Deep autoencoder neural networks for gene ontology annotation predictions

@article{Chicco2014DeepAN,
  title={Deep autoencoder neural networks for gene ontology annotation predictions},
  author={Davide Chicco and Peter Sadowski and Pierre Baldi},
  journal={Proceedings of the 5th ACM Conference on Bioinformatics, Computational Biology, and Health Informatics},
  year={2014}
}
  • D. Chicco, Peter Sadowski, P. Baldi
  • Published 2014
  • Computer Science
  • Proceedings of the 5th ACM Conference on Bioinformatics, Computational Biology, and Health Informatics
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… Expand
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References

SHOWING 1-10 OF 30 REFERENCES
Information theory applied to the sparse gene ontology annotation network to predict novel gene function
TLDR
A novel approach, information theory-based semantic similarity (ITSS), to automatically predict molecular functions of genes based on existing GO annotations is proposed, able to generate highly accurate predictions in sparsely annotated portions of GO, where previous algorithms have failed. Expand
Semantically improved genome-wide prediction of Gene Ontology annotations
TLDR
A novel prediction algorithm that incorporates gene clustering based on gene functional similarity computed on Gene Ontology annotations and tested both prediction methods performing k-fold cross-validation on two organism genomes. Expand
Probabilistic Latent Semantic Analysis for prediction of Gene Ontology annotations
TLDR
The effectiveness of the pLSAnorm prediction method is proved by performing k-fold cross-validation of the GO annotations of two organisms, Gallus gallus and Bos taurus, by using a modified Probabilistic Latent Semantic Analysis algorithm. Expand
Predicting gene function from patterns of annotation.
TLDR
The Gene Ontology (GO) Consortium has produced a controlled vocabulary for annotation of gene function that is used in many organism-specific gene annotation databases, and the relationships among GO attributes with decision trees and Bayesian networks are modeled. Expand
Predicting Novel Human Gene Ontology Annotations Using Semantic Analysis
TLDR
A technique is described that improves the previous method for predicting novel GO annotations by extracting implicit semantic relationships between genes and functions by using a vector space model and a number of weighting schemes in addition to the previous latent semantic indexing approach. Expand
Improved Biomolecular Annotation Prediction through Weighting Scheme Methods
Biomolecular annotation databases are very important in modern biomedical and genetic research. Correct interpretation of biological experiments depends on consistency and completeness of suchExpand
Protein Function Prediction with Incomplete Annotations
TLDR
This work proposes a Protein Function Prediction method with Weak-label Learning (ProWL) and its variant ProWL-IF, which can replenish the missing functions of proteins and makes use of the knowledge that a protein cannot have certain functions, to boost the performance of protein function prediction. Expand
Associating genes with gene ontology codes using a maximum entropy analysis of biomedical literature.
TLDR
It is concluded that statistical methods may be used to assign GO codes and may be useful for the difficult task of reassignment as terminology standards evolve over time. Expand
Latent Dirichlet Allocation based on Gibbs Sampling for gene function prediction
TLDR
Two variants of the known Latent Dirichlet Allocation algorithm applied to the prediction of gene annotations are proposed, using the collapsed Gibbs Sampling method during the training phase and two distinct initialization approaches to adapt the LDA mathematical model to the biomolecular annotation scenario. Expand
A semantic analysis of the annotations of the human genome
TLDR
The technique is able to identify missing and inaccurate annotations in existing annotation databases, and thus help improve their accuracy, and is used to analyze and improve the quality of the data of any public or private annotation database. Expand
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