A literature network of human genes for high-throughput analysis of gene expression

Jenssen, Tor-Kristian; Lægreid, Astrid; Komorowski, Jan; Hovig, Eivind

doi:10.1038/ng0501-21

Cited by 486 publications

(351 citation statements)

References 14 publications

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“…The one is to filter low confidence data by parameters as used in our study, the other is by integrating more relevant types of biological information. For example, the relationships among proteins can be described in many types as co-expression, shared functional annotations, co-occurrence in literature and co-regulation [29,53-55]. These highly heterogeneous data contributed not only to inferring stronger relationships through the accumulation of evidence, but also providing broader coverage than any single data source.…”

Section: Discussionmentioning

confidence: 99%

Prioritizing protein complexes implicated in human diseases by network optimization

et al. 2014

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BackgroundThe detection of associations between protein complexes and human inherited diseases is of great importance in understanding mechanisms of diseases. Dysfunctions of a protein complex are usually defined by its member disturbance and consequently result in certain diseases. Although individual disease proteins have been widely predicted, computational methods are still absent for systematically investigating disease-related protein complexes.ResultsWe propose a method, MAXCOM, for the prioritization of candidate protein complexes. MAXCOM performs a maximum information flow algorithm to optimize relationships between a query disease and candidate protein complexes through a heterogeneous network that is constructed by combining protein-protein interactions and disease phenotypic similarities. Cross-validation experiments on 539 protein complexes show that MAXCOM can rank 382 (70.87%) protein complexes at the top against protein complexes constructed at random. Permutation experiments further confirm that MAXCOM is robust to the network structure and parameters involved. We further analyze protein complexes ranked among top ten for breast cancer and demonstrate that the SWI/SNF complex is potentially associated with breast cancer.ConclusionsMAXCOM is an effective method for the discovery of disease-related protein complexes based on network optimization. The high performance and robustness of this approach can facilitate not only pathologic studies of diseases, but also the design of drugs targeting on multiple proteins.

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Section: Discussionmentioning

confidence: 99%

Prioritizing protein complexes implicated in human diseases by network optimization

et al. 2014

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“…The genes are color-coded in blue according to their amount of immunological information in Medline. 37 Genes with a decreased expression in the comparison are shaped as diamond squares with green borders, and those with increased expression are shaped as circles with red borders. The networks have been visualized by using the Cytoscape software.…”

Section: Discussionmentioning

confidence: 99%

“…[33][34][35] For the CLC network, the highest-ranked interactions were retrieved from a resource that integrates the protein interaction phrases from the Biomolecular Interaction Network Database and their co-citations with gene names in the same sentence from the over 20 million abstracts in Medline. 36,37 A gene has a connection to a neighboring gene in both networks only if the…”

Section: Analysis Of Microarray Datamentioning

confidence: 99%

CLC and IFNAR1 are differentially expressed and a global immunity score is distinct between early- and late-onset colorectal cancer

et al. 2011

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“…Biomedical literature represents almost all of our existing knowledge about biological entities and their relationships. For the analysis presented here, we employed a simple but effective way to derive potential associations between PTMs from literature, the cooccurrence approach, which was previously applied for GRN reconstruction [51–53]. Simply, if two PTMs appear in an article abstract indexed in PubMed, we assume an association between them.…”

Section: Resultsmentioning

confidence: 99%

A nucleosomal approach to inferring causal relationships of histone modifications

et al. 2014

BMC Genomics

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MotivationHistone proteins are subject to various posttranslational modifications (PTMs). Elucidating their functional relationships is crucial toward understanding many biological processes. Bayesian network (BN)-based approaches have shown the advantage of revealing causal relationships, rather than simple cooccurrences, of PTMs. Previous works employing BNs to infer causal relationships of PTMs require that all confounders should be included. This assumption, however, is unavoidably violated given the fact that several modifications are often regulated by a common but unobserved factor. An existing non-parametric method can be applied to tackle the problem but the complexity and inflexibility make it impractical.ResultsWe propose a novel BN-based method to infer causal relationships of histone modifications. First, from the evidence that nucleosome organization in vivo significantly affects the activities of PTM regulators working on chromatin substrate, hidden confounders of PTMs are selectively introduced by an information-theoretic criterion. Causal relationships are then inferred from a network model of both PTMs and the derived confounders. Application on human epigenomic data shows the advantage of the proposed method, in terms of computational performance and support from literature. Requiring less strict data assumptions also makes it more practical. Interestingly, analysis of the most significant relationships suggests that the proposed method can recover biologically relevant causal effects between histone modifications, which should be important for future investigation of histone crosstalk.

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A literature network of human genes for high-throughput analysis of gene expression

Cited by 486 publications

References 14 publications

Prioritizing protein complexes implicated in human diseases by network optimization

Prioritizing protein complexes implicated in human diseases by network optimization

CLC and IFNAR1 are differentially expressed and a global immunity score is distinct between early- and late-onset colorectal cancer

A nucleosomal approach to inferring causal relationships of histone modifications

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