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Chromosome preference of disease genes and vectorization for the prediction of non-coding disease genes
Disease-related protein-coding genes have been widely studied, but disease-related non-coding genes remain largely unknown. This work introduces a new vector to represent diseases, and applies the newly vectorized data for a positive-unlabeled learning algorithm to predict and rank disease-related l...
Autores principales: | , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Impact Journals LLC
2017
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5668007/ https://www.ncbi.nlm.nih.gov/pubmed/29108274 http://dx.doi.org/10.18632/oncotarget.20481 |
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author | Peng, Hui Lan, Chaowang Liu, Yuansheng Liu, Tao Blumenstein, Michael Li, Jinyan |
author_facet | Peng, Hui Lan, Chaowang Liu, Yuansheng Liu, Tao Blumenstein, Michael Li, Jinyan |
author_sort | Peng, Hui |
collection | PubMed |
description | Disease-related protein-coding genes have been widely studied, but disease-related non-coding genes remain largely unknown. This work introduces a new vector to represent diseases, and applies the newly vectorized data for a positive-unlabeled learning algorithm to predict and rank disease-related long non-coding RNA (lncRNA) genes. This novel vector representation for diseases consists of two sub-vectors, one is composed of 45 elements, characterizing the information entropies of the disease genes distribution over 45 chromosome substructures. This idea is supported by our observation that some substructures (e.g., the chromosome 6 p-arm) are highly preferred by disease-related protein coding genes, while some (e.g., the 21 p-arm) are not favored at all. The second sub-vector is 30-dimensional, characterizing the distribution of disease gene enriched KEGG pathways in comparison with our manually created pathway groups. The second sub-vector complements with the first one to differentiate between various diseases. Our prediction method outperforms the state-of-the-art methods on benchmark datasets for prioritizing disease related lncRNA genes. The method also works well when only the sequence information of an lncRNA gene is known, or even when a given disease has no currently recognized long non-coding genes. |
format | Online Article Text |
id | pubmed-5668007 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2017 |
publisher | Impact Journals LLC |
record_format | MEDLINE/PubMed |
spelling | pubmed-56680072017-11-04 Chromosome preference of disease genes and vectorization for the prediction of non-coding disease genes Peng, Hui Lan, Chaowang Liu, Yuansheng Liu, Tao Blumenstein, Michael Li, Jinyan Oncotarget Research Paper Disease-related protein-coding genes have been widely studied, but disease-related non-coding genes remain largely unknown. This work introduces a new vector to represent diseases, and applies the newly vectorized data for a positive-unlabeled learning algorithm to predict and rank disease-related long non-coding RNA (lncRNA) genes. This novel vector representation for diseases consists of two sub-vectors, one is composed of 45 elements, characterizing the information entropies of the disease genes distribution over 45 chromosome substructures. This idea is supported by our observation that some substructures (e.g., the chromosome 6 p-arm) are highly preferred by disease-related protein coding genes, while some (e.g., the 21 p-arm) are not favored at all. The second sub-vector is 30-dimensional, characterizing the distribution of disease gene enriched KEGG pathways in comparison with our manually created pathway groups. The second sub-vector complements with the first one to differentiate between various diseases. Our prediction method outperforms the state-of-the-art methods on benchmark datasets for prioritizing disease related lncRNA genes. The method also works well when only the sequence information of an lncRNA gene is known, or even when a given disease has no currently recognized long non-coding genes. Impact Journals LLC 2017-08-24 /pmc/articles/PMC5668007/ /pubmed/29108274 http://dx.doi.org/10.18632/oncotarget.20481 Text en Copyright: © 2017 Peng et al. http://creativecommons.org/licenses/by/3.0/ This is an open-access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/3.0/) 3.0 (CC BY 3.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. |
spellingShingle | Research Paper Peng, Hui Lan, Chaowang Liu, Yuansheng Liu, Tao Blumenstein, Michael Li, Jinyan Chromosome preference of disease genes and vectorization for the prediction of non-coding disease genes |
title | Chromosome preference of disease genes and vectorization for the prediction of non-coding disease genes |
title_full | Chromosome preference of disease genes and vectorization for the prediction of non-coding disease genes |
title_fullStr | Chromosome preference of disease genes and vectorization for the prediction of non-coding disease genes |
title_full_unstemmed | Chromosome preference of disease genes and vectorization for the prediction of non-coding disease genes |
title_short | Chromosome preference of disease genes and vectorization for the prediction of non-coding disease genes |
title_sort | chromosome preference of disease genes and vectorization for the prediction of non-coding disease genes |
topic | Research Paper |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5668007/ https://www.ncbi.nlm.nih.gov/pubmed/29108274 http://dx.doi.org/10.18632/oncotarget.20481 |
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