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Biomarker Prioritisation and Power Estimation Using Ensemble Gene Regulatory Network Inference
Inferring the topology of a gene regulatory network (GRN) from gene expression data is a challenging but important undertaking for gaining a better understanding of gene regulation. Key challenges include working with noisy data and dealing with a higher number of genes than samples. Although a numb...
Autores principales: | , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
MDPI
2020
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7660606/ https://www.ncbi.nlm.nih.gov/pubmed/33114263 http://dx.doi.org/10.3390/ijms21217886 |
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author | Aziz, Furqan Acharjee, Animesh Williams, John A. Russ, Dominic Bravo-Merodio, Laura Gkoutos, Georgios V. |
author_facet | Aziz, Furqan Acharjee, Animesh Williams, John A. Russ, Dominic Bravo-Merodio, Laura Gkoutos, Georgios V. |
author_sort | Aziz, Furqan |
collection | PubMed |
description | Inferring the topology of a gene regulatory network (GRN) from gene expression data is a challenging but important undertaking for gaining a better understanding of gene regulation. Key challenges include working with noisy data and dealing with a higher number of genes than samples. Although a number of different methods have been proposed to infer the structure of a GRN, there are large discrepancies among the different inference algorithms they adopt, rendering their meaningful comparison challenging. In this study, we used two methods, namely the MIDER (Mutual Information Distance and Entropy Reduction) and the PLSNET (Partial least square based feature selection) methods, to infer the structure of a GRN directly from data and computationally validated our results. Both methods were applied to different gene expression datasets resulting from inflammatory bowel disease (IBD), pancreatic ductal adenocarcinoma (PDAC), and acute myeloid leukaemia (AML) studies. For each case, gene regulators were successfully identified. For example, for the case of the IBD dataset, the UGT1A family genes were identified as key regulators while upon analysing the PDAC dataset, the SULF1 and THBS2 genes were depicted. We further demonstrate that an ensemble-based approach, that combines the output of the MIDER and PLSNET algorithms, can infer the structure of a GRN from data with higher accuracy. We have also estimated the number of the samples required for potential future validation studies. Here, we presented our proposed analysis framework that caters not only to candidate regulator genes prediction for potential validation experiments but also an estimation of the number of samples required for these experiments. |
format | Online Article Text |
id | pubmed-7660606 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2020 |
publisher | MDPI |
record_format | MEDLINE/PubMed |
spelling | pubmed-76606062020-11-13 Biomarker Prioritisation and Power Estimation Using Ensemble Gene Regulatory Network Inference Aziz, Furqan Acharjee, Animesh Williams, John A. Russ, Dominic Bravo-Merodio, Laura Gkoutos, Georgios V. Int J Mol Sci Article Inferring the topology of a gene regulatory network (GRN) from gene expression data is a challenging but important undertaking for gaining a better understanding of gene regulation. Key challenges include working with noisy data and dealing with a higher number of genes than samples. Although a number of different methods have been proposed to infer the structure of a GRN, there are large discrepancies among the different inference algorithms they adopt, rendering their meaningful comparison challenging. In this study, we used two methods, namely the MIDER (Mutual Information Distance and Entropy Reduction) and the PLSNET (Partial least square based feature selection) methods, to infer the structure of a GRN directly from data and computationally validated our results. Both methods were applied to different gene expression datasets resulting from inflammatory bowel disease (IBD), pancreatic ductal adenocarcinoma (PDAC), and acute myeloid leukaemia (AML) studies. For each case, gene regulators were successfully identified. For example, for the case of the IBD dataset, the UGT1A family genes were identified as key regulators while upon analysing the PDAC dataset, the SULF1 and THBS2 genes were depicted. We further demonstrate that an ensemble-based approach, that combines the output of the MIDER and PLSNET algorithms, can infer the structure of a GRN from data with higher accuracy. We have also estimated the number of the samples required for potential future validation studies. Here, we presented our proposed analysis framework that caters not only to candidate regulator genes prediction for potential validation experiments but also an estimation of the number of samples required for these experiments. MDPI 2020-10-23 /pmc/articles/PMC7660606/ /pubmed/33114263 http://dx.doi.org/10.3390/ijms21217886 Text en © 2020 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/). |
spellingShingle | Article Aziz, Furqan Acharjee, Animesh Williams, John A. Russ, Dominic Bravo-Merodio, Laura Gkoutos, Georgios V. Biomarker Prioritisation and Power Estimation Using Ensemble Gene Regulatory Network Inference |
title | Biomarker Prioritisation and Power Estimation Using Ensemble Gene Regulatory Network Inference |
title_full | Biomarker Prioritisation and Power Estimation Using Ensemble Gene Regulatory Network Inference |
title_fullStr | Biomarker Prioritisation and Power Estimation Using Ensemble Gene Regulatory Network Inference |
title_full_unstemmed | Biomarker Prioritisation and Power Estimation Using Ensemble Gene Regulatory Network Inference |
title_short | Biomarker Prioritisation and Power Estimation Using Ensemble Gene Regulatory Network Inference |
title_sort | biomarker prioritisation and power estimation using ensemble gene regulatory network inference |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7660606/ https://www.ncbi.nlm.nih.gov/pubmed/33114263 http://dx.doi.org/10.3390/ijms21217886 |
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