Paper Info
Title
Using Abc Algorithm With Shrinkage Estimator To Identify Biomarkers Of Ovarian Cancer From Mass Spectrometry Analysis
Abstract
Biomarker discovery through mass spectrometry analysis has continuously intrigued researchers from various fields such as analytical researchers, computer scientists and mathematicians. The uniqueness of this study relies on the ability of the proteomic patterns to detect particular disease especially at the early stage. However, identification through high-throughput mass spectrometry analysis raises some challenges. Typically, it suffers from high dimensionality of data with tens of thousands attributes and high level of redundancy and noises. Hence this study will focus on two stages of mass spectrometry pipelines; firstly we propose shrinkage estimation of covariance to evaluate the discriminant characteristics among peaks of mass spectrometry data for feature extraction; secondly a sophisticated computational technique that mimic survival and natural processing which is called as Artificial Bee Colony (ABC) as feature selection is integrated with linear SVM classifier for this biomarker discovery analysis. The proposed method is tested with real-world ovarian cancer dataset to evaluate the discrimination power, accuracy, sensitivity and also specificity.
Year
DOI
Venue
2013
10.1007/978-3-642-40846-5_35
HYBRID ARTIFICIAL INTELLIGENT SYSTEMS
Keywords
Field
DocType
metaheuristic, feature selection, swarm algorithm, bio-inspired algorithm, classification, feature extraction
Shrinkage estimator,Pattern recognition,Feature selection,Computer science,Feature extraction,Redundancy (engineering),Artificial intelligence,Mass spectrometry,Biomarker discovery,Classifier (linguistics),Metaheuristic
Conference
Volume
ISSN
Citations 
8073
0302-9743
1
PageRank 
References 
Authors
0.38
9
3
Name
Order
Citations
PageRank
Syarifah Adilah Mohamed Yusoff141.20
Rosni Abdullah215624.82
Ibrahim Venkat37014.37