A comparative study of principal component regression and partial least squares on and partial least squares regression with regression with application to FTIR diabetes data

Venkatesan, P; Dharuman, C.; Gunasekaran, S

doi:10.17485/ijst/2011/v4i7.13

Cited by 6 publications

(4 citation statements)

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“…This was also revealed in the metabolic pathway analysis via MetaboAnalyst (Figure S8b in Supplementary Materials). 3-Hydroxybutyrate is also involved in butanoate metabolism, and it is also a degradation product of branched-chain amino acids, mainly of leucine [60]. The decreased concentrations of 3-hydroxybutyrate and acetone after NPs exposure are in contrast with other studies, which reported elevated levels of 3-hydroxybutyrate [21,23,61].…”

Section: Analysis Of Blood Plasmamentioning

confidence: 87%

Research in Metabolomics via Nuclear Magnetic Resonance Spectroscopy: Data Mining, Biochemistry and Clinical Chemistry

2022

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Section: Analysis Of Blood Plasmamentioning

confidence: 87%

Research in Metabolomics via Nuclear Magnetic Resonance Spectroscopy: Data Mining, Biochemistry and Clinical Chemistry

2022

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“…Plus, they looked into using a combination method called principle component regression, which mixes PCR and LR to obtain even better prediction results. In study [71], scientists compared the effectiveness of two biased regression models for predicting diabetic and normal serum using FTIR spectroscopy data. The study worked with data from 11 normal samples and utilized regression for diagnostic classification.…”

Section: Univariate Linear Regression (Lr) Is a Statistical Technique...mentioning

confidence: 99%

Exploring the Steps of Infrared (IR) Spectral Analysis: Pre-Processing, (Classical) Data Modelling, and Deep Learning

Mokari,

Guo,

Bocklitz

2023

Molecules

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Infrared (IR) spectroscopy has greatly improved the ability to study biomedical samples because IR spectroscopy measures how molecules interact with infrared light, providing a measurement of the vibrational states of the molecules. Therefore, the resulting IR spectrum provides a unique vibrational fingerprint of the sample. This characteristic makes IR spectroscopy an invaluable and versatile technology for detecting a wide variety of chemicals and is widely used in biological, chemical, and medical scenarios. These include, but are not limited to, micro-organism identification, clinical diagnosis, and explosive detection. However, IR spectroscopy is susceptible to various interfering factors such as scattering, reflection, and interference, which manifest themselves as baseline, band distortion, and intensity changes in the measured IR spectra. Combined with the absorption information of the molecules of interest, these interferences prevent direct data interpretation based on the Beer–Lambert law. Instead, more advanced data analysis approaches, particularly artificial intelligence (AI)-based algorithms, are required to remove the interfering contributions and, more importantly, to translate the spectral signals into high-level biological/chemical information. This leads to the tasks of spectral pre-processing and data modeling, the main topics of this review. In particular, we will discuss recent developments in both tasks from the perspectives of classical machine learning and deep learning.

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“…For a small number of variables and a high number of observations, it is commonly adopted for β the ordinary least square solution ((X T X) −1 X T Y). In the opposite case, where it is not possible to evaluate the inverse of the singular matrix (X T X), other solutions have to be considered [60]. One of them is the principal component regression (PCR) that makes use of the first PCs achieved by running PCA to fit the linear regression model instead of using all original variables.…”

Section: Principal Component Regression (Pcr) and Partial Least Squar...mentioning

confidence: 99%

NMR in Metabolomics: From Conventional Statistics to Machine Learning and Neural Network Approaches

et al. 2022

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NMR measurements combined with chemometrics allow achieving a great amount of information for the identification of potential biomarkers responsible for a precise metabolic pathway. These kinds of data are useful in different fields, ranging from food to biomedical fields, including health science. The investigation of the whole set of metabolites in a sample, representing its fingerprint in the considered condition, is known as metabolomics and may take advantage of different statistical tools. The new frontier is to adopt self-learning techniques to enhance clustering or classification actions that can improve the predictive power over large amounts of data. Although machine learning is already employed in metabolomics, deep learning and artificial neural networks approaches were only recently successfully applied. In this work, we give an overview of the statistical approaches underlying the wide range of opportunities that machine learning and neural networks allow to perform with accurate metabolites assignment and quantification.Various actual challenges are discussed, such as proper metabolomics, deep learning architectures and model accuracy.

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A comparative study of principal component regression and partial least squares on and partial least squares regression with regression with application to FTIR diabetes data

Cited by 6 publications

References 0 publications

Research in Metabolomics via Nuclear Magnetic Resonance Spectroscopy: Data Mining, Biochemistry and Clinical Chemistry

Research in Metabolomics via Nuclear Magnetic Resonance Spectroscopy: Data Mining, Biochemistry and Clinical Chemistry

Exploring the Steps of Infrared (IR) Spectral Analysis: Pre-Processing, (Classical) Data Modelling, and Deep Learning

NMR in Metabolomics: From Conventional Statistics to Machine Learning and Neural Network Approaches

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