Methods for Biological Data Integration: Perspectives and Challenges

Overview

Journal J R Soc Interface

Specialties Biology
Biomedical Engineering
Biophysics

Date 2015 Oct 23

PMID 26490630

Citations 92

Authors

Vladimir Gligorijevic

Natasa Przulj

Affiliations

Soon will be listed here.

Abstract

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 state-of-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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