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Prediction of Druggable Proteins Using Machine Learning and Systems Biology: A Mini-Review
The emergence of -omics technologies has allowed the collection of vast amounts of data on biological systems. Although, the pace of such collection has been exponential, the impact of these data remains small on many critical biomedical applications such as drug development. Limited resources, high...
Autores principales: | , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Frontiers Media S.A.
2015
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4672042/ https://www.ncbi.nlm.nih.gov/pubmed/26696900 http://dx.doi.org/10.3389/fphys.2015.00366 |
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author | Kandoi, Gaurav Acencio, Marcio L. Lemke, Ney |
author_facet | Kandoi, Gaurav Acencio, Marcio L. Lemke, Ney |
author_sort | Kandoi, Gaurav |
collection | PubMed |
description | The emergence of -omics technologies has allowed the collection of vast amounts of data on biological systems. Although, the pace of such collection has been exponential, the impact of these data remains small on many critical biomedical applications such as drug development. Limited resources, high costs, and low hit-to-lead ratio have led researchers to search for more cost effective methodologies. A possible alternative is to incorporate computational methods of potential drug target prediction early during drug discovery workflow. Computational methods based on systems approaches have the advantage of taking into account the global properties of a molecule not limited to its sequence, structure or function. Machine learning techniques are powerful tools that can extract relevant information from massive and noisy data sets. In recent years the scientific community has explored the combined power of these fields to propose increasingly accurate and low cost methods to propose interesting drug targets. In this mini-review, we describe promising approaches based on the simultaneous use of systems biology and machine learning to access gene and protein druggability. Moreover, we discuss the state-of-the-art of this emerging and interdisciplinary field, discussing data sources, algorithms and the performance of the different methodologies. Finally, we indicate interesting avenues of research and some remaining open challenges. |
format | Online Article Text |
id | pubmed-4672042 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2015 |
publisher | Frontiers Media S.A. |
record_format | MEDLINE/PubMed |
spelling | pubmed-46720422015-12-22 Prediction of Druggable Proteins Using Machine Learning and Systems Biology: A Mini-Review Kandoi, Gaurav Acencio, Marcio L. Lemke, Ney Front Physiol Physiology The emergence of -omics technologies has allowed the collection of vast amounts of data on biological systems. Although, the pace of such collection has been exponential, the impact of these data remains small on many critical biomedical applications such as drug development. Limited resources, high costs, and low hit-to-lead ratio have led researchers to search for more cost effective methodologies. A possible alternative is to incorporate computational methods of potential drug target prediction early during drug discovery workflow. Computational methods based on systems approaches have the advantage of taking into account the global properties of a molecule not limited to its sequence, structure or function. Machine learning techniques are powerful tools that can extract relevant information from massive and noisy data sets. In recent years the scientific community has explored the combined power of these fields to propose increasingly accurate and low cost methods to propose interesting drug targets. In this mini-review, we describe promising approaches based on the simultaneous use of systems biology and machine learning to access gene and protein druggability. Moreover, we discuss the state-of-the-art of this emerging and interdisciplinary field, discussing data sources, algorithms and the performance of the different methodologies. Finally, we indicate interesting avenues of research and some remaining open challenges. Frontiers Media S.A. 2015-12-08 /pmc/articles/PMC4672042/ /pubmed/26696900 http://dx.doi.org/10.3389/fphys.2015.00366 Text en Copyright © 2015 Kandoi, Acencio and Lemke. http://creativecommons.org/licenses/by/4.0/ This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms. |
spellingShingle | Physiology Kandoi, Gaurav Acencio, Marcio L. Lemke, Ney Prediction of Druggable Proteins Using Machine Learning and Systems Biology: A Mini-Review |
title | Prediction of Druggable Proteins Using Machine Learning and Systems Biology: A Mini-Review |
title_full | Prediction of Druggable Proteins Using Machine Learning and Systems Biology: A Mini-Review |
title_fullStr | Prediction of Druggable Proteins Using Machine Learning and Systems Biology: A Mini-Review |
title_full_unstemmed | Prediction of Druggable Proteins Using Machine Learning and Systems Biology: A Mini-Review |
title_short | Prediction of Druggable Proteins Using Machine Learning and Systems Biology: A Mini-Review |
title_sort | prediction of druggable proteins using machine learning and systems biology: a mini-review |
topic | Physiology |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4672042/ https://www.ncbi.nlm.nih.gov/pubmed/26696900 http://dx.doi.org/10.3389/fphys.2015.00366 |
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