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The emission probabilities of the ¯rst four observations of gene expression changes in the illustrative example shown in Table 3.
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Living cells are realized by complex gene expression programs that are moderated by regulatory proteins called transcription factors (TFs). The TFs control the differential expression of target genes in the context of transcriptional regulatory networks (TRNs), either individually or in groups. Deciphering the mechanisms of how the TFs control the...
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Context 1
... the active emission probabilities of all other states are decreased to 0.25 and the inactive emission probabilities of them are increased to 0.75. See Table 4 for the updated emissions. At time t 2 , the upregulation of TF 1 and the downregulation of g trigger the state RS of TF 1 ( ...Context 2
... 19, 2012 11:38:14am WSPC/185-JBCB 1250012 ISSN: 0219-7200 FA2 ðTF 2 ; ANÞ is updated to ð0:375 þ 1Þ=2 ¼ 0:688 and the active emission probability of ðTF 1 ; RSÞ to ð0:625 þ 1Þ=2 ¼ 0:813. See Table 4 for the emission probabilities of the four observations. The training process continues until all the gene expression observations are processed. ...Similar publications
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Citations
... A prominent direction for addressing this problem is using computational data mining approaches for the analysis of high-throughput biological data, such as gene expression data [1][2][3][4]. In particular, analysis methods have been developed to infer regulatory interactions from transcriptome data [5][6][7][8][9][10][11][12][13][14]. These regulatory interactions link regulators, such as transcription factors and kinases, to their targets and may include the regulatory type of the interaction, which indicates whether there is an activating (positive) or inhibitory (negative) association between the interactor pair. ...
Knowledge of interaction types in biological networks is important for understanding the functional organization of the cell. Currently information-based approaches are widely used for inferring gene regulatory interactions from genomics data, such as gene expression profiles; however, these approaches do not provide evidence about the regulation type (positive or negative sign) of the interaction.
This paper describes a novel algorithm, "Signing of Regulatory Networks" (SIREN), which can infer the regulatory type of interactions in a known gene regulatory network (GRN) given corresponding genome-wide gene expression data. To assess our new approach, we applied it to three different benchmark gene regulatory networks, including Escherichia coli, prostate cancer, and an in silico constructed network. Our new method has approximately 68, 70, and 100 percent accuracy, respectively, for these networks. To showcase the utility of SIREN algorithm, we used it to predict previously unknown regulation types for 454 interactions related to the prostate cancer GRN.
SIREN is an efficient algorithm with low computational complexity; hence, it is applicable to large biological networks. It can serve as a complementary approach for a wide range of network reconstruction methods that do not provide information about the interaction type.
... In our previous research [19], a Hidden Markov model was developed to relate gene expression patterns to regulatory interactions, in order to solve a relatively simpler subproblem that considers only two TFs. To predict regulatory interactions for all possible collaborative TFs, we propose an algorithm called "mTRIM" (multiple Transcriptional Regulatory Interaction Mechanism) in this paper. ...
... mTRIM was applied on two independently-constructed yeast transcriptional regulatory networks (the Harbison dataset [15] and the Reimand dataset [12]) to identify regulatory interactions. For performance comparison, DREM v3.0 [17] and TRIM [19] were both applied on the same datasets. We did not compare mTRIM with Yeang's method [3] because the latter's objective is to build a reliable TRN instead of predicting regulatory interactions. ...
... In these experiments, yeast cells were first synchronized to the same cell cycle stage, released from synchronization, and then the total RNA samples were taken at even intervals for a period of time (Table SI in Additional file 1). In order to decide whether a gene is significantly up or down regulated, a gene expression change cutoff of 0.35 was applied (the same threshold used in [19]). ...
Living cells are realized by complex gene expression programs that are moderated by regulatory proteins called transcription factors (TFs). The TFs control the differential expression of target genes in the context of transcriptional regulatory networks (TRNs), either individually or in groups. Deciphering the mechanisms of how the TFs control the expression of target genes is a challenging task, especially when multiple TFs collaboratively participate in the transcriptional regulation.
We model the underlying regulatory interactions in terms of the directions (activation or repression) and their logical roles (necessary and/or sufficient) with a modified association rule mining approach, called mTRIM. The experiment on Yeast discovered 670 regulatory interactions, in which multiple TFs express their functions on common target genes collaboratively. The evaluation on yeast genetic interactions, TF knockouts and a synthetic dataset shows that our algorithm is significantly better than the existing ones.
mTRIM is a novel method to infer TF collaborations in transcriptional regulation networks. mTRIM is available at http://www.msu.edu/~jinchen/mTRIM.
This paper proposes a novel multi-Laplacian prior (MLP) and augmented Lagrangian method (ALM) approach for gene interactions and putative transcription factors (TFs) identification from time-course gene microarray data. It employs a non-linear time-varying auto-regressive (N-TVAR) model and the Maximum-A-Posteriori-Probability method for incorporating the multi-Laplacian prior and the continuity constraint. The MLP allows connections to/from a gene to be better preserved for putative TF identification in non-stationarity gene regulatory network as compared with conventional L
1
-based penalties. Moreover, the ALM allows the resultant non-smooth L
1
-based penalties to be decoupled from the remaining smooth terms, so that the former and latter can be efficiently solved using a low-complexity proximity operator and smooth optimization technique, respectively. Synthetic and real time-course gene microarray datasets are tested to evaluate the performance of the proposed method. Experimental results show that the proposed method gives better accuracy and higher computational speed than our previous work using smoothed approximation. Moreover, its performance, without the use of ChIP-chip data, is found to be highly comparable with other state-of-the-art methods integrating both ChIP-chip and gene microarray data. It suggests that the proposed method may serve as a useful exploratory tool for putative TF identification with reduced experimental cost.
Exploring the complex interactive mechanism in a Gene Regulatory Network (GRN) developed using transcriptome data obtained from standard microarray and/or RNA-seq experiments helps us to understand the triggering factors in cancer research. The Transcription Factor (TF) genes generate protein complexes which affect the transcription of various target genes. However, considering the mode of regulation in a time frame such transcriptional activities are dependent on some specific activation time points only. It is also crucial to check whether the regulating capabilities are uniform across varied conditions, especially when periodicity is a big issue. In this context, we propose an algorithm called RIFT which helps to monitor the temporal differential regulatory pattern of a Differentially Expressed (DE) target gene either by a TF gene or a group of TF genes from a large time series (TS) data. We have tested our algorithm on HeLa cell cycle data and compared the result with its most advanced state of the art counterpart proposed so far. As our algorithm yields up stringent mode and target specific significant valid TF genes for a DE gene, we can expect to have new forms of genetic interactions.
Identifying condition-specific co-expressed gene groups is critical for gene functional and regulatory analysis. However, given that genes with critical functions (such as transcription factors) may not co-express with their target genes, it is insufficient to uncover gene functional associations only from gene expression data. In this paper, we propose a novel integrative biclustering approach to build high quality biclusters from gene expression data, and to identify critical missing genes in biclusters based on Gene Ontology as well. Our approach delivers a complete inter- and intra-bicluster functional relationship, thus provides biologists a clear picture for gene functional association study. We experimented with the Yeast cell cycle and Arabidopsis cold-response gene expression datasets. Experimental results show that a clear inter- and intra-bicluster relationship is identified, and the biological significance of the biclusters is considerably improved.
