scholarly journals Discovering Temporal Patterns in Longitudinal Nontargeted Metabolomics Data via Group and Nuclear Norm Regularized Multivariate Regression

Metabolites ◽  
2020 ◽  
Vol 10 (1) ◽  
pp. 33 ◽  
Author(s):  
Zhaozhou Lin ◽  
Qiao Zhang ◽  
Shengyun Dai ◽  
Xiaoyan Gao
Keyword(s):  
Discriminant Analysis ◽  
Least Squares ◽  
Partial Least Squares ◽  
Temporal Patterns ◽  
Nuclear Norm ◽  
Compact Set ◽  
Metabolomics Data ◽  
Pattern Recognition Methods ◽  
High Prediction ◽  
Consecutive Time

Temporal associations in longitudinal nontargeted metabolomics data are generally ignored by common pattern recognition methods such as partial least squares discriminant analysis (PLS-DA) and orthogonal partial least squares discriminant analysis (OPLS-DA). To discover temporal patterns in longitudinal metabolomics, a multitask learning (MTL) method employing structural regularization was proposed. The group regularization term of the proposed MTL method enables the selection of a small number of tentative biomarkers while maintaining high prediction accuracy. Meanwhile, the nuclear norm imposed into the regression coefficient accounts for the interrelationship of the metabolomics data obtained on consecutive time points. The effectiveness of the proposed method was demonstrated by comparison study performed on a metabolomics dataset and a simulating dataset. The results showed that a compact set of tentative biomarkers charactering the whole antipyretic process of Qingkailing injection were selected with the proposed method. In addition, the nuclear norm introduced in the new method could help the group norm to improve the method’s recovery ability.

Moving Window Partial Least-Squares Discriminant Analysis for Identification of Different Kinds of Bezoar Samples by near Infrared Spectroscopy and Comparison of Different Pattern Recognition Methods

2007 ◽  
Vol 15 (5) ◽  
pp. 291-297 ◽  
Author(s):  
Hai-Yan Fu ◽  
Shuang-Yan Huan ◽  
Lu Xu ◽  
Li-Juan Tang ◽  
Jian-Hui Jiang ◽  
...  
Keyword(s):  
Pattern Recognition ◽  
Discriminant Analysis ◽  
Least Squares ◽  
Partial Least Squares ◽  
Near Infrared ◽  
Geographical Origin ◽  
Quality Analysis ◽  
Moving Window ◽  
Linear Discriminant ◽  
Pattern Recognition Methods

Moving window partial least-squares (MWPLS) regression was coupled with near infrared (NIR) spectra as an interval selection method to improve the performance of partial least squares discriminant analysis (PLSDA) models. This method was applied to the identification of artificial bezoar, natural bezoar and artificial bezoar in natural bezoar and compared with some traditional pattern recognition methods, such as principal component analysis (PCA), linear discriminant analysis (LDA) and PLSDA. The introduction of MWPLS enhanced the performance of PLSDA model. The results obtained showed that moving window partial least-squares discriminant analysis (MWPLSDA) can extract wavelength intervals with useful information and build simple yet effective classification models that can significantly improve the classification accuracy. Then MWPLSDA was used to identify natural bezoar by geographical origin; a promising result was achieved. The work showed that MWPLSDA could be a promising method for quality analysis and discrimination of chinese medical herbs according to geographical origin.

scholarly journals Partial Least Squares Discriminant Analysis and Bayesian Networks for Metabolomic Prediction of Childhood Asthma

Metabolites ◽  
2018 ◽  
Vol 8 (4) ◽  
pp. 68 ◽  
Author(s):  
Rachel Kelly ◽  
Michael McGeachie ◽  
Kathleen Lee-Sarwar ◽  
Priyadarshini Kachroo ◽  
Su Chu ◽  
...  
Keyword(s):  
Discriminant Analysis ◽  
Bayesian Networks ◽  
Least Squares ◽  
Partial Least Squares ◽  
Permutation Test ◽  
Area Under The Curve ◽  
Metabolomics Data ◽  
Causative Agents ◽  
Plasma Metabolite ◽  
Analytical Approaches

To explore novel methods for the analysis of metabolomics data, we compared the ability of Partial Least Squares Discriminant Analysis (PLS-DA) and Bayesian networks (BN) to build predictive plasma metabolite models of age three asthma status in 411 three year olds (n = 59 cases and 352 controls) from the Vitamin D Antenatal Asthma Reduction Trial (VDAART) study. The standard PLS-DA approach had impressive accuracy for the prediction of age three asthma with an Area Under the Curve Convex Hull (AUCCH) of 81%. However, a permutation test indicated the possibility of overfitting. In contrast, a predictive Bayesian network including 42 metabolites had a significantly higher AUCCH of 92.1% (p for difference < 0.001), with no evidence that this accuracy was due to overfitting. Both models provided biologically informative insights into asthma; in particular, a role for dysregulated arginine metabolism and several exogenous metabolites that deserve further investigation as potential causative agents. As the BN model outperformed the PLS-DA model in both accuracy and decreased risk of overfitting, it may therefore represent a viable alternative to typical analytical approaches for the investigation of metabolomics data.

Sparse Partial-least-squares Discriminant Analysis for Different Geographical Origins of Salvia miltiorrhiza by 1 H-NMR-based Metabolomics

2013 ◽  
Vol 25 (1) ◽  
pp. 50-58 ◽  
Author(s):  
Miaomiao Jiang ◽  
Chunhua Wang ◽  
Yu Zhang ◽  
Yifan Feng ◽  
Yuefei Wang ◽  
...  
Keyword(s):  
Discriminant Analysis ◽  
Least Squares ◽  
Partial Least Squares ◽  
Salvia Miltiorrhiza ◽  
H Nmr

scholarly journals 1H-NMR spectroscopy identifies potential biomarkers in serum metabolomic signatures for early stage esophageal squamous cell carcinoma

PeerJ ◽  
2019 ◽  
Vol 7 ◽  
pp. e8151 ◽  
Author(s):  
Yan-Yan Liu ◽  
Zhong-Xian Yang ◽  
Li-Min Ma ◽  
Xu-Qing Wen ◽  
Huan-Lin Ji ◽  
...  
Keyword(s):  
Squamous Cell Carcinoma ◽  
Discriminant Analysis ◽  
Nmr Spectroscopy ◽  
Esophageal Squamous Cell Carcinoma ◽  
Cell Carcinoma ◽  
Least Squares ◽  
Partial Least Squares ◽  
Early Stage ◽  
H Nmr ◽  
Potential Biomarkers

Background Esophageal squamous cell carcinoma (ESCC) is one of the most prevalent types of upper gastrointestinal malignancies. Here, we used 1H nuclear magnetic resonance spectroscopy (1H-NMR) to identify potential serum biomarkers in patients with early stage ESCC. Methods Sixty-five serum samples from early stage ESCC patients (n = 25) and healthy controls (n = 40) were analysed using 1H-NMR spectroscopy. We distinguished between different metabolites through principal component analysis, partial least squares-discriminant analysis, and orthogonal partial least squares-discriminant analysis (OPLS-DA) using SIMCA-P+ version 14.0 software. Receiver operating characteristic (ROC) analysis was conducted to verify potential biomarkers. Results Using OPLS-DA, 31 altered serum metabolites were successfully identified between the groups. Based on the area under the ROC curve (AUROC), and the biomarker panel with AUROC of 0.969, six serum metabolites (α-glucose, choline, glutamine, glutamate, valine, and dihydrothymine) were selected as potential biomarkers for early stage ESCC. Dihydrothymine particularly was selected as a new feasible biomarker associated with tumor occurrence. Conclusions 1H-NMR spectroscopy may be a useful tumour detection approach in identifying useful metabolic ESCC biomarkers for early diagnosis and in the exploration of the molecular pathogenesis of ESCC.

Sign in / Sign up

Export Citation Format

Share Document