short-paper

Deep autoencoder neural networks for gene ontology annotation predictions

Authors:

Peter Sadowski,

Pierre BaldiAuthors Info & Claims

BCB '14: Proceedings of the 5th ACM Conference on Bioinformatics, Computational Biology, and Health Informatics

Pages 533 - 540

https://doi.org/10.1145/2649387.2649442

Published: 20 September 2014 Publication History

Abstract

The annotation of genomic information is a major challenge in biology and bioinformatics. Existing databases of known gene functions are incomplete and prone to errors, and the bimolecular experiments needed to improve these databases are slow and costly. While computational methods are not a substitute for experimental verification, they can help in two ways: algorithms can aid in the curation of gene annotations by automatically suggesting inaccuracies, and they can predict previously-unidentified gene functions, accelerating the rate of gene function discovery. In this work, we develop an algorithm that achieves both goals using deep autoencoder neural networks. With experiments on gene annotation data from the Gene Ontology project, we show that deep autoencoder networks achieve better performance than other standard machine learning methods, including the popular truncated singular value decomposition.

References

[1]

G. Pandey, V. Kumar, and M. Steinbach, "Computational approaches for protein function prediction: A survey". Twin Cities: Department of Computer Science and Engineering, University of Minnesota, 2006.

Digital Library

[2]

The Gene Ontology Consortium, "Creating the Gene Ontology Resource: Design and Implementation". Genome Research, vol. 11, pp. 1425--1433, 2001.

[3]

M. Ashburner, C. A. Ball, J. A. Blake, D. Botstein, H. Butler, J. M. Cherry, A. P. Davis, K. Dolinski, S. S. Dwight, J. T. Eppig, M. A. Harris, D. P. Hill, L. Issel-Tarver, A. Kasarskis, S. Lewis, J. C. Matese, J. E. Richardson, M. Ringwald, G. M. Rubin, and G. Sherlock, "Gene Ontology: tool for the unification of biology". Nature Genetics, vol. 25.1: pp. 25--29, 2000.

[4]

O. D. King, R. E. Foulger, S. S. Dwight, J. V. White, and F. P. Roth, "Predicting gene function from patterns of annotation". Genome Research 13.5: pp. 896--904, 2003.

[5]

Y. Tao, L. Sam, J. Li, C. Friedman, and Y. A. Lussier, "Information theory applied to the sparse gene ontology annotation network to predict novel gene function". Bioinformatics, vol. 23.13: pp. 529--538, 2007.

Digital Library

[6]

Z. Barutcuoglu, R. E. Schapire, and O. G. Troyanskaya, "Hierarchical multi-label prediction of gene function". Bioinformatics, vol. 22.7: pp. 830--836, 2006.

Digital Library

[7]

S. Raychaudhuri, et al. "Associating genes with gene ontology codes using a maximum entropy analysis of biomedical literature". Genome Research, vol. 12.1: pp. 203--214, 2002.

[8]

A. Perez, C. Perez-Iratxeta, P. Bork, G. Thode, and M. A. Andrade, "Gene annotation from scientific literature using mappings between keyword systems". Bioinformatics, vol. 20.13: pp. 2084--2091, 2004.

Digital Library

[9]

G. Yu, H. Rangwala, C. Domeniconi, G. Zhang, and Z. Yu, "Protein Function Prediction with Incomplete Annotations". IEEE/ACM Transactions on Computational Biology and Bioinformatics, vol. 11.3: pp. 579--591, 2013.

Digital Library

[10]

P. Khatri, B. Done, A. Rao, A. Done, and S. Draghici, "A semantic analysis of the annotations of the human genome". Bioinformatics, vol. 21.16: pp. 3416--3421, 2005.

Digital Library

[11]

M. Masseroli, M. Tagliasacchi, and D. Chicco, "Semantically improved genome-wide prediction of Gene Ontology annotations". Proceedings of IEEE ISDA 2011, the 11th International Conference on Intelligent Systems Design and Applications, pp. 1080--1085, 2011.

[12]

P. Pinoli, D. Chicco, and M. Masseroli. "Improved biomolecular annotation prediction through Weighting Scheme methods". Proceedings of CIBB 2013, the Tenth International Meeting on Computational Intelligence Methods for Bioinformatics and Biostatistics, Nice, France, pp. 1--12, 2013.

[13]

H. Bourlard, and Y. Kamp, "Auto-association by multilayer perceptrons and singular value decomposition." Biological cybernetics, vol. 59.4-5, pp. 291--294, 1988.

Digital Library

[14]

P. Baldi and K. Hornik, "Neural networks and principal component analysis: Learning from examples without local minima." Neural networks, vol. 2.1, pp. 53--58, 1989.

Digital Library

[15]

P. Baldi, "Autoencoders, Unsupervised Learning, and Deep Architectures". Journal of Machine Learning Research-Proceedings Track, vol. 27, pp. 37--50, 2012.

[16]

G. H. Golub, and C. Reinsch, "Singular value decomposition and least squares solutions". Numerische Mathematik vol. 14.5: pp. 403--420, 1970.

Digital Library

[17]

M. Masseroli, M. Tagliasacchi, "Web resources for gene list analysis in biomedicine", In: Lazakidou, A., editor. Web-based Applications in Health Care and Biomedicine. Heidelberg, D: Springer, Annals of Information Systems Series, vol. 7, pp. 117--141, 2010

[18]

B. Done, P. Khatri, A. Done, and S. Draghici, "Semantic analysis of genome annotations using weighting schemes". Proceedings of CIBCB 2007, the IEEE Symposium Computational Intelligence and Bioinformatics and Computational Biology, pp. 212--218, 2007.

[19]

B. Done, P. Khatri, A. Done, and S. Draghici, "Predicting novel human gene ontology annotations using semantic analysis." IEEE/ACM Transactions on Computational Biology and Bioinformatics, vol. 7.1: pp. 91--99, 2010.

Digital Library

[20]

D. Chicco, and M. Masseroli, "A Discrete Optimization Approach for SVD Best Truncation Choice based on ROC Curves". Proceedings of IEEE BIBE 2013, the 13rd International Conference on Bioinformatics and Bioengineering, pp. 1--4, 2013.

[21]

R. Collobert, K. Kavukcuoglu and C. Farabet, "Torch7: A Matlab-like Environment for Machine Learning". BigLearn, NIPS Workshop, 2011.

[22]

A. Canakoglu, G. Ghisalberti, and M. Masseroli, "Integration of Biomolecular Interaction Data in a Genomic and Proteomic Data Warehouse to Support Biomedical Knowledge Discovery". Computational Intelligence Methods for Bioinformatics and Biostatistics, Springer Berlin Heidelberg, pp. 112--126, 2012.

[23]

F. Pessina, M. Masseroli, and A. Canakoglu, "Visual composition of complex queries on an integrative Genomic and Proteomic Data Warehouse". Engineering, vol. 5:10B, pp. 1--8, 2013.

[24]

D. Chicco, M. Tagliasacchi, and M. Masseroli, "Genomic annotation prediction based on integrated information". Computational Intelligence Methods for Bioinformatics and Biostatistics, Springer Berlin Heidelberg, pp. 238--252, 2012.

[25]

D. Chicco, M. Tagliasacchi, and M. Masseroli, "Biomolecular annotation prediction through information integration". Proceedings of CIBB 2011, the 8th Computational Intelligence Methods for Bioinformatics and Biostatistics, pp. 1--8, 2011.

[26]

M. Masseroli, D. Chicco, and P. Pinoli, "Probabilistic Latent Semantic Analysis for prediction of Gene Ontology annotations". Proceedings of IEEE IJCNN 2012, the International Joint Conference on Neural Networks, pp- 1--8 2012.

[27]

P. Pinoli, D. Chicco, and M. Masseroli, "Latent Dirichlet Allocation based on Gibbs Sampling for Gene Function Prediction". Proceedings of IEEE CIBCB 2014, the Conference on Computational Intelligence in Bioinformatics and Computational Biology, pp. 1--4, 2014.

[28]

S. Ceri, D. Braga, F. Corcoglioniti, M. Grossniklaus, and S. Vadacca, "Search computing challenges and directions". Springer, Berlin Heidelberg, 2010.

Digital Library

[29]

D. Chicco, "Integration of bioinformatics web services through the Search Computing technology". Technical Report, TR 2012/02. Dipartimento di Elettronica e Informazione, Politecnico di Milano, Milan, Italy.

Cited By

Tjärnberg ABeheler-Amass MJackson CChristiaen LGresham DBonneau R(2024)Structure-primed embedding on the transcription factor manifold enables transparent model architectures for gene regulatory network and latent activity inferenceGenome Biology10.1186/s13059-023-03134-125:1Online publication date: 18-Jan-2024
https://doi.org/10.1186/s13059-023-03134-1
Liu YZhang RDong XYang HLi JCao HTian JZhang Y(2024)DAE-CFR: detecting microRNA-disease associations using deep autoencoder and combined feature representationBMC Bioinformatics10.1186/s12859-024-05757-y25:1Online publication date: 29-Mar-2024
https://doi.org/10.1186/s12859-024-05757-y
Alfayez SBchir OBen Ismail M(2023)Dynamic Depth Learning in Stacked AutoEncodersApplied Sciences10.3390/app13191099413:19(10994)Online publication date: 5-Oct-2023
https://doi.org/10.3390/app131910994
Show More Cited By

Index Terms

Deep autoencoder neural networks for gene ontology annotation predictions

Recommendations

Analysis of cancer-related lncRNAs using gene ontology and KEGG pathways

We investigated cancer-related lncRNAs using GO and KEGG enrichment scores of the co-expressed neighbors of lncRNAs.The biological analysis confirmed the crucial cancer associated GO term and KEGG pathways we screened out.This study provided novel ...
Gene Ontology analysis in multiple gene clusters under multiple hypothesis testing framework

Objective: Gene Ontology (GO) has become a routine resource for functional analysis of gene lists. Although a number of tools have been provided to identify enriched GO terms in one or two gene lists, two technical challenges remain. First, how to ...
GOSAP: Gene Ontology-Based Semantic Alignment of Biological Pathways

We present a new method for semantic comparison of biological pathways, aiming to discover evolutionary conservation of pathways between species. Our method uses all three sub-ontologies of Gene Ontology (GO) and a measure of semantic similarity to ...

Comments

Information & Contributors

Information

Published In

cover image ACM Conferences

BCB '14: Proceedings of the 5th ACM Conference on Bioinformatics, Computational Biology, and Health Informatics

September 2014

851 pages

ISBN:9781450328944

DOI:10.1145/2649387

General Chairs:
Pierre Baldi
University of California, Irvine
,
Wei Wang
University of California, Los Angeles

Copyright © 2014 ACM.

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

Sponsors

SIGBio: ACM Special Interest Group on Bioinformatics

Publisher

Association for Computing Machinery

New York, NY, United States

Publication History

Published: 20 September 2014

Permissions

Request permissions for this article.

Request Permissions

Check for updates

Author Tags

Qualifiers

Short-paper

Conference

BCB '14

Sponsor:

SIGBio

BCB '14: ACM-BCB '14

September 20 - 23, 2014

California, Newport Beach

Acceptance Rates

Overall Acceptance Rate 254 of 885 submissions, 29%

Upcoming Conference

Contributors

Other Metrics

View Article Metrics

Bibliometrics & Citations

Bibliometrics

Article Metrics

93
Total Citations
View Citations
2,277
Total Downloads

Downloads (Last 12 months)91
Downloads (Last 6 weeks)10

Reflects downloads up to 06 Oct 2024

Other Metrics

View Author Metrics

Citations

Cited By

Tjärnberg ABeheler-Amass MJackson CChristiaen LGresham DBonneau R(2024)Structure-primed embedding on the transcription factor manifold enables transparent model architectures for gene regulatory network and latent activity inferenceGenome Biology10.1186/s13059-023-03134-125:1Online publication date: 18-Jan-2024
https://doi.org/10.1186/s13059-023-03134-1
Liu YZhang RDong XYang HLi JCao HTian JZhang Y(2024)DAE-CFR: detecting microRNA-disease associations using deep autoencoder and combined feature representationBMC Bioinformatics10.1186/s12859-024-05757-y25:1Online publication date: 29-Mar-2024
https://doi.org/10.1186/s12859-024-05757-y
Alfayez SBchir OBen Ismail M(2023)Dynamic Depth Learning in Stacked AutoEncodersApplied Sciences10.3390/app13191099413:19(10994)Online publication date: 5-Oct-2023
https://doi.org/10.3390/app131910994
Rahaie ZRabiee HAlinejad-Rokny H(2023)DeepGenePrior: A deep learning model for prioritizing genes affected by copy number variantsPLOS Computational Biology10.1371/journal.pcbi.101124919:7(e1011249)Online publication date: 24-Jul-2023
https://doi.org/10.1371/journal.pcbi.1011249
Mallampati SSeetha H(2023)An Integrated Feature Extraction Based on Principal Components and Deep Auto Encoder with Extra Tree for Intrusion Detection SystemsJournal of Information & Knowledge Management10.1142/S021964922350066123:01Online publication date: 17-Nov-2023
https://doi.org/10.1142/S0219649223500661
Zhu MHuo XZhang ZLiu JJi J(2023)Arrhythmia Detection from Electrocardiogram Signal Data Based on Wavelet Transform and Deep Reinforcement Learning2023 IEEE International Conference on Medical Artificial Intelligence (MedAI)10.1109/MedAI59581.2023.00051(325-333)Online publication date: 18-Nov-2023
https://doi.org/10.1109/MedAI59581.2023.00051
Jhee JSong MKim BShin HLee S(2023)Transformer-Based Gene Scoring Model for Extracting Representative Characteristic of Central Dogma Process to Prioritize Pathogenic Genes Applying Breast Cancer Multi-omics Data2023 IEEE International Conference on Big Data and Smart Computing (BigComp)10.1109/BigComp57234.2023.00033(149-154)Online publication date: Feb-2023
https://doi.org/10.1109/BigComp57234.2023.00033
Li LMorgantini MBetti R(2023)Structural damage assessment through a new generalized autoencoder with features in the quefrency domainMechanical Systems and Signal Processing10.1016/j.ymssp.2022.109713184(109713)Online publication date: Feb-2023
https://doi.org/10.1016/j.ymssp.2022.109713
Ogunsanya MDesai S(2023)Physics-based and data-driven modeling for biomanufacturing 4.0Manufacturing Letters10.1016/j.mfglet.2023.04.003Online publication date: May-2023
https://doi.org/10.1016/j.mfglet.2023.04.003
Teng ZZhang YDai QWu CLi D(2023)Constructing discriminative feature space for LncRNA–protein interaction based on deep autoencoder and marginal fisher analysisComputers in Biology and Medicine10.1016/j.compbiomed.2023.106711157:COnline publication date: 1-May-2023
https://dl.acm.org/doi/10.1016/j.compbiomed.2023.106711
Show More Cited By

View Options

Get Access

Login options

Check if you have access through your login credentials or your institution to get full access on this article.

Full Access

Get this Publication

View options

PDF

View or Download as a PDF file.

eReader

View online with eReader.

Media

Figures

Other

Tables

View Table of Contents