Paper Info
Title
ABC algorithm as feature selection for biomarker discovery in mass spectrometry analysis.
Abstract
Mass spectrometry technique is gradually gaining momentum among the recent techniques deployed by several analytical research labs which intends to study biological or chemical properties of complex structures such as protein sequences. Literature reveals that reasoning voluminous mass spectrometry data via sophisticated computational techniques inspired by observing natural processes adapted by biological life has been yielding fruitful results towards the advancement of fields including bioinformatics and proteomics. Such advanced approaches provide efficient ways to mine mass spectrometry data in order to extract discriminating features that aid in discovering vital information, specifically discovering disease-related protein patterns in complex protein sequences. This study reveals the use of artificial bee colony (ABC) as a new feature selection technique incorporated with SVM classifier. Results achieved96 and 100% for sensitivity and specificity respectively in discriminating cirrhosis and liver cancer cases.
Year
DOI
Venue
2012
10.1109/DMO.2012.6329800
DMO
Keywords
Field
DocType
bioinformatics,cancer,feature extraction,mass spectra,proteins,proteomics,support vector machines,ABC algorithm,SVM classifier,artificial bee colony,bioinformatics,biological life,biomarker discovery,cirrhosis,disease related protein pattern,feature selection,liver cancer,mass spectrometry analysis,protein sequence,proteomics,ABC algorithm,biomarker discovery,feature selection,mass spectrometry
Data mining,Algorithm design,Proteomics,Feature selection,Computer science,Support vector machine,Feature extraction,Artificial intelligence,Mass spectrometry,Biomarker discovery,Statistical classification,Machine learning
Conference
Citations 
PageRank 
References 
5
0.47
11
Authors
3
Name
Order
Citations
PageRank
M. Y. SyarifahAdilah150.47
Rosni Abdullah215624.82
Ibrahim Venkat37014.37