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An integrated network of Arabidopsis growth regulators and its use for gene prioritization.

Sabaghian E, Drebert Z, Inzé D, Saeys Y - Sci Rep (2015)

Bottom Line: Elucidating the molecular mechanisms that govern plant growth has been an important topic in plant research, and current advances in large-scale data generation call for computational tools that efficiently combine these different data sources to generate novel hypotheses.In this work, we present a novel, integrated network that combines multiple large-scale data sources to characterize growth regulatory genes in Arabidopsis, one of the main plant model organisms.In addition, the integrated network is made available to the scientific community, providing a rich data source that will be useful for many biological processes, not necessarily restricted to plant growth.

View Article: PubMed Central - PubMed

Affiliation: Department of Plant Systems Biology, VIB, 9052 Gent, Belgium.

ABSTRACT
Elucidating the molecular mechanisms that govern plant growth has been an important topic in plant research, and current advances in large-scale data generation call for computational tools that efficiently combine these different data sources to generate novel hypotheses. In this work, we present a novel, integrated network that combines multiple large-scale data sources to characterize growth regulatory genes in Arabidopsis, one of the main plant model organisms. The contributions of this work are twofold: first, we characterized a set of carefully selected growth regulators with respect to their connectivity patterns in the integrated network, and, subsequently, we explored to which extent these connectivity patterns can be used to suggest new growth regulators. Using a large-scale comparative study, we designed new supervised machine learning methods to prioritize growth regulators. Our results show that these methods significantly improve current state-of-the-art prioritization techniques, and are able to suggest meaningful new growth regulators. In addition, the integrated network is made available to the scientific community, providing a rich data source that will be useful for many biological processes, not necessarily restricted to plant growth.

No MeSH data available.


Related in: MedlinePlus

Comparing both the RF Model and the SVM Model to GeneMANIA.Pairwise comparison between (A) GeneMANIA and Random Forest (RF), and between (B) GeneMANIA and Support Vector Machines (SVM). Each dot represents a GR gene, and its coordinates correspond to the ranks assigned by the different methods (ranks are displayed on a logarithmic scale). (C) Comparison of the three best prioritization approaches to the state-of-the-art method GeneMANIA.
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f3: Comparing both the RF Model and the SVM Model to GeneMANIA.Pairwise comparison between (A) GeneMANIA and Random Forest (RF), and between (B) GeneMANIA and Support Vector Machines (SVM). Each dot represents a GR gene, and its coordinates correspond to the ranks assigned by the different methods (ranks are displayed on a logarithmic scale). (C) Comparison of the three best prioritization approaches to the state-of-the-art method GeneMANIA.

Mentions: In order to compare our results with a state-of-the-art gene prioritization tool, we ran GeneMANIA in the same LOOCV setting on our set of GR genes. Figure 3C compares GeneMANIA with the best model-based prioritization techniques, and a detailed overview of all ranking criteria is shown in the bottom part of Table 3. While the SVM model clearly outperformed all others only in terms of first quartile results, the RF model markedly outperformed GeneMANIA in terms of both first quartile and median rank. The difference between the RF model and GeneMANIA was significant at a 95% confidence level (Mann-Whitney test, P-value = 0.014).


An integrated network of Arabidopsis growth regulators and its use for gene prioritization.

Sabaghian E, Drebert Z, Inzé D, Saeys Y - Sci Rep (2015)

Comparing both the RF Model and the SVM Model to GeneMANIA.Pairwise comparison between (A) GeneMANIA and Random Forest (RF), and between (B) GeneMANIA and Support Vector Machines (SVM). Each dot represents a GR gene, and its coordinates correspond to the ranks assigned by the different methods (ranks are displayed on a logarithmic scale). (C) Comparison of the three best prioritization approaches to the state-of-the-art method GeneMANIA.
© Copyright Policy - open-access
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC4664945&req=5

f3: Comparing both the RF Model and the SVM Model to GeneMANIA.Pairwise comparison between (A) GeneMANIA and Random Forest (RF), and between (B) GeneMANIA and Support Vector Machines (SVM). Each dot represents a GR gene, and its coordinates correspond to the ranks assigned by the different methods (ranks are displayed on a logarithmic scale). (C) Comparison of the three best prioritization approaches to the state-of-the-art method GeneMANIA.
Mentions: In order to compare our results with a state-of-the-art gene prioritization tool, we ran GeneMANIA in the same LOOCV setting on our set of GR genes. Figure 3C compares GeneMANIA with the best model-based prioritization techniques, and a detailed overview of all ranking criteria is shown in the bottom part of Table 3. While the SVM model clearly outperformed all others only in terms of first quartile results, the RF model markedly outperformed GeneMANIA in terms of both first quartile and median rank. The difference between the RF model and GeneMANIA was significant at a 95% confidence level (Mann-Whitney test, P-value = 0.014).

Bottom Line: Elucidating the molecular mechanisms that govern plant growth has been an important topic in plant research, and current advances in large-scale data generation call for computational tools that efficiently combine these different data sources to generate novel hypotheses.In this work, we present a novel, integrated network that combines multiple large-scale data sources to characterize growth regulatory genes in Arabidopsis, one of the main plant model organisms.In addition, the integrated network is made available to the scientific community, providing a rich data source that will be useful for many biological processes, not necessarily restricted to plant growth.

View Article: PubMed Central - PubMed

Affiliation: Department of Plant Systems Biology, VIB, 9052 Gent, Belgium.

ABSTRACT
Elucidating the molecular mechanisms that govern plant growth has been an important topic in plant research, and current advances in large-scale data generation call for computational tools that efficiently combine these different data sources to generate novel hypotheses. In this work, we present a novel, integrated network that combines multiple large-scale data sources to characterize growth regulatory genes in Arabidopsis, one of the main plant model organisms. The contributions of this work are twofold: first, we characterized a set of carefully selected growth regulators with respect to their connectivity patterns in the integrated network, and, subsequently, we explored to which extent these connectivity patterns can be used to suggest new growth regulators. Using a large-scale comparative study, we designed new supervised machine learning methods to prioritize growth regulators. Our results show that these methods significantly improve current state-of-the-art prioritization techniques, and are able to suggest meaningful new growth regulators. In addition, the integrated network is made available to the scientific community, providing a rich data source that will be useful for many biological processes, not necessarily restricted to plant growth.

No MeSH data available.


Related in: MedlinePlus