scholarly journals A comparison of high-throughput plasma NMR protocols for comparative untargeted metabolomics

Metabolomics ◽  
2020 ◽  
Vol 16 (5) ◽  
Author(s):  
Nikolaos G. Bliziotis ◽  
Udo F. H. Engelke ◽  
Ruud L. E. G. Aspers ◽  
Jasper Engel ◽  
Jaap Deinum ◽  
...  
Keyword(s):  
High Throughput ◽  
Classification Accuracy ◽  
Biomarker Discovery ◽  
Internal Standard ◽  
Valuable Contribution ◽  
Diffusion Nmr ◽  
Sample Classification ◽  
Plasma Metabolome ◽  
Spectral Reduction ◽  
Data Acquisition And Processing

Abstract Introduction When analyzing the human plasma metabolome with Nuclear Magnetic Resonance (NMR) spectroscopy, the Carr–Purcell–Meiboom–Gill (CPMG) experiment is commonly employed for large studies. However, this process can lead to compromised statistical analyses due to residual macromolecule signals. In addition, the utilization of Trimethylsilylpropanoic acid (TSP) as an internal standard often leads to quantification issues, and binning, as a spectral summarization step, can result in features not clearly assignable to metabolites. Objectives Our aim was to establish a new complete protocol for large plasma cohorts collected with the purpose of describing the comparative metabolic profile of groups of samples. Methods We compared the conventional CPMG approach to a novel procedure that involves diffusion NMR, using the Longitudinal Eddy-Current Delay (LED) experiment, maleic acid (MA) as the quantification reference and peak picking for spectral reduction. This comparison was carried out using the ultrafiltration method as a gold standard in a simple sample classification experiment, with Partial Least Squares–Discriminant Analysis (PLS-DA) and the resulting metabolic signatures for multivariate data analysis. In addition, the quantification capabilities of the method were evaluated. Results We found that the LED method applied was able to detect more metabolites than CPMG and suppress macromolecule signals more efficiently. The complete protocol was able to yield PLS-DA models with enhanced classification accuracy as well as a more reliable set of important features than the conventional CPMG approach. Assessment of the quantitative capabilities of the method resulted in good linearity, recovery and agreement with an established amino acid assay for the majority of the metabolites tested. Regarding repeatability, ~ 85% of all peaks had an adequately low coefficient of variation (< 30%) in replicate samples. Conclusion Overall, our comparison yielded a high-throughput untargeted plasma NMR protocol for optimized data acquisition and processing that is expected to be a valuable contribution in the field of metabolic biomarker discovery.

scholarly journals Data Augmentation and Spectral Structure Features for Limited Samples Hyperspectral Classification

2021 ◽  
Vol 13 (4) ◽  
pp. 547
Author(s):  
Wenning Wang ◽  
Xuebin Liu ◽  
Xuanqin Mou
Keyword(s):  
Classification Accuracy ◽  
Data Augmentation ◽  
Classification Problem ◽  
Classification Performance ◽  
Spectral Structure ◽  
Limited Sample ◽  
Sample Classification ◽  
Training Samples ◽  
Traditional Classification ◽  
Hyperspectral Classification

For both traditional classification and current popular deep learning methods, the limited sample classification problem is very challenging, and the lack of samples is an important factor affecting the classification performance. Our work includes two aspects. First, the unsupervised data augmentation for all hyperspectral samples not only improves the classification accuracy greatly with the newly added training samples, but also further improves the classification accuracy of the classifier by optimizing the augmented test samples. Second, an effective spectral structure extraction method is designed, and the effective spectral structure features have a better classification accuracy than the true spectral features.

scholarly journals Human body-fluid proteome: quantitative profiling and computational prediction

2020 ◽  
Author(s):  
Lan Huang ◽  
Dan Shao ◽  
Yan Wang ◽  
Xueteng Cui ◽  
Yufei Li ◽  
...  
Keyword(s):  
High Throughput ◽  
Human Body ◽  
Body Fluid ◽  
Biomarker Discovery ◽  
Interaction Network ◽  
Computational Prediction ◽  
Computational Effort ◽  
The Body ◽  
Support Vector ◽  
Learning Approaches

Abstract Empowered by the advancement of high-throughput bio technologies, recent research on body-fluid proteomes has led to the discoveries of numerous novel disease biomarkers and therapeutic drugs. In the meantime, a tremendous progress in disclosing the body-fluid proteomes was made, resulting in a collection of over 15 000 different proteins detected in major human body fluids. However, common challenges remain with current proteomics technologies about how to effectively handle the large variety of protein modifications in those fluids. To this end, computational effort utilizing statistical and machine-learning approaches has shown early successes in identifying biomarker proteins in specific human diseases. In this article, we first summarized the experimental progresses using a combination of conventional and high-throughput technologies, along with the major discoveries, and focused on current research status of 16 types of body-fluid proteins. Next, the emerging computational work on protein prediction based on support vector machine, ranking algorithm, and protein–protein interaction network were also surveyed, followed by algorithm and application discussion. At last, we discuss additional critical concerns about these topics and close the review by providing future perspectives especially toward the realization of clinical disease biomarker discovery.

scholarly journals High-throughput metabolic profiling, combined with chemometrics and bioinformatic analysis reveals functional alterations in myocardial dysfunction

RSC Advances ◽  
2019 ◽  
Vol 9 (6) ◽  
pp. 3351-3358 ◽  
Author(s):  
Qun Liang ◽  
Han Liu ◽  
Xiuli Li ◽  
Panguo Hairong ◽  
Peiyang Sun ◽  
...  
Keyword(s):  
High Throughput ◽  
Metabolic Profiling ◽  
Biomarker Discovery ◽  
Myocardial Dysfunction ◽  
Bioinformatic Analysis

High-throughput metabolic profiling technology has been used for biomarker discovery and to reveal underlying metabolic mechanisms.

scholarly journals Semiautomated Alignment of High-Throughput Metabolite Profiles with Chemometric Tools

2017 ◽  
Vol 2017 ◽  
pp. 1-9 ◽  
Author(s):  
Ze-ying Wu ◽  
Zhong-da Zeng ◽  
Zi-dan Xiao ◽  
Daniel Kam-Wah Mok ◽  
Yi-zeng Liang ◽  
...  
Keyword(s):  
Data Processing ◽  
High Throughput ◽  
Biomarker Discovery ◽  
Linear Interpolation ◽  
Time Shift ◽  
Data Partition ◽  
High Throughput Data ◽  
Automated Method ◽  
Metabolite Profiles ◽  
Standard Profile

The rapid increase in the use of metabolite profiling/fingerprinting techniques to resolve complicated issues in metabolomics has stimulated demand for data processing techniques, such as alignment, to extract detailed information. In this study, a new and automated method was developed to correct the retention time shift of high-dimensional and high-throughput data sets. Information from the target chromatographic profiles was used to determine the standard profile as a reference for alignment. A novel, piecewise data partition strategy was applied for the determination of the target components in the standard profile as markers for alignment. An automated target search (ATS) method was proposed to find the exact retention times of the selected targets in other profiles for alignment. The linear interpolation technique (LIT) was employed to align the profiles prior to pattern recognition, comprehensive comparison analysis, and other data processing steps. In total, 94 metabolite profiles of ginseng were studied, including the most volatile secondary metabolites. The method used in this article could be an essential step in the extraction of information from high-throughput data acquired in the study of systems biology, metabolomics, and biomarker discovery.

scholarly journals Cancer biomarker discovery for cholangiocarcinoma: the high-throughput approaches

2014 ◽  
Vol 21 (6) ◽  
pp. 388-396 ◽  
Author(s):  
Atit Silsirivanit ◽  
Kanlayanee Sawanyawisuth ◽  
Gregory J. Riggins ◽  
Chaisiri Wongkham
Keyword(s):  
High Throughput ◽  
Biomarker Discovery ◽  
Cancer Biomarker

scholarly journals A TMA De-Arraying Method for High Throughput Biomarker Discovery in Tissue Research

PLoS ONE ◽  
2011 ◽  
Vol 6 (10) ◽  
pp. e26007 ◽  
Author(s):  
Yinhai Wang ◽  
Kienan Savage ◽  
Claire Grills ◽  
Andrena McCavigan ◽  
Jacqueline A. James ◽  
...  
Keyword(s):  
High Throughput ◽  
Biomarker Discovery ◽  
Tissue Research

scholarly journals Robust biomarker discovery for hepatocellular carcinoma from high-throughput data by multiple feature selection methods

2021 ◽  
Vol 14 (S1) ◽  
Author(s):  
Zishuang Zhang ◽  
Zhi-Ping Liu
Keyword(s):  
Machine Learning ◽  
Hepatocellular Carcinoma ◽  
Feature Selection ◽  
High Throughput ◽  
Biomarker Discovery ◽  
Selection Process ◽  
Recursive Feature Elimination ◽  
Statistical Interpretation ◽  
Cancer Data ◽  
High Throughput Data

Abstract Background Hepatocellular carcinoma (HCC) is one of the most common cancers. The discovery of specific genes severing as biomarkers is of paramount significance for cancer diagnosis and prognosis. The high-throughput omics data generated by the cancer genome atlas (TCGA) consortium provides a valuable resource for the discovery of HCC biomarker genes. Numerous methods have been proposed to select cancer biomarkers. However, these methods have not investigated the robustness of identification with different feature selection techniques. Methods We use six different recursive feature elimination methods to select the gene signiatures of HCC from TCGA liver cancer data. The genes shared in the six selected subsets are proposed as robust biomarkers. Akaike information criterion (AIC) is employed to explain the optimization process of feature selection, which provides a statistical interpretation for the feature selection in machine learning methods. And we use several methods to validate the screened biomarkers. Results In this paper, we propose a robust method for discovering biomarker genes for HCC from gene expression data. Specifically, we implement recursive feature elimination cross-validation (RFE-CV) methods based on six different classication algorithms. The overlaps in the discovered gene sets via different methods are referred as the identified biomarkers. We give an interpretation of the feature selection process based on machine learning using AIC in statistics. Furthermore, the features selected by the backward logistic stepwise regression via AIC minimum theory are completely contained in the identified biomarkers. Through the classification results, the superiority of interpretable robust biomarker discovery method is verified. Conclusions It is found that overlaps among gene subsets contain different quantitative features selected by the RFE-CV of 6 classifiers. The AIC values in the model selection provide a theoretical foundation for the feature selection process of biomarker discovery via machine learning. What’s more, genes containing in more optimally selected subsets make better biological sense and implication. The quality of feature selection is improved by the intersections of biomarkers selected from different classifiers. This is a general method suitable for screening biomarkers of complex diseases from high-throughput data.

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