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A classification scoring schema to validate protein interactors

Hypothetical protein [HP] annotation poses a great challenge especially when the protein is putatively linked or mapped to another protein. With protein interaction networks (PIN) prevailing, many visualizers still remain unsupported to the HP annotation. Through this work, we propose a six-point cl...

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Detalles Bibliográficos
Autores principales: Suravajhala, Prashanth, Sundararajan, Vijayaraghava Seshadri
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Biomedical Informatics 2012
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3282273/
https://www.ncbi.nlm.nih.gov/pubmed/22359432
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author Suravajhala, Prashanth
Sundararajan, Vijayaraghava Seshadri
author_facet Suravajhala, Prashanth
Sundararajan, Vijayaraghava Seshadri
author_sort Suravajhala, Prashanth
collection PubMed
description Hypothetical protein [HP] annotation poses a great challenge especially when the protein is putatively linked or mapped to another protein. With protein interaction networks (PIN) prevailing, many visualizers still remain unsupported to the HP annotation. Through this work, we propose a six-point classification system to validate protein interactions based on diverse features. The HP data-set was used as a training data-set to find putative functional interaction partners to the remaining proteins that are waiting to be interacting. A Total Reliability Score (TRS) was calculated based on the six-point classification which was evaluated using machine learning algorithm on a single node. We found that multilayer perceptron of neural network yielded 81.08% of accuracy in modelling TRS whereas feature selection algorithms confirmed that all classification features are implementable. Furthermore statistical results using variance and co-variance analyses confirmed the usefulness of these classification metrics. It has been evaluated that of all the classification features, subcellular location (sorting signals) makes higher impact in predicting the function of HPs.
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spelling pubmed-32822732012-02-22 A classification scoring schema to validate protein interactors Suravajhala, Prashanth Sundararajan, Vijayaraghava Seshadri Bioinformation Hypothesis Hypothetical protein [HP] annotation poses a great challenge especially when the protein is putatively linked or mapped to another protein. With protein interaction networks (PIN) prevailing, many visualizers still remain unsupported to the HP annotation. Through this work, we propose a six-point classification system to validate protein interactions based on diverse features. The HP data-set was used as a training data-set to find putative functional interaction partners to the remaining proteins that are waiting to be interacting. A Total Reliability Score (TRS) was calculated based on the six-point classification which was evaluated using machine learning algorithm on a single node. We found that multilayer perceptron of neural network yielded 81.08% of accuracy in modelling TRS whereas feature selection algorithms confirmed that all classification features are implementable. Furthermore statistical results using variance and co-variance analyses confirmed the usefulness of these classification metrics. It has been evaluated that of all the classification features, subcellular location (sorting signals) makes higher impact in predicting the function of HPs. Biomedical Informatics 2012-01-06 /pmc/articles/PMC3282273/ /pubmed/22359432 Text en © 2012 Biomedical Informatics This is an open-access article, which permits unrestricted use, distribution, and reproduction in any medium, for non-commercial purposes, provided the original author and source are credited.
spellingShingle Hypothesis
Suravajhala, Prashanth
Sundararajan, Vijayaraghava Seshadri
A classification scoring schema to validate protein interactors
title A classification scoring schema to validate protein interactors
title_full A classification scoring schema to validate protein interactors
title_fullStr A classification scoring schema to validate protein interactors
title_full_unstemmed A classification scoring schema to validate protein interactors
title_short A classification scoring schema to validate protein interactors
title_sort classification scoring schema to validate protein interactors
topic Hypothesis
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3282273/
https://www.ncbi.nlm.nih.gov/pubmed/22359432
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