article

Molecular Function Prediction Using Neighborhood Features

Authors:

Petko Bogdanov,

Ambuj K. SinghAuthors Info & Claims

IEEE/ACM Transactions on Computational Biology and Bioinformatics (TCBB), Volume 7, Issue 2

Pages 208 - 217

https://doi.org/10.1109/TCBB.2009.81

Published: 01 April 2010 Publication History

Abstract

The recent advent of high-throughput methods has generated large amounts of gene interaction data. This has allowed the construction of genomewide networks. A significant number of genes in such networks remain uncharacterized and predicting the molecular function of these genes remains a major challenge. A number of existing techniques assume that genes with similar functions are topologically close in the network. Our hypothesis is that genes with similar functions observe similar annotation patterns in their neighborhood, regardless of the distance between them in the interaction network. We thus predict molecular functions of uncharacterized genes by comparing their functional neighborhoods to genes of known function. We propose a two-phase approach. First, we extract functional neighborhood features of a gene using Random Walks with Restarts. We then employ a KNN classifier to predict the function of uncharacterized genes based on the computed neighborhood features. We perform leave-one-out validation experiments on two S. cerevisiae interaction networks and show significant improvements over previous techniques. Our technique provides a natural control of the trade-off between accuracy and coverage of prediction. We further propose and evaluate prediction in sparse genomes by exploiting features from well-annotated genomes.

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Cited By

Kwon SYoon S(2019)End-to-End Representation Learning for Chemical-Chemical Interaction PredictionIEEE/ACM Transactions on Computational Biology and Bioinformatics10.1109/TCBB.2018.286414916:5(1436-1447)Online publication date: 1-Sep-2019
https://dl.acm.org/doi/10.1109/TCBB.2018.2864149
Frasca MBianchi N(2019)Multitask Protein Function Prediction through Task DissimilarityIEEE/ACM Transactions on Computational Biology and Bioinformatics10.1109/TCBB.2017.268412716:5(1550-1560)Online publication date: 1-Sep-2019
https://dl.acm.org/doi/10.1109/TCBB.2017.2684127
Taha K(2018)Inferring the Functions of Proteins from the Interrelationships between Functional CategoriesIEEE/ACM Transactions on Computational Biology and Bioinformatics10.1109/TCBB.2016.261560815:1(157-167)Online publication date: 1-Jan-2018
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Index Terms

Molecular Function Prediction Using Neighborhood Features
1. Applied computing
  1. Document management and text processing
  2. Life and medical sciences
2. Computing methodologies
  1. Machine learning
    1. Learning paradigms
      1. Supervised learning
        Supervised learning by classification
    2. Machine learning approaches
      1. Classification and regression trees

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cover image IEEE/ACM Transactions on Computational Biology and Bioinformatics

IEEE/ACM Transactions on Computational Biology and Bioinformatics Volume 7, Issue 2

April 2010

189 pages

ISSN:1545-5963

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Publisher

IEEE Computer Society Press

Washington, DC, United States

Publication History

Published: 01 April 2010

Published in TCBB Volume 7, Issue 2

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Kwon SYoon S(2019)End-to-End Representation Learning for Chemical-Chemical Interaction PredictionIEEE/ACM Transactions on Computational Biology and Bioinformatics10.1109/TCBB.2018.286414916:5(1436-1447)Online publication date: 1-Sep-2019
https://dl.acm.org/doi/10.1109/TCBB.2018.2864149
Frasca MBianchi N(2019)Multitask Protein Function Prediction through Task DissimilarityIEEE/ACM Transactions on Computational Biology and Bioinformatics10.1109/TCBB.2017.268412716:5(1550-1560)Online publication date: 1-Sep-2019
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Taha K(2018)Inferring the Functions of Proteins from the Interrelationships between Functional CategoriesIEEE/ACM Transactions on Computational Biology and Bioinformatics10.1109/TCBB.2016.261560815:1(157-167)Online publication date: 1-Jan-2018
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Maji PShah E(2017)Significance and Functional Similarity for Identification of Disease GenesIEEE/ACM Transactions on Computational Biology and Bioinformatics10.1109/TCBB.2016.259816314:6(1419-1433)Online publication date: 1-Nov-2017
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