Data Mining of Microarray Datasets in Translational Neuroscience
Data mining involves the computational analysis of a plethora of publicly available datasets to generate new hypotheses that can be further validated by experiments for the improved understanding of the pathogenesis of neurodegenerative diseases. Although the number of sequencing datasets is on the...
Autores principales: | , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
MDPI
2023
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10527016/ https://www.ncbi.nlm.nih.gov/pubmed/37759919 http://dx.doi.org/10.3390/brainsci13091318 |
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author | O’Connor, Lance M. O’Connor, Blake A. Zeng, Jialiu Lo, Chih Hung |
author_facet | O’Connor, Lance M. O’Connor, Blake A. Zeng, Jialiu Lo, Chih Hung |
author_sort | O’Connor, Lance M. |
collection | PubMed |
description | Data mining involves the computational analysis of a plethora of publicly available datasets to generate new hypotheses that can be further validated by experiments for the improved understanding of the pathogenesis of neurodegenerative diseases. Although the number of sequencing datasets is on the rise, microarray analysis conducted on diverse biological samples represent a large collection of datasets with multiple web-based programs that enable efficient and convenient data analysis. In this review, we first discuss the selection of biological samples associated with neurological disorders, and the possibility of a combination of datasets, from various types of samples, to conduct an integrated analysis in order to achieve a holistic understanding of the alterations in the examined biological system. We then summarize key approaches and studies that have made use of the data mining of microarray datasets to obtain insights into translational neuroscience applications, including biomarker discovery, therapeutic development, and the elucidation of the pathogenic mechanisms of neurodegenerative diseases. We further discuss the gap to be bridged between microarray and sequencing studies to improve the utilization and combination of different types of datasets, together with experimental validation, for more comprehensive analyses. We conclude by providing future perspectives on integrating multi-omics, to advance precision phenotyping and personalized medicine for neurodegenerative diseases. |
format | Online Article Text |
id | pubmed-10527016 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2023 |
publisher | MDPI |
record_format | MEDLINE/PubMed |
spelling | pubmed-105270162023-09-28 Data Mining of Microarray Datasets in Translational Neuroscience O’Connor, Lance M. O’Connor, Blake A. Zeng, Jialiu Lo, Chih Hung Brain Sci Review Data mining involves the computational analysis of a plethora of publicly available datasets to generate new hypotheses that can be further validated by experiments for the improved understanding of the pathogenesis of neurodegenerative diseases. Although the number of sequencing datasets is on the rise, microarray analysis conducted on diverse biological samples represent a large collection of datasets with multiple web-based programs that enable efficient and convenient data analysis. In this review, we first discuss the selection of biological samples associated with neurological disorders, and the possibility of a combination of datasets, from various types of samples, to conduct an integrated analysis in order to achieve a holistic understanding of the alterations in the examined biological system. We then summarize key approaches and studies that have made use of the data mining of microarray datasets to obtain insights into translational neuroscience applications, including biomarker discovery, therapeutic development, and the elucidation of the pathogenic mechanisms of neurodegenerative diseases. We further discuss the gap to be bridged between microarray and sequencing studies to improve the utilization and combination of different types of datasets, together with experimental validation, for more comprehensive analyses. We conclude by providing future perspectives on integrating multi-omics, to advance precision phenotyping and personalized medicine for neurodegenerative diseases. MDPI 2023-09-14 /pmc/articles/PMC10527016/ /pubmed/37759919 http://dx.doi.org/10.3390/brainsci13091318 Text en © 2023 by the authors. https://creativecommons.org/licenses/by/4.0/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 (https://creativecommons.org/licenses/by/4.0/). |
spellingShingle | Review O’Connor, Lance M. O’Connor, Blake A. Zeng, Jialiu Lo, Chih Hung Data Mining of Microarray Datasets in Translational Neuroscience |
title | Data Mining of Microarray Datasets in Translational Neuroscience |
title_full | Data Mining of Microarray Datasets in Translational Neuroscience |
title_fullStr | Data Mining of Microarray Datasets in Translational Neuroscience |
title_full_unstemmed | Data Mining of Microarray Datasets in Translational Neuroscience |
title_short | Data Mining of Microarray Datasets in Translational Neuroscience |
title_sort | data mining of microarray datasets in translational neuroscience |
topic | Review |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10527016/ https://www.ncbi.nlm.nih.gov/pubmed/37759919 http://dx.doi.org/10.3390/brainsci13091318 |
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