scholarly journals A review on machine learning principles for multi-view biological data integration

2016 ◽  
pp. bbw113 ◽  
Author(s):  
Yifeng Li ◽  
Fang-Xiang Wu ◽  
Alioune Ngom
Keyword(s):  
Machine Learning ◽  
Data Integration ◽  
Biological Data ◽  
Biological Data Integration ◽  
Learning Principles

Analysis and Integration of Biological Data

2010 ◽  
pp. 287-314
Author(s):  
Diego Milone ◽  
Georgina Stegmayer ◽  
Matías Gerard ◽  
Laura Kamenetzky ◽  
Mariana López ◽  
...  
Keyword(s):  
Data Integration ◽  
New Technologies ◽  
Biological Significance ◽  
Neural Model ◽  
Biological Data ◽  
Data Sets ◽  
Clustering Methods ◽  
Gene Expressions ◽  
Internal Coherence ◽  
Biological Data Integration

The volume of information derived from post genomic technologies is rapidly increasing. Due to the amount of involved data, novel computational methods are needed for the analysis and knowledge discovery into the massive data sets produced by these new technologies. Furthermore, data integration is also gaining attention for merging signals from different sources in order to discover unknown relations. This chapter presents a pipeline for biological data integration and discovery of a priori unknown relationships between gene expressions and metabolite accumulations. In this pipeline, two standard clustering methods are compared against a novel neural network approach. The neural model provides a simple visualization interface for identification of coordinated patterns variations, independently of the number of produced clusters. Several quality measurements have been defined for the evaluation of the clustering results obtained on a case study involving transcriptomic and metabolomic profiles from tomato fruits. Moreover, a method is proposed for the evaluation of the biological significance of the clusters found. The neural model has shown a high performance in most of the quality measures, with internal coherence in all the identified clusters and better visualization capabilities.

scholarly journals Sparse canonical methods for biological data integration: application to a cross-platform study

2009 ◽  
Vol 10 (1) ◽  
Author(s):  
Kim-Anh Lê Cao ◽  
Pascal GP Martin ◽  
Christèle Robert-Granié ◽  
Philippe Besse
Keyword(s):  
Data Integration ◽  
Biological Data ◽  
Cross Platform ◽  
Biological Data Integration

scholarly journals Advancing Post-Genome Data and System Integration through Machine Learning

2002 ◽  
Vol 3 (1) ◽  
pp. 28-31 ◽  
Author(s):  
Francisco Azuaje
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
System Integration ◽  
Intelligent Systems ◽  
Biological Data ◽  
Markup Languages ◽  
Public And Private ◽  
Genome Data ◽  
The Creation ◽  
Biological Data Integration

Research on biological data integration has traditionally focused on the development of systems for the maintenance and interconnection of databases. In the next few years, public and private biotechnology organisations will expand their actions to promote the creation of a post-genome semantic web. It has commonly been accepted that artificial intelligence and data mining techniques may support the interpretation of huge amounts of integrated data. But at the same time, these research disciplines are contributing to the creation of content markup languages and sophisticated programs able to exploit the constraints and preferences of user domains. This paper discusses a number of issues on intelligent systems for the integration of bioinformatic resources.

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