> .
Year
DOI
Venue
2022
10.1109/TCBB.2020.3044230
IEEE/ACM Transactions on Computational Biology and Bioinformatics
Keywords
DocType
Volume
Databases, Protein,Neural Networks, Computer,Proteins,Sequence Alignment,Software
Journal
19
Issue
ISSN
Citations 
3
1545-5963
0
PageRank 
References 
Authors
0.34
24
8
Name
Order
Citations
PageRank
Kai Hakala1286.14
Suwisa Kaewphan262.54
Jari Björne384232.10
Farrokh Mehryary402.03
Hans Moen5174.11
Martti Tolvanen600.34
Tapio Salakoski71513106.70
Filip Ginter8128676.74