Integration of molecular network data reconstructs Gene Ontology

Gligorijević, Vladimir; Janjić, Vuk; Pržulj, Nataša

doi:10.1093/bioinformatics/btu470

Cited by 34 publications

(34 citation statements)

References 32 publications

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“…Interface 12: 20150571 links between PPI and gene Co-Ex networks [122], whereas a small overlap of links has been observed between PPI and GI networks [123]. Hence, these studies have indicated that a GI network is a valuable complement to the other two biological networks and this has been confirmed in several network integration studies [55,61,66].…”

Section: Biological Data and Network Representationmentioning

confidence: 75%

“…The authors also estimate the influence of each data source onto the model prediction accuracy and find that the GI network contributes the most to the quality of the integrated model. A similar study demonstrates the potential of the method to reconstruct GO and to predict new GO term associations and gene annotations (GAs) [55] by using evidence from four different types of molecular networks of baker's yeast. Another study uses an NMTF matrix completion approach to predict new GDAs by factorizing known GDAs under prior knowledge from the DSN and the PPI network [60].…”

Section: Non-negative Matrix Factorizationmentioning

confidence: 76%

“…It aims to computationally assign molecular functions to unannotated proteins. The accuracy of these methods has largely improved with the use of integration methods that can incorporate multiple different biological data conveying complementary information about protein functions [51][52][53][54][55]. Also, interactions between proteins are important for understanding intracellular signalling pathways and other cellular process.…”

Section: The Need For Data Integrationmentioning

confidence: 99%

See 2 more Smart Citations

Methods for biological data integration: perspectives and challenges

Gligorijević¹,

Pržulj²

2015

J. R. Soc. Interface.

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229

157

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Rapid technological advances have led to the production of different types of biological data and enabled construction of complex networks with various types of interactions between diverse biological entities. Standard network data analysis methods were shown to be limited in dealing with such heterogeneous networked data and consequently, new methods for integrative data analyses have been proposed. The integrative methods can collectively mine multiple types of biological data and produce more holistic, systems-level biological insights. We survey recent methods for collective mining (integration) of various types of networked biological data. We compare different stateof-the-art methods for data integration and highlight their advantages and disadvantages in addressing important biological problems. We identify the important computational challenges of these methods and provide a general guideline for which methods are suited for specific biological problems, or specific data types. Moreover, we propose that recent non-negative matrix factorization-based approaches may become the integration methodology of choice, as they are well suited and accurate in dealing with heterogeneous data and have many opportunities for further development.

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Section: Biological Data and Network Representationmentioning

confidence: 75%

Section: Non-negative Matrix Factorizationmentioning

confidence: 76%

Section: The Need For Data Integrationmentioning

confidence: 99%

See 1 more Smart Citation

Methods for biological data integration: perspectives and challenges

Gligorijević¹,

Pržulj²

2015

J. R. Soc. Interface.

Self Cite

229

157

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show abstract

“…Additionally, a large number of ontology learning methods have been developed that commonly use natural language as a source to generate formal representations of concepts within a domain [40]. In biology and biomedicine, where large volumes of experimental data are available, several methods have also been developed to generate ontologies in a data-driven manner from high-throughput datasets [16,19,38]. These rely on generation of concepts through clustering of information within a network and use ontology mapping techniques [28] to align these clusters to ontology classes.…”

Section: Turning Data Into Knowledgementioning

confidence: 99%

Data Science and symbolic AI: Synergies, challenges and opportunities

Hoehndorf

Queralt-Rosiñach

2017

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Editor: Tobias Kuhn (https://orcid.org/0000-0002-1267-0234) Solicited reviews: anonymous reviewer; Rinke Hoekstra (https://orcid.org/0000-0001-7076-9083); Agnieszka Ławrynowicz (https://orcid.org/0000-0002-2442-345X); Honghan Wu (https://orcid.org/0000-0002-0213-5668) Received 21 February 2017 Accepted 27 April 2017Abstract. Symbolic approaches to Artificial Intelligence (AI) represent things within a domain of knowledge through physical symbols, combine symbols into symbol expressions, and manipulate symbols and symbol expressions through inference processes. While a large part of Data Science relies on statistics and applies statistical approaches to AI, there is an increasing potential for successfully applying symbolic approaches as well. Symbolic representations and symbolic inference are close to human cognitive representations and therefore comprehensible and interpretable; they are widely used to represent data and metadata, and their specific semantic content must be taken into account for analysis of such information; and human communication largely relies on symbols, making symbolic representations a crucial part in the analysis of natural language. Here we discuss the role symbolic representations and inference can play in Data Science, highlight the research challenges from the perspective of the data scientist, and argue that symbolic methods should become a crucial component of the data scientists' toolbox.

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“…This has provided us with a valuable framework for fusion (integration) of any number and type of interrelated heterogeneous datasets 12,13 . NMTF has demonstrated a great potential in addressing various biological problems, such as disease association prediction 12 , disease gene discovery 14 , protein-protein interaction prediction 15 and gene function prediction 16 .…”

Section: Introductionmentioning

confidence: 99%

Patient-Specific Data Fusion for Cancer Stratification and Personalised Treatment

2015

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According to Cancer Research UK, cancer is a leading cause of death accounting for more than one in four of all deaths in 2011. The recent advances in experimental technologies in cancer research have resulted in the accumulation of large amounts of patient-specific datasets, which provide complementary information on the same cancer type. We introduce a versatile data fusion (integration) framework that can effectively integrate somatic mutation data, molecular interactions and drug chemical data to address three key challenges in cancer research: stratification of patients into groups having different clinical outcomes, prediction of driver genes whose mutations trigger the onset and development of cancers, and repurposing of drugs treating particular cancer patient groups. Our new framework is based on graph-regularised non-negative matrix tri-factorization, a machine learning technique for co-clustering heterogeneous datasets. We apply our framework on ovarian cancer data to simultaneously cluster patients, genes and drugs by utilising all datasets. We demonstrate superior performance of our method over the state-of-the-art method, Network-based Stratification, in identifying three patient subgroups that have significant differences in survival outcomes and that are in good agreement with other clinical data. Also, we identify potential new driver genes that we obtain by analysing the gene clusters enriched in known drivers of ovarian cancer progression. We validated the top scoring genes identified as new drivers through database search and biomedical literature curation. Finally, we identify potential candidate drugs for repurposing that could be used in treatment of the identified patient subgroups by targeting their mutated gene products. We validated a large percentage of our drug-target predictions by using other databases and through literature curation.

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Integration of molecular network data reconstructs Gene Ontology

Cited by 34 publications

References 32 publications

Methods for biological data integration: perspectives and challenges

Methods for biological data integration: perspectives and challenges

Data Science and symbolic AI: Synergies, challenges and opportunities

Patient-Specific Data Fusion for Cancer Stratification and Personalised Treatment

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