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DTW-MIC Coexpression Networks from Time-Course Data
When modeling coexpression networks from high-throughput time course data, Pearson Correlation Coefficient (PCC) is one of the most effective and popular similarity functions. However, its reliability is limited since it cannot capture non-linear interactions and time shifts. Here we propose to over...
Autores principales: | , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Public Library of Science
2016
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4816347/ https://www.ncbi.nlm.nih.gov/pubmed/27031641 http://dx.doi.org/10.1371/journal.pone.0152648 |
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author | Riccadonna, Samantha Jurman, Giuseppe Visintainer, Roberto Filosi, Michele Furlanello, Cesare |
author_facet | Riccadonna, Samantha Jurman, Giuseppe Visintainer, Roberto Filosi, Michele Furlanello, Cesare |
author_sort | Riccadonna, Samantha |
collection | PubMed |
description | When modeling coexpression networks from high-throughput time course data, Pearson Correlation Coefficient (PCC) is one of the most effective and popular similarity functions. However, its reliability is limited since it cannot capture non-linear interactions and time shifts. Here we propose to overcome these two issues by employing a novel similarity function, Dynamic Time Warping Maximal Information Coefficient (DTW-MIC), combining a measure taking care of functional interactions of signals (MIC) and a measure identifying time lag (DTW). By using the Hamming-Ipsen-Mikhailov (HIM) metric to quantify network differences, the effectiveness of the DTW-MIC approach is demonstrated on a set of four synthetic and one transcriptomic datasets, also in comparison to TimeDelay ARACNE and Transfer Entropy. |
format | Online Article Text |
id | pubmed-4816347 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2016 |
publisher | Public Library of Science |
record_format | MEDLINE/PubMed |
spelling | pubmed-48163472016-04-14 DTW-MIC Coexpression Networks from Time-Course Data Riccadonna, Samantha Jurman, Giuseppe Visintainer, Roberto Filosi, Michele Furlanello, Cesare PLoS One Research Article When modeling coexpression networks from high-throughput time course data, Pearson Correlation Coefficient (PCC) is one of the most effective and popular similarity functions. However, its reliability is limited since it cannot capture non-linear interactions and time shifts. Here we propose to overcome these two issues by employing a novel similarity function, Dynamic Time Warping Maximal Information Coefficient (DTW-MIC), combining a measure taking care of functional interactions of signals (MIC) and a measure identifying time lag (DTW). By using the Hamming-Ipsen-Mikhailov (HIM) metric to quantify network differences, the effectiveness of the DTW-MIC approach is demonstrated on a set of four synthetic and one transcriptomic datasets, also in comparison to TimeDelay ARACNE and Transfer Entropy. Public Library of Science 2016-03-31 /pmc/articles/PMC4816347/ /pubmed/27031641 http://dx.doi.org/10.1371/journal.pone.0152648 Text en © 2016 Riccadonna et al http://creativecommons.org/licenses/by/4.0/ This is an open access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0/) , which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. |
spellingShingle | Research Article Riccadonna, Samantha Jurman, Giuseppe Visintainer, Roberto Filosi, Michele Furlanello, Cesare DTW-MIC Coexpression Networks from Time-Course Data |
title | DTW-MIC Coexpression Networks from Time-Course Data |
title_full | DTW-MIC Coexpression Networks from Time-Course Data |
title_fullStr | DTW-MIC Coexpression Networks from Time-Course Data |
title_full_unstemmed | DTW-MIC Coexpression Networks from Time-Course Data |
title_short | DTW-MIC Coexpression Networks from Time-Course Data |
title_sort | dtw-mic coexpression networks from time-course data |
topic | Research Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4816347/ https://www.ncbi.nlm.nih.gov/pubmed/27031641 http://dx.doi.org/10.1371/journal.pone.0152648 |
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