Comparative study of discretization methods of microarray data for inferring transcriptional regulatory networks

Li, Yong; Liu, Huan; Bai, Xi; Cai, Hua; Ji, Wei; Guo, Dianjing; Zhu, Yanming

doi:10.1186/1471-2105-11-520

Cited by 47 publications

(31 citation statements)

References 18 publications

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“…Because gene expression is noisy as reported (25), unsupervised discretization methods were usually used to discretize the gene expression profiles in gene regulatory network construction including equal width discretization, equal frequency discretization, K-means, column K-means discretization, and bidirectional K-means discretization. We discretized the expression values by rank ordering across genes and dividing the ranked 2158 expression values of each gene across experiments into three bins, labeled as "1" (upper 719 experiments where the gene ranked highest), "0" (lower 719 experiments where the gene ranked lowest), and "middle" (720 remaining experiments).…”

Section: Gene Expression Profilesmentioning

confidence: 99%

Modulation of Gene Expression Regulated by the Transcription Factor NF-κB/RelA

Zhao

Tian

et al. 2014

Journal of Biological Chemistry

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Background:Interacting proteins modulate the activity of NF-B/RelA transcription factor and expression of its targets. Results: By analyzing gene expression, protein binding, and DNA binding, we inferred and characterized 8349 such modulations. Conclusion: Different modulator groups affect separate pathways. Significance: We provide new insight into the activity of NF-B/RelA. Our inference model can be applied to other processes and pathways.

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Section: Gene Expression Profilesmentioning

confidence: 99%

Modulation of Gene Expression Regulated by the Transcription Factor NF-κB/RelA

Zhao

Tian

et al. 2014

Journal of Biological Chemistry

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“…We obtained a network for a specific tissue type by constructing the subgraph of the original Pathway Commons network using the set of active genes in that tissue. Specifically, given the integrated network as a graph G = (V, E) and the set of active genes for a condition as V t , the tissue specific graph is given as [21]. In this study, we used a simple approach for identification of gene activity.…”

Section: Tissue/disease Specific Networkmentioning

confidence: 99%

Comparison of tissue/disease specific integrated networks using directed graphlet signatures

Sonmez

Can

2016

Proceedings of the 7th ACM International Conference on Bioinformatics, Computational Biology, and Health Informatics

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Background: Analysis of integrated genome-scale networks is a challenging problem due to heterogeneity of high-throughput data. There are several topological measures, such as graphlet counts, for characterization of biological networks. Results: In this paper, we present methods for counting small sub-graph patterns in integrated genome-scale networks which are modeled as labeled multidigraphs. We have obtained physical, regulatory, and metabolic interactions between H. sapiens proteins from the Pathway Commons database. The integrated network is filtered for tissue/disease specific proteins by using a large-scale human transcriptional profiling study, resulting in several tissue and disease specific sub-networks. We have applied and extended the idea of graphlet counting in undirected protein-protein interaction (PPI) networks to directed multi-labeled networks and represented each network as a vector of graphlet counts. Graphlet counts are assessed for statistical significance by comparison against a set of randomized networks. We present our results on analysis of differential graphlets between different conditions and on the utility of graphlet count vectors for clustering multiple condition specific networks. Conclusions: Our results show that there are numerous statistically significant graphlets in integrated biological networks and the graphlet signature vector can be used as an effective representation of a multi-labeled network for clustering and systems level analysis of tissue/disease specific networks.

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“…Gene Set Enrichment Analysis [12] and Gene Set Analysis [13] are some of the many approaches currently available that focus on the analysis of gene sets, which may be obtained via databases such as the Molecular Signatures Database [12] or by discretizing time series data [14] and steady state data. Gene sets are more interpretable as they correspond to lists of biological processes [15] and may be thought of as derived sample features that succinctly summarize the original gene expression data [16].…”

Section: Introductionmentioning

confidence: 99%

GSCA: Reconstructing biological pathway topologies using a cultural algorithms approach

Judeh

Jayyousi

Acharya³

et al. 2014

2014 IEEE Congress on Evolutionary Computation (CEC)

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With the increasing availability of gene sets and pathway resources, novel approaches that combine both resources to reconstruct networks from gene sets are of interest. Currently, few computational approaches explore the search space of candidate networks using a parallel search. In particular, search agents employed by evolutionary computational approaches may better escape false peaks compared to previous approaches. It may also be hypothesized that gene sets may model signal transduction events, which refer to linear chains or cascades of reactions starting at the cell membrane and ending at the cell nucleus. These events may be indirectly observed as a set of unordered and overlapping gene sets. Thus, the goal is to reverse engineer the order information within each gene set to reconstruct the underlying source network using prior knowledge to limit the search space.We propose the Gene Set Cultural Algorithm (GSCA) to reconstruct networks from unordered gene sets. We introduce a robust heuristic based on the arborescence of a directed graph that performs well for random topological sort orderings across gene sets simulated for four E. coli networks and five Insilico networks from the DREAM3 and DREAM4 initiatives, respectively. Furthermore, GSCA performs favorably when reconstructing networks from randomly ordered gene sets for the aforementioned networks. Finally, we note that from a set of 23 gene sets discretized from a set of 300 S. cerevisiae expression profiles, GSCA reconstructs a network preserving most of the weak order information found in the KEGG Cell Cycle pathway, which was used as prior knowledge.

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Comparative study of discretization methods of microarray data for inferring transcriptional regulatory networks

Cited by 47 publications

References 18 publications

Modulation of Gene Expression Regulated by the Transcription Factor NF-κB/RelA

Modulation of Gene Expression Regulated by the Transcription Factor NF-κB/RelA

Comparison of tissue/disease specific integrated networks using directed graphlet signatures

GSCA: Reconstructing biological pathway topologies using a cultural algorithms approach

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