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Principal Component Analysis

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Pure Ion Chromatograms Combined with Advanced Machine Learning Methods Improve Accuracy of Discriminant Models in LC-MS-Based Untargeted Metabolomics.

Molecules (Basel, Switzerland)
Untargeted metabolomics based on liquid chromatography coupled with mass spectrometry (LC-MS) can detect thousands of features in samples and produce highly complex datasets. The accurate extraction of meaningful features and the building of discrimi...

Deriving accurate molecular indicators of protein synthesis through Raman-based sparse classification.

The Analyst
Raman spectroscopy has the ability to retrieve molecular information from live biological samples non-invasively through optical means. Coupled with machine learning, it is possible to use this large amount of information to create models that can pr...

Machine learning applied to near-infrared spectra for clinical pleural effusion classification.

Scientific reports
Lung cancer patients with malignant pleural effusions (MPE) have a particular poor prognosis. It is crucial to distinguish MPE from benign pleural effusion (BPE). The present study aims to develop a rapid, convenient and economical diagnostic method ...

Unwrapping the phase portrait features of adventitious crackle for auscultation and classification: a machine learning approach.

Journal of biological physics
The paper delves into the plausibility of applying fractal, spectral, and nonlinear time series analyses for lung auscultation. The thirty-five sound signals of bronchial (BB) and pulmonary crackle (PC) analysed by fast Fourier transform and wavelet ...

Increasing prediction accuracy of pathogenic staging by sample augmentation with a GAN.

PloS one
Accurate prediction of cancer stage is important in that it enables more appropriate treatment for patients with cancer. Many measures or methods have been proposed for more accurate prediction of cancer stage, but recently, machine learning, especia...

Implicit adversarial data augmentation and robustness with Noise-based Learning.

Neural networks : the official journal of the International Neural Network Society
We introduce a Noise-based Learning (NoL) approach for training neural networks that are intrinsically robust to adversarial attacks. We find that the learning of random noise introduced with the input with the same loss function used during posterio...

Assessing the Outbreak Risk of Epidemics Using Fuzzy Evidential Reasoning.

Risk analysis : an official publication of the Society for Risk Analysis
Epidemic diseases (EDs) present a significant but challenging risk endangering public health, evidenced by the outbreak of COVID-19. Compared to other risks affecting public health such as flooding, EDs attract little attention in terms of risk asses...

Machine Learning-Based Classification of Lignocellulosic Biomass from Pyrolysis-Molecular Beam Mass Spectrometry Data.

International journal of molecular sciences
High-throughput analysis of biomass is necessary to ensure consistent and uniform feedstocks for agricultural and bioenergy applications and is needed to inform genomics and systems biology models. Pyrolysis followed by mass spectrometry such as mole...

A Hybrid Supervised Approach to Human Population Identification Using Genomics Data.

IEEE/ACM transactions on computational biology and bioinformatics
Single nucleotide polymorphisms (SNPs) are one type of genetic variations and each SNP represents a difference in a single DNA building block, namely a nucleotide. Previous research demonstrated that SNPs can be used to identify the correct source po...

IPCARF: improving lncRNA-disease association prediction using incremental principal component analysis feature selection and a random forest classifier.

BMC bioinformatics
BACKGROUND: Identifying lncRNA-disease associations not only helps to better comprehend the underlying mechanisms of various human diseases at the lncRNA level but also speeds up the identification of potential biomarkers for disease diagnoses, treat...