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Learning pair-wise gene functional similarity by multiplex gene expression maps.

An L, Ling H, Obradovic Z, Smith DJ, Megalooikonomou V - BMC Bioinformatics (2012)

Bottom Line: We also detect the most significant single voxels and pairs of neighboring voxels and visualize them in the expression map image of a mouse brain.This work is very important for predicting functions of unknown genes.It also has broader applicability since the methodology can be applied to analyze any large-scale dataset without a target attribute and is not restricted to gene expressions.

View Article: PubMed Central - HTML - PubMed

Affiliation: Data Engineering Laboratory, Department of Computer and Information Sciences, Temple University, PA, USA. anli@temple.edu

ABSTRACT

Background: The relationships between the gene functional similarity and gene expression profile, and between gene function annotation and gene sequence have been studied extensively. However, not much work has considered the connection between gene functions and location of a gene's expression in the mammalian tissues. On the other hand, although unsupervised learning methods have been commonly used in functional genomics, supervised learning cannot be directly applied to a set of normal genes without having a target (class) attribute.

Results: Here, we propose a supervised learning methodology to predict pair-wise gene functional similarity from multiplex gene expression maps that provide information about the location of gene expression. The features are extracted from expression maps and the labels denote the functional similarities of pairs of genes. We make use of wavelet features, original expression values, difference and average values of neighboring voxels and other features to perform boosting analysis. The experimental results show that with increasing similarities of gene expression maps, the functional similarities are increased too. The model predicts the functional similarities between genes to a certain degree. The weights of the features in the model indicate the features that are more significant for this prediction.

Conclusions: By considering pairs of genes, we propose a supervised learning methodology to predict pair-wise gene functional similarity from multiplex gene expression maps. We also explore the relationship between similarities of gene maps and gene functions. By using AdaBoost coupled with our proposed weak classifier we analyze a large-scale gene expression dataset and predict gene functional similarities. We also detect the most significant single voxels and pairs of neighboring voxels and visualize them in the expression map image of a mouse brain. This work is very important for predicting functions of unknown genes. It also has broader applicability since the methodology can be applied to analyze any large-scale dataset without a target attribute and is not restricted to gene expressions.

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Related in: MedlinePlus

The most selected original voxels on MFEP specific subset with respect to cellular component (best viewed in color). The darker mark means that the voxel is more significant.
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Figure 8: The most selected original voxels on MFEP specific subset with respect to cellular component (best viewed in color). The darker mark means that the voxel is more significant.

Mentions: Because the 68 original features are gene expression values in the 68 voxels (Figure 1), we can visualize and locate these features in the mouse brain. For example, the most selected original voxels with respect to cellular component are shown in Figure 8 as D1, F3, F8, C9, D7, G4, and G9. In the figure, the darker mark indicates that the voxel is selected more frequently (in terms of sum of weights) and that is more significant in predicting the functional similarity of genes from the gene expression maps. The boosting experiment also selected features extracted from pairs of neighbour cells. The top selected such features were the average expression values of pairs of voxels: (F3, E3), (C9, C10), (C1, C2), and the absolute value of difference between pairs of voxels: (A5, A6), (D6, E7) as Figure 9 shows.


Learning pair-wise gene functional similarity by multiplex gene expression maps.

An L, Ling H, Obradovic Z, Smith DJ, Megalooikonomou V - BMC Bioinformatics (2012)

The most selected original voxels on MFEP specific subset with respect to cellular component (best viewed in color). The darker mark means that the voxel is more significant.
© Copyright Policy - open-access
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC3375633&req=5

Figure 8: The most selected original voxels on MFEP specific subset with respect to cellular component (best viewed in color). The darker mark means that the voxel is more significant.
Mentions: Because the 68 original features are gene expression values in the 68 voxels (Figure 1), we can visualize and locate these features in the mouse brain. For example, the most selected original voxels with respect to cellular component are shown in Figure 8 as D1, F3, F8, C9, D7, G4, and G9. In the figure, the darker mark indicates that the voxel is selected more frequently (in terms of sum of weights) and that is more significant in predicting the functional similarity of genes from the gene expression maps. The boosting experiment also selected features extracted from pairs of neighbour cells. The top selected such features were the average expression values of pairs of voxels: (F3, E3), (C9, C10), (C1, C2), and the absolute value of difference between pairs of voxels: (A5, A6), (D6, E7) as Figure 9 shows.

Bottom Line: We also detect the most significant single voxels and pairs of neighboring voxels and visualize them in the expression map image of a mouse brain.This work is very important for predicting functions of unknown genes.It also has broader applicability since the methodology can be applied to analyze any large-scale dataset without a target attribute and is not restricted to gene expressions.

View Article: PubMed Central - HTML - PubMed

Affiliation: Data Engineering Laboratory, Department of Computer and Information Sciences, Temple University, PA, USA. anli@temple.edu

ABSTRACT

Background: The relationships between the gene functional similarity and gene expression profile, and between gene function annotation and gene sequence have been studied extensively. However, not much work has considered the connection between gene functions and location of a gene's expression in the mammalian tissues. On the other hand, although unsupervised learning methods have been commonly used in functional genomics, supervised learning cannot be directly applied to a set of normal genes without having a target (class) attribute.

Results: Here, we propose a supervised learning methodology to predict pair-wise gene functional similarity from multiplex gene expression maps that provide information about the location of gene expression. The features are extracted from expression maps and the labels denote the functional similarities of pairs of genes. We make use of wavelet features, original expression values, difference and average values of neighboring voxels and other features to perform boosting analysis. The experimental results show that with increasing similarities of gene expression maps, the functional similarities are increased too. The model predicts the functional similarities between genes to a certain degree. The weights of the features in the model indicate the features that are more significant for this prediction.

Conclusions: By considering pairs of genes, we propose a supervised learning methodology to predict pair-wise gene functional similarity from multiplex gene expression maps. We also explore the relationship between similarities of gene maps and gene functions. By using AdaBoost coupled with our proposed weak classifier we analyze a large-scale gene expression dataset and predict gene functional similarities. We also detect the most significant single voxels and pairs of neighboring voxels and visualize them in the expression map image of a mouse brain. This work is very important for predicting functions of unknown genes. It also has broader applicability since the methodology can be applied to analyze any large-scale dataset without a target attribute and is not restricted to gene expressions.

Show MeSH
Related in: MedlinePlus