AIMC Topic: Principal Component Analysis

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KLFDAPC: a supervised machine learning approach for spatial genetic structure analysis.

Briefings in bioinformatics Jul 18, 2022
Geographic patterns of human genetic variation provide important insights into human evolution and disease. A commonly used tool to detect and describe them is principal component analysis (PCA) or the supervised linear discriminant analysis of princ...
Supervised Machine Learning Genetics, Population Genetic Structures Principal Component Analysis Discriminant Analysis Humans Polymorphism, Single Nucleotide
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Intelligent identification of film on cotton based on hyperspectral imaging and convolutional neural network.

Science progress Jan 1, 2022
The identification of the film on cotton is of great significance for the improvement of cotton quality. Most of the existing technologies are dedicated to removing colored foreign fibers from cotton using photoelectric sorting methods. However, the ...
Principal Component Analysis Neural Networks, Computer Hyperspectral Imaging Algorithms Motion Pictures
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Improved Automatic Grading of Diabetic Retinopathy Using Deep Learning and Principal Component Analysis.

Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference Nov 1, 2021
Diabetic retinopathy (DR) is one of the most common chronic diseases around the world. Early screening and diagnosis of DR patients through retinal fundus is always preferred. However, image screening and diagnosis is a highly time-consuming task for...
Deep Learning Diabetes Mellitus Fundus Oculi Mass Screening Humans Principal Component Analysis Diabetic Retinopathy
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iPiDi-PUL: identifying Piwi-interacting RNA-disease associations based on positive unlabeled learning.

Briefings in bioinformatics May 20, 2021
Accumulated researches have revealed that Piwi-interacting RNAs (piRNAs) are regulating the development of germ and stem cells, and they are closely associated with the progression of many diseases. As the number of the detected piRNAs is increasing ...
Humans Machine Learning Algorithms Principal Component Analysis RNA, Small Interfering Datasets as Topic Genetic Predisposition to Disease Computational Biology
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Fourier and Laplace-like low-field NMR spectroscopy: The perspectives of multivariate and artificial neural networks analyses.

Journal of magnetic resonance (San Diego, Calif. : 1997) Mar 1, 2021
Low field Nuclear Magnetic Resonance (LF-NMR) is a rich source of information for a wide range of samples types. These can be hard or soft solids, such as plastics or elastomers; bulk liquids or liquids absorbed in porous materials, and can come from...
Magnetic Resonance Spectroscopy Fourier Analysis Neural Networks, Computer Female Uterus Uterine Neoplasms Principal Component Analysis Humans
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Variable Selection for Time-to-Event Data.

Methods in molecular biology (Clifton, N.J.) Jan 1, 2021
With the increasing availability of large scale biomedical and -omics data, researchers are offered with unprecedented opportunities to discover novel biomarkers for clinical outcomes. At the same time, they are also faced with great challenges to ac...
Biomarkers Genomics Principal Component Analysis Proportional Hazards Models Regression Analysis Machine Learning Algorithms Humans
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Constipation Predominant Irritable Bowel Syndrome and Functional Constipation Are Not Discrete Disorders: A Machine Learning Approach.

The American journal of gastroenterology Jan 1, 2021
INTRODUCTION: Chronic constipation is classified into 2 main syndromes, irritable bowel syndrome with constipation (IBS-C) and functional constipation (FC), on the assumption that they differ along multiple clinical characteristics and are plausibly ...
Irritable Bowel Syndrome Constipation Cost of Illness Cohort Studies Chronic Disease Abdominal Pain Supervised Machine Learning Principal Component Analysis Adult Male Middle Aged Humans Female
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Classification, identification, and growth stage estimation of microalgae based on transmission hyperspectral microscopic imaging and machine learning.

Optics express Oct 12, 2020
A transmission hyperspectral microscopic imager (THMI) that utilizes machine learning algorithms for hyperspectral detection of microalgae is presented. The THMI system has excellent performance with spatial and spectral resolutions of 4 µm and 3 nm,...
Microalgae Species Specificity Machine Learning Absorption, Radiation Solutions Microscopy Support Vector Machine Hyperspectral Imaging Principal Component Analysis
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Using high-dimensional features for high-accuracy pulse diagnosis.

Mathematical biosciences and engineering : MBE Oct 9, 2020
Accurate pulse diagnosis is often based on extensive clinical experience. Recently, modern computer-aided pulse diagnostic methods have been developed to help doctors to quickly determine patients' physiological conditions. Most pulse diagnostic meth...
Heart Rate Humans Neural Networks, Computer Computers Principal Component Analysis Algorithms
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A three-dimensional discriminant analysis approach for hyperspectral images.

The Analyst Aug 24, 2020
Raman hyperspectral imaging is a powerful technique that provides both chemical and spatial information of a sample matrix being studied. The generated data are composed of three-dimensional (3D) arrays containing the spatial information across the x...
Principal Component Analysis Algorithms Discriminant Analysis Support Vector Machine
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