Integrative approaches for data analysis in systems biology: Current advances

Frolova, Alina; Obolenska, Maria

doi:10.1109/ysf.2016.7753835

Cited by 5 publications

(3 citation statements)

References 32 publications

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“…It is evident that this approach is highly applicable to the context of poly-omic data integration in genome scale metabolic models, owing to the interdependencies and correlations between all types of poly-omic data. At the very least, genomics, transcriptomics and proteomics are inextricably linked by the central dogma of molecular biology [191].…”

Section: Perspective: Integration With Multi-view Machine Learning Apmentioning

confidence: 99%

Seeing the wood for the trees: a forest of methods for optimization and omic-network integration in metabolic modelling

Vijayakumar

Conway

Lió

et al. 2017

Briefings in Bioinformatics

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Metabolic modelling has entered a mature phase with dozens of methods and software implementations available to the practitioner and the theoretician. It is not easy for a modeller to be able to see the wood (or the forest) for the trees. Driven by this analogy, we here present a 'forest' of principal methods used for constraint-based modelling in systems biology. This provides a tree-based view of methods available to prospective modellers, also available in interactive version at http://modellingmetabolism.net, where it will be kept updated with new methods after the publication of the present manuscript. Our updated classification of existing methods and tools highlights the most promising in the different branches, with the aim to develop a vision of how existing methods could hybridize and become more complex. We then provide the first hands-on tutorial for multi-objective optimization of metabolic models in R. We finally discuss the implementation of multi-view machine learning approaches in poly-omic integration. Throughout this work, we demonstrate the optimization of trade-offs between multiple metabolic objectives, with a focus on omic data integration through machine learning. We anticipate that the combination of a survey, a perspective on multi-view machine learning and a step-by-step R tutorial should be of interest for both the beginner and the advanced user.

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Section: Perspective: Integration With Multi-view Machine Learning Apmentioning

confidence: 99%

Seeing the wood for the trees: a forest of methods for optimization and omic-network integration in metabolic modelling

Vijayakumar

Conway

Lió

et al. 2017

Briefings in Bioinformatics

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“…An integrative analysis is one of the approaches in this case. Unlike meta-analysis, which is essentially attaching the results of different experiments, an integrative analysis implies merging or integration of raw data which, as studies show [3,4], identifies significantly more differentially expressed genes. Besides, integrative analysis provides larger sample sets and Bioinformatics ISSN 1993-6842 (on-line); ISSN 0233-7657 (print) Biopolymers and Cell.…”

Section: Introductionmentioning

confidence: 99%

Creation of gene expression database on preeclampsia-affected human placenta

2018

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Publication of gene expression raw data in public repositories made it possible to reuse these data for cross-experiment integrative analysis and make new insights into biological phenomena. However, data uploaded by independent contributors are not standardized, sometimes incomplete and need preprocessing before any further analysis. Hence, the aim of this study was to create a specialized database of gene expression profiles, particularly in preeclampsia-affected human placenta as a cause of high rate of morbidity and mortality all over the world with unknown etiology and pathogenesis. Methods. All experiment and sample metadata were automatically extracted from ArrayExpress database via Bioservices. NCBI database was used to supplement the missing data along with the corresponding scientific articles and authors personal data. The experimental sample attributes were standardized according to MeSH term dictionary and Experimental Factor Ontology. Results. A database of more than 1000 samples of normal and preeclampsia-affected human placenta was created and supplied with metadata containing information on biological specimen, diagnosis, gestational age, mode of delivery and other sample characteristics. Conclusion. The samples in our newly created database now contain metadata for them to be comparable. The biological samples may be arranged in different case-control groups of larger size than in individual datasets for statistically significant analysis.

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“…An integrative analysis is one of the approaches in this case. Unlike metaanalysis, which is essentially adding up results of different experiments, an integrative analysis implies merging or integration of raw data which, as studies show [11,12], identifies significantly more differentially expressed genes [19]. Besides, integrative analysis provides larger sample sets and thus increases statistical significance and reduces experimental bias [20] which makes it most useful in cases when individual experiments' average sample set is small.…”

Section: Introductionmentioning

confidence: 99%

Creation of gene expression database on preeclampsia-affected human placenta

Lykhenko

Frolova

Obolenska

2017

Preprint

Self Cite

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Publication of gene expression raw data in open access at online resources like NCBI or ArrayExpress made it possible to use these data for cross-experiment integrative analysis and make new insights into biological phenomena. However, most popular of the present online resources are meant to be archives rather than ready for immediate access and interpretation databases. Data uploaded by independent contributors is not standardized and sometimes incomplete and needs further processing before it is ready for the analysis. Hence, the need for a specialized database appears.Given in this article is the description of the database that was created after processing a collection of 33 relevant datasets on pre-eclampsia-affected human placenta. Data processing includes the choice of relevant experiments from ArrayExpress database, the experiment sample attributes standardization according to MeSH term dictionary and Experimental Factor Ontology and the completion of missing data using information from the corresponding articles and authors.A database of more than 1000 samples contains sufficient sample-wise metadata for them to be arranged into relevant case-control groups. Metadata includes information on biological specimen, donor's diagnosis, gestational age, mode of delivery etc. The average size of these groups will be higher than it is in separate experiments. This will reduce experiment bias and enhance statistical accuracy of the subsequent analysis such as search for differentially expressed genes or inferring gene networks. The article concludes with the guidelines for the microarray experiment metadata uploading for future contributors.

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Integrative approaches for data analysis in systems biology: Current advances

Cited by 5 publications

References 32 publications

Seeing the wood for the trees: a forest of methods for optimization and omic-network integration in metabolic modelling

Seeing the wood for the trees: a forest of methods for optimization and omic-network integration in metabolic modelling

Creation of gene expression database on preeclampsia-affected human placenta

Creation of gene expression database on preeclampsia-affected human placenta

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