Integrating data and knowledge to identify functional modules of genes: a multilayer approach

Background Characterizing the modular structure of cellular network is an important way to identify novel genes for targeted therapeutics. This is made possible by the rising of high-throughput technology. Unfortunately, computational methods to identify functional modules were limited by the data q...

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Veröffentlicht in:BMC bioinformatics Jg. 20; H. 1; S. 225 - 15
Hauptverfasser: Liang, Lifan, Chen, Vicky, Zhu, Kunju, Fan, Xiaonan, Lu, Xinghua, Lu, Songjian
Format: Journal Article
Sprache:Englisch
Veröffentlicht: London BioMed Central 02.05.2019
BioMed Central Ltd
Springer Nature B.V
BMC
Schlagworte:
Accuracy
Algorithms
Bioinformatics
Biomedical and Life Sciences
Cancer
Cellular communication
Cellular structure
Cluster Analysis
Clustering
Computational Biology/Bioinformatics
Computer Appl. in Life Sciences
Computer applications
Gene expression
Gene Expression Profiling
Genes
Genes, Fungal
Genetic research
Graph clustering
Health aspects
Humans
Identification
Identification methods
Information management
Knowledge
Life Sciences
Messenger RNA
Methodology
Methodology Article
Microarrays
Modular structures
Modules
Multilayers
Multiplex
Networks analysis
Novels
Pharmacogenomics
Protein Interaction Mapping - methods
Protein-protein interaction
Protein-protein interactions
Proteins
Random walk
RNA
Saccharomyces cerevisiae Proteins - genetics
Saccharomyces cerevisiae Proteins - metabolism
Studies
Technology
Telecommunications systems
Therapeutics
Topic modeling
Wireless communications
Yeast
United States
China
New York
United States > US
Graph clustering
Topic modeling
Protein complex
Protein-protein interaction
Functional module
Random walk
Multiplex
Gene expression
ISSN:1471-2105, 1471-2105
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Zusammenfassung:Background Characterizing the modular structure of cellular network is an important way to identify novel genes for targeted therapeutics. This is made possible by the rising of high-throughput technology. Unfortunately, computational methods to identify functional modules were limited by the data quality issues of high-throughput techniques. This study aims to integrate knowledge extracted from literature to further improve the accuracy of functional module identification. Results Our new model and algorithm were applied to both yeast and human interactomes. Predicted functional modules have covered over 90% of the proteins in both organisms, while maintaining a comparable overall accuracy. We found that the combination of both mRNA expression information and biomedical knowledge greatly improved the performance of functional module identification, which is better than those only using protein interaction network weighted with transcriptomic data, literature knowledge, or simply unweighted protein interaction network. Our new algorithm also achieved better performance when comparing with some other well-known methods, especially in terms of the positive predictive value (PPV), which indicated the confidence of novel discovery. Conclusion Higher PPV with the multiplex approach suggested that information from both sources has been effectively integrated to reduce false positive. With protein coverage higher than 90%, our algorithm is able to generate more novel biological hypothesis with higher confidence.
Bibliographie:ObjectType-Article-1
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ISSN:1471-2105
1471-2105
DOI:10.1186/s12859-019-2800-y