A method for handling metabonomics data from liquid chromatography/mass spectrometry: combinational use of support vector machine recursive feature elimination, genetic algorithm and random forest for feature selection

Metabolomics ◽  
2011 ◽  
Vol 7 (4) ◽  
pp. 549-558 ◽  
Author(s):  
Xiaohui Lin ◽  
Quancai Wang ◽  
Peiyuan Yin ◽  
Liang Tang ◽  
Yexiong Tan ◽  
...  
Keyword(s):  
Mass Spectrometry ◽  
Genetic Algorithm ◽  
Support Vector Machine ◽  
Feature Selection ◽  
Liquid Chromatography ◽  
Random Forest ◽  
Recursive Feature Elimination ◽  
Support Vector ◽  
Liquid Chromatography Mass Spectrometry ◽  
Chromatography Mass Spectrometry

scholarly journals An Adaptive Genetic Algorithm with Recursive Feature Elimination Approach for Predicting Malaria Vector Gene Expression Data Classification using Support Vector Machine Kernels

2021 ◽  
Vol 18 (17) ◽  
Author(s):  
Micheal Olaolu AROWOLO ◽  
Marion Olubunmi ADEBIYI ◽  
Chiebuka Timothy NNODIM ◽  
Sulaiman Olaniyi ABDULSALAM ◽  
Ayodele Ariyo ADEBIYI
Keyword(s):  
Gene Expression ◽  
Genetic Algorithm ◽  
Support Vector Machine ◽  
Feature Selection ◽  
Malaria Vector ◽  
Recursive Feature Elimination ◽  
Support Vector ◽  
Adaptive Genetic Algorithm ◽  
Rna Seq ◽  
Gene Expressions

As mosquito parasites breed across many parts of the sub-Saharan Africa part of the world, infected cells embrace an unpredictable and erratic life period. Millions of individual parasites have gene expressions. Ribonucleic acid sequencing (RNA-seq) is a popular transcriptional technique that has improved the detection of major genetic probes. The RNA-seq analysis generally requires computational improvements of machine learning techniques since it computes interpretations of gene expressions. For this study, an adaptive genetic algorithm (A-GA) with recursive feature elimination (RFE) (A-GA-RFE) feature selection algorithms was utilized to detect important information from a high-dimensional gene expression malaria vector RNA-seq dataset. Support Vector Machine (SVM) kernels were used as the classification algorithms to evaluate its predictive performances. The feasibility of this study was confirmed by using an RNA-seq dataset from the mosquito Anopheles gambiae. The technique results in related performance had 98.3 and 96.7 % accuracy rates, respectively. HIGHLIGHTS Dimensionality reduction method based of feature selection Classification using Support vector machine Classification of malaria vector dataset using an adaptive GA-RFE-SVM GRAPHICAL ABSTRACT

Intra-batch effect correction in liquid chromatography-mass spectrometry using quality control samples and support vector regression (QC-SVRC)

The Analyst ◽  
2015 ◽  
Vol 140 (22) ◽  
pp. 7810-7817 ◽  
Author(s):  
Julia Kuligowski ◽  
Ángel Sánchez-Illana ◽  
Daniel Sanjuán-Herráez ◽  
Máximo Vento ◽  
Guillermo Quintás
Keyword(s):  
Mass Spectrometry ◽  
Quality Control ◽  
Liquid Chromatography ◽  
Support Vector Regression ◽  
Batch Effect ◽  
Support Vector ◽  
Batch Effects ◽  
Liquid Chromatography Mass Spectrometry ◽  
Chromatography Mass Spectrometry ◽  
Control Samples

Intra-batch effects in liquid chromatography-mass spectrometry are corrected using quality control samples and support vector regression.

scholarly journals Bayesian ABC-MCMC Classification of Liquid Chromatography–Mass Spectrometry Data

2015 ◽  
Vol 14s5 ◽  
pp. CIN.S30798
Author(s):  
Upamanyu Banerjee ◽  
Ulisses M. Braga-Neto
Keyword(s):  
Mass Spectrometry ◽  
Liquid Chromatography ◽  
Small Sample ◽  
Mass Spectrometry Data ◽  
Classification Rule ◽  
Support Vector ◽  
Liquid Chromatography Mass Spectrometry ◽  
Linear Discriminant ◽  
Chromatography Mass Spectrometry

Proteomics promises to revolutionize cancer treatment and prevention by facilitating the discovery of molecular biomarkers. Progress has been impeded, however, by the small-sample, high-dimensional nature of proteomic data. We propose the application of a Bayesian approach to address this issue in classification of proteomic profiles generated by liquid chromatography-mass spectrometry (LC-MS). Our approach relies on a previously proposed model of the LC-MS experiment, as well as on the theory of the optimal Bayesian classifier (OBC). Computation of the OBC requires the combination of a likelihood-free methodology called approximate Bayesian computation (ABC) as well as Markov chain Monte Carlo (MCMC) sampling. Numerical experiments using synthetic LC-MS data based on an actual human proteome indicate that the proposed ABC-MCMC classification rule outperforms classical methods such as support vector machines, linear discriminant analysis, and 3-nearest neighbor classification rules in the case when sample size is small or the number of selected proteins used to classify is large.

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