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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

Examples of pairs of neighbouring voxels.
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Figure 4: Examples of pairs of neighbouring voxels.

Mentions: Moreover, we extract features from pairs of neighboring voxels in the gene expression maps. The neighboring voxels, for example, in Figure 4, include the horizontal pairs of cells (A, B) and (C, D), the vertical pairs of cells (A, C) and (B, D), and the diagonal pairs of cells (A, D) and (C, B). Among the 68 cells in Figure 1, there are 61 pairs of horizontal neighboring cells, 57 pairs of vertical neighboring cells, and 53 pairs of diagonal neighboring cells. So, we have a total of 171 (= 61 + 57 + 53) pairs of neighboring voxels. For each pair, we average the gene expression values and calculate the absolute value of the difference of the two cells. For example, for the pair of voxels (A, B), the average value is (A+B)/2 and the absolute value of difference is /A-B/. Thus, we totally extract 342 (= 171*2) features from the pairs of neighboring voxels.


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

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

Examples of pairs of neighbouring voxels.
© Copyright Policy - open-access
Related In: Results  -  Collection

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

Figure 4: Examples of pairs of neighbouring voxels.
Mentions: Moreover, we extract features from pairs of neighboring voxels in the gene expression maps. The neighboring voxels, for example, in Figure 4, include the horizontal pairs of cells (A, B) and (C, D), the vertical pairs of cells (A, C) and (B, D), and the diagonal pairs of cells (A, D) and (C, B). Among the 68 cells in Figure 1, there are 61 pairs of horizontal neighboring cells, 57 pairs of vertical neighboring cells, and 53 pairs of diagonal neighboring cells. So, we have a total of 171 (= 61 + 57 + 53) pairs of neighboring voxels. For each pair, we average the gene expression values and calculate the absolute value of the difference of the two cells. For example, for the pair of voxels (A, B), the average value is (A+B)/2 and the absolute value of difference is /A-B/. Thus, we totally extract 342 (= 171*2) features from the pairs of neighboring voxels.

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