scholarly journals A data integration methodology for systems biology

2005 ◽  
Vol 102 (48) ◽  
pp. 17296-17301 ◽  
Author(s):  
D. Hwang ◽  
A. G. Rust ◽  
S. Ramsey ◽  
J. J. Smith ◽  
D. M. Leslie ◽  
...  
Keyword(s):  
Systems Biology ◽  
Data Integration

scholarly journals An integrated dataset for in silico drug discovery

2010 ◽  
Vol 7 (3) ◽  
Author(s):  
Simon J Cockell ◽  
Jochen Weile ◽  
Phillip Lord ◽  
Claire Wipat ◽  
Dmytro Andriychenko ◽  
...  
Keyword(s):  
Systems Biology ◽  
Drug Discovery ◽  
Drug Development ◽  
Data Integration ◽  
In Silico ◽  
Drug Repositioning ◽  
Integrated Systems ◽  
Integration Platform ◽  
Treatment Indications ◽  
New Treatment

SummaryDrug development is expensive and prone to failure. It is potentially much less risky and expensive to reuse a drug developed for one condition for treating a second disease, than it is to develop an entirely new compound. Systematic approaches to drug repositioning are needed to increase throughput and find candidates more reliably. Here we address this need with an integrated systems biology dataset, developed using the Ondex data integration platform, for the in silico discovery of new drug repositioning candidates. We demonstrate that the information in this dataset allows known repositioning examples to be discovered. We also propose a means of automating the search for new treatment indications of existing compounds.

scholarly journals DIPSBC - data integration platform for systems biology collaborations

2012 ◽  
Vol 13 (1) ◽  
Author(s):  
Felix Dreher ◽  
Thomas Kreitler ◽  
Christopher Hardt ◽  
Atanas Kamburov ◽  
Reha Yildirimman ◽  
...  
Keyword(s):  
Systems Biology ◽  
Data Integration ◽  
Integration Platform

Drug discovery in the age of systems biology: the rise of computational approaches for data integration

2012 ◽  
Vol 23 (4) ◽  
pp. 609-616 ◽  
Author(s):  
Murat Iskar ◽  
Georg Zeller ◽  
Xing-Ming Zhao ◽  
Vera van Noort ◽  
Peer Bork
Keyword(s):  
Systems Biology ◽  
Drug Discovery ◽  
Data Integration ◽  
Computational Approaches

ChemInform Abstract: Data Integration for Dynamic and Sustainable Systems Biology Resources: Challenges and Lessons Learned

ChemInform ◽  
2010 ◽  
Vol 41 (32) ◽  
pp. no-no
Author(s):  
Daniel E. Sullivan ◽  
Joseph L. Jr. Gabbard ◽  
Maulik Shukla ◽  
Bruno Sobral
Keyword(s):  
Systems Biology ◽  
Data Integration ◽  
Lessons Learned ◽  
Sustainable Systems ◽  
Abstract Data

scholarly journals A Customizable Analysis Flow in Integrative Multi-Omics

Biomolecules ◽  
2020 ◽  
Vol 10 (12) ◽  
pp. 1606
Author(s):  
Samuel M. Lancaster ◽  
Akshay Sanghi ◽  
Si Wu ◽  
Michael P. Snyder
Keyword(s):  
Machine Learning ◽  
Mass Spectrometry ◽  
Systems Biology ◽  
Nucleic Acid ◽  
Best Practices ◽  
Data Integration ◽  
Study Design ◽  
Molecular Interactions ◽  
Analysis Workflow ◽  
Integration Techniques

The number of researchers using multi-omics is growing. Though still expensive, every year it is cheaper to perform multi-omic studies, often exponentially so. In addition to its increasing accessibility, multi-omics reveals a view of systems biology to an unprecedented depth. Thus, multi-omics can be used to answer a broad range of biological questions in finer resolution than previous methods. We used six omic measurements—four nucleic acid (i.e., genomic, epigenomic, transcriptomics, and metagenomic) and two mass spectrometry (proteomics and metabolomics) based—to highlight an analysis workflow on this type of data, which is often vast. This workflow is not exhaustive of all the omic measurements or analysis methods, but it will provide an experienced or even a novice multi-omic researcher with the tools necessary to analyze their data. This review begins with analyzing a single ome and study design, and then synthesizes best practices in data integration techniques that include machine learning. Furthermore, we delineate methods to validate findings from multi-omic integration. Ultimately, multi-omic integration offers a window into the complexity of molecular interactions and a comprehensive view of systems biology.

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