Please use this identifier to cite or link to this item:
|Title:||DEVELOPMENT AND INVESTIGATION OF CHEMOMETRIC BASELINE CORRECTION APPROACHES AND METABONOMIC CLASSIFICATION ALGORITHMS||Authors:||BHASKARAN DAVID PRAKASH||Keywords:||Metabonomics, chemometric, baseline-correction, classification, algorithms, data-mining||Issue Date:||31-Jul-2015||Citation:||BHASKARAN DAVID PRAKASH (2015-07-31). DEVELOPMENT AND INVESTIGATION OF CHEMOMETRIC BASELINE CORRECTION APPROACHES AND METABONOMIC CLASSIFICATION ALGORITHMS. ScholarBank@NUS Repository.||Abstract:||Metabonomic analysis has been used for classification in a diverse range of areas from toxicology and dietary effects through to parasitology and molecular epidemiology, including disease diagnosis and therapy monitoring. Metabonomic data requires correction via pre-processing approaches followed by post-processing involving a robust modelling approach to provide accurate and fast classification. In this work, we developed novel algorithms for both phases. For pre-processing, we developed a baseline correction algorithm, Automated Iterative Moving Averaging (AIMA) , which has similar accuracy as existing semi-automated algorithms but is fully automated and computationally more efficient (28.6 to 197.7 times faster). For post-processing, we developed a fully automated classification algorithm, Automated Pearson?s correlation change classification (APC3) , which has similar or better prediction accuracy as the current state of art algorithms for metabonomic data but is 3.9 to 7 times faster. Finally, we did a comparative study on four sparsity embedded classification techniques.||URI:||http://scholarbank.nus.edu.sg/handle/10635/122842|
|Appears in Collections:||Ph.D Theses (Open)|
Show full item record
Files in This Item:
|BDavidPrakash.pdf||1.62 MB||Adobe PDF|
Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.