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SVS: Data and knowledge integration in computational biology
- Source :
- EMBC, Scopus-Elsevier
- Publication Year :
- 2011
- Publisher :
- IEEE, 2011.
-
Abstract
- In this paper we present a framework for structured variable selection (SVS). The main concept of the proposed schema is to take a step towards the integration of two different aspects of data mining: database and machine learning perspective. The framework is flexible enough to use not only microarray data, but other high-throughput data of choice (e.g. from mass spectrometry, microarray, next generation sequencing). Moreover, the feature selection phase incorporates prior biological knowledge in a modular way from various repositories and is ready to host different statistical learning techniques. We present a proof of concept of SVS, illustrating some implementation details and describing current results on high-throughput microarray data.
- Subjects :
- Databases, Factual
Microarray
Computer science
Knowledge engineering
Feature selection
Genomics
Mass spectrometry
computer.software_genre
Machine learning
Mass Spectrometry
DNA sequencing
Artificial Intelligence
Knowledge integration
Gene expression
Data Mining
Humans
Oligonucleotide Array Sequence Analysis
Models, Statistical
Computers
business.industry
Microarray analysis techniques
Gene Expression Profiling
Computational Biology
Parkinson Disease
Modular design
Gene expression profiling
Schema (genetic algorithms)
Programming Languages
Database theory
Artificial intelligence
Data mining
business
computer
Algorithms
Software
Data integration
Subjects
Details
- Database :
- OpenAIRE
- Journal :
- 2011 Annual International Conference of the IEEE Engineering in Medicine and Biology Society
- Accession number :
- edsair.doi.dedup.....aa1d4140705732562c82892c8581b459