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Biological Data Integration System
Biological data are often most valuable when it can be integrated with other types of
related biological data
...
Such analysis is often
referred as data (or network) integration
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A starting point of this analysis is to use a mathematical concept of networks to
represents

omics

layers
...
In biological networks, nodes usually represent discrete biological entities
at a molecular (e
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, genes, proteins, metabolites, drugs, etc
...
g
...
For the last couple of decades, networks have been one of the most widely used
mathematical tools for modelling and analysing omics data
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The need for data integration
Understanding cellular process and molecular interactions by integrating molecular
networks has just been one of the challenges of data integration
...
Some
examples of these data include protein and genome sequence data, disease data from genomewide association studies (GWAS) , mutation data from The Cancer Genome Atlas (TCGA) ,
copy number variation (CNV) data , functional annotation and ontology data, such as gene

ontology (GO) and disease ontology (DO), protein structure data , drug chemical structure and
drug–target interaction (DTI) data
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Data Integration Approaches
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Network inference and functional linkage network (FLN) construction
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It aims to construct network topology (or wiring between
genes) based on the evidence from different data types
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Standard methods for GRN inference
have mostly been based on gene expression data
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Protein function (also known as protein
annotation) prediction has been demonstrated to be a good alternative to the timeconsuming experimental protein function characterization
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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
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Disease gene prioritization and disease–disease association prediction
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It deals with the
identification of genes involved in a specific disease and providing a better
understanding of gene aberrations and their roles in the formation of diseases
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Therefore, computational methods for prioritization of disease
genes have been proposed
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Accumulation of various biological
data involving interactions between drugs, diseases and genes, and protein structural

and functional similarities, provide us with new opportunities for data integration
methods to generate new associations between diseases and existing drugs
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g
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), genetic (e
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, somatic mutations) and
genomic data (e
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, gene expression data from healthy and diseased tissues)
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Data integration methods have started
contributing to this growing field

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