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Identification of temporal association rules from time-series microarray data sets.

Nam H, Lee K, Lee D - BMC Bioinformatics (2009)

Bottom Line: From the extracted temporal association rules, associated genes, which play same role of biological processes within short transcriptional time delay and some temporal dependencies between genes with specific biological processes are identified.TARM showed higher precision score than Dynamic Bayesian network and Bayesian network.Advantages of TARM are that it tells us the size of transcriptional time delay between associated genes, activation and inhibition relationship between genes, and sets of co-regulators.

View Article: PubMed Central - HTML - PubMed

Affiliation: Department of Bio and Brain Engineering, KAIST, 373-1 Guseong-dong, Yuseong-gu, Daejeon, Korea. hjnam@kaist.ac.kr

ABSTRACT

Background: One of the most challenging problems in mining gene expression data is to identify how the expression of any particular gene affects the expression of other genes. To elucidate the relationships between genes, an association rule mining (ARM) method has been applied to microarray gene expression data. However, a conventional ARM method has a limit on extracting temporal dependencies between gene expressions, though the temporal information is indispensable to discover underlying regulation mechanisms in biological pathways. In this paper, we propose a novel method, referred to as temporal association rule mining (TARM), which can extract temporal dependencies among related genes. A temporal association rule has the form [gene A upward arrow, gene B downward arrow] --> (7 min) [gene C upward arrow], which represents that high expression level of gene A and significant repression of gene B followed by significant expression of gene C after 7 minutes. The proposed TARM method is tested with Saccharomyces cerevisiae cell cycle time-series microarray gene expression data set.

Results: In the parameter fitting phase of TARM, the fitted parameter set [threshold = +/- 0.8, support >or= 3 transactions, confidence >or= 90%] with the best precision score for KEGG cell cycle pathway has been chosen for rule mining phase. With the fitted parameter set, numbers of temporal association rules with five transcriptional time delays (0, 7, 14, 21, 28 minutes) are extracted from gene expression data of 799 genes, which are pre-identified cell cycle relevant genes. From the extracted temporal association rules, associated genes, which play same role of biological processes within short transcriptional time delay and some temporal dependencies between genes with specific biological processes are identified.

Conclusion: In this work, we proposed TARM, which is an applied form of conventional ARM. TARM showed higher precision score than Dynamic Bayesian network and Bayesian network. Advantages of TARM are that it tells us the size of transcriptional time delay between associated genes, activation and inhibition relationship between genes, and sets of co-regulators.

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An illustration of temporal association rule mining process. An illustration of temporal association rule mining process with transcriptional time delay Δ = 2, support ≥ 50%, confidence ≥ 50%.
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Figure 2: An illustration of temporal association rule mining process. An illustration of temporal association rule mining process with transcriptional time delay Δ = 2, support ≥ 50%, confidence ≥ 50%.

Mentions: Figure 2 shows an illustration of temporal association rule mining process. First, continuous gene expression values are converted into discrete values (up, down, and none) (Figure 2(a)). Second, to find temporally associated genes, we first assume that all related genes may have various sizes of transcriptional time delay. Therefore, our method searches associated genes in all possible sets of different time point experiments where the time interval is from 0 to n (Figure 2(b)). In this illustration, Δ is 2. For example, Temporal transaction set t0 + t2 = [g1L↑, g2L↓, g1R↑, g2R↑, g3R↓] consists of up or down regulated genes at time stamps t0 and t2 with the size of transcriptional time delay Δ = 2. Note that, for g1, it is up regulated in both cases of t0 and t2, but we marked them as two different genes like g1L (g1 in Left hand side) and g1R (g1 in Right hand side). Third, Figure 2(c) indicates the extracted temporal frequent item sets with support threshold 50%. And finally, two temporal association rules are discovered with confidence threshold 50% as shown in Figure 2(d). In this manner, TARM can find (1) various sizes of transcriptional time delay between associated genes, (2) activation and inhibition relationship, (3) sets of co-regulators for the target genes.


Identification of temporal association rules from time-series microarray data sets.

Nam H, Lee K, Lee D - BMC Bioinformatics (2009)

An illustration of temporal association rule mining process. An illustration of temporal association rule mining process with transcriptional time delay Δ = 2, support ≥ 50%, confidence ≥ 50%.
© Copyright Policy - open-access
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC2665054&req=5

Figure 2: An illustration of temporal association rule mining process. An illustration of temporal association rule mining process with transcriptional time delay Δ = 2, support ≥ 50%, confidence ≥ 50%.
Mentions: Figure 2 shows an illustration of temporal association rule mining process. First, continuous gene expression values are converted into discrete values (up, down, and none) (Figure 2(a)). Second, to find temporally associated genes, we first assume that all related genes may have various sizes of transcriptional time delay. Therefore, our method searches associated genes in all possible sets of different time point experiments where the time interval is from 0 to n (Figure 2(b)). In this illustration, Δ is 2. For example, Temporal transaction set t0 + t2 = [g1L↑, g2L↓, g1R↑, g2R↑, g3R↓] consists of up or down regulated genes at time stamps t0 and t2 with the size of transcriptional time delay Δ = 2. Note that, for g1, it is up regulated in both cases of t0 and t2, but we marked them as two different genes like g1L (g1 in Left hand side) and g1R (g1 in Right hand side). Third, Figure 2(c) indicates the extracted temporal frequent item sets with support threshold 50%. And finally, two temporal association rules are discovered with confidence threshold 50% as shown in Figure 2(d). In this manner, TARM can find (1) various sizes of transcriptional time delay between associated genes, (2) activation and inhibition relationship, (3) sets of co-regulators for the target genes.

Bottom Line: From the extracted temporal association rules, associated genes, which play same role of biological processes within short transcriptional time delay and some temporal dependencies between genes with specific biological processes are identified.TARM showed higher precision score than Dynamic Bayesian network and Bayesian network.Advantages of TARM are that it tells us the size of transcriptional time delay between associated genes, activation and inhibition relationship between genes, and sets of co-regulators.

View Article: PubMed Central - HTML - PubMed

Affiliation: Department of Bio and Brain Engineering, KAIST, 373-1 Guseong-dong, Yuseong-gu, Daejeon, Korea. hjnam@kaist.ac.kr

ABSTRACT

Background: One of the most challenging problems in mining gene expression data is to identify how the expression of any particular gene affects the expression of other genes. To elucidate the relationships between genes, an association rule mining (ARM) method has been applied to microarray gene expression data. However, a conventional ARM method has a limit on extracting temporal dependencies between gene expressions, though the temporal information is indispensable to discover underlying regulation mechanisms in biological pathways. In this paper, we propose a novel method, referred to as temporal association rule mining (TARM), which can extract temporal dependencies among related genes. A temporal association rule has the form [gene A upward arrow, gene B downward arrow] --> (7 min) [gene C upward arrow], which represents that high expression level of gene A and significant repression of gene B followed by significant expression of gene C after 7 minutes. The proposed TARM method is tested with Saccharomyces cerevisiae cell cycle time-series microarray gene expression data set.

Results: In the parameter fitting phase of TARM, the fitted parameter set [threshold = +/- 0.8, support >or= 3 transactions, confidence >or= 90%] with the best precision score for KEGG cell cycle pathway has been chosen for rule mining phase. With the fitted parameter set, numbers of temporal association rules with five transcriptional time delays (0, 7, 14, 21, 28 minutes) are extracted from gene expression data of 799 genes, which are pre-identified cell cycle relevant genes. From the extracted temporal association rules, associated genes, which play same role of biological processes within short transcriptional time delay and some temporal dependencies between genes with specific biological processes are identified.

Conclusion: In this work, we proposed TARM, which is an applied form of conventional ARM. TARM showed higher precision score than Dynamic Bayesian network and Bayesian network. Advantages of TARM are that it tells us the size of transcriptional time delay between associated genes, activation and inhibition relationship between genes, and sets of co-regulators.

Show MeSH