AIMC Topic: Principal Component Analysis

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Phylogenetic convolutional neural networks in metagenomics.

BMC bioinformatics
BACKGROUND: Convolutional Neural Networks can be effectively used only when data are endowed with an intrinsic concept of neighbourhood in the input space, as is the case of pixels in images. We introduce here Ph-CNN, a novel deep learning architectu...

Predication of different stages of Alzheimer's disease using neighborhood component analysis and ensemble decision tree.

Journal of neuroscience methods
BACKGROUND: There is a spectrum of the progression from healthy control (HC) to mild cognitive impairment (MCI) without conversion to Alzheimer's disease (AD), to MCI with conversion to AD (cMCI), and to AD. This study aims to predict the different d...

Application of kernel principal component analysis and computational machine learning to exploration of metabolites strongly associated with diet.

Scientific reports
Computer-based technological innovation provides advancements in sophisticated and diverse analytical instruments, enabling massive amounts of data collection with relative ease. This is accompanied by a fast-growing demand for technological progress...

Screening of Mangifera indica L. functional content using PCA and neural networks (ANN).

Food chemistry
A method using reversed-phase high-performance liquid chromatography with diode array detection (HPLC-DAD) was applied after extraction with acidified methanol, to determine 12 bioactive phenolic compounds in the peel and pulp of the mango fruit (Man...

Prediction of activation patterns preceding hallucinations in patients with schizophrenia using machine learning with structured sparsity.

Human brain mapping
Despite significant progress in the field, the detection of fMRI signal changes during hallucinatory events remains difficult and time-consuming. This article first proposes a machine-learning algorithm to automatically identify resting-state fMRI pe...

Multiscale High-Level Feature Fusion for Histopathological Image Classification.

Computational and mathematical methods in medicine
Histopathological image classification is one of the most important steps for disease diagnosis. We proposed a method for multiclass histopathological image classification based on deep convolutional neural network referred to as coding network. It c...

Gas chromatography-mass spectrometry metabolomic study of lipopolysaccharides toxicity on rat basophilic leukemia cells.

Chemico-biological interactions
Lipopolysaccharide (LPS) can lead to uncontrollable cytokine production, fatal sepsis syndrome and depression/multiple organ failure, as pathophysiologic demonstration. Various toxic effects of LPS have been extensively reported, mainly on the toxici...

Detection of American Football Head Impacts Using Biomechanical Features and Support Vector Machine Classification.

Scientific reports
Accumulation of head impacts may contribute to acute and long-term brain trauma. Wearable sensors can measure impact exposure, yet current sensors do not have validated impact detection methods for accurate exposure monitoring. Here we demonstrate a ...

Epileptic seizure detection in EEG signal using machine learning techniques.

Australasian physical & engineering sciences in medicine
Epilepsy is a well-known nervous system disorder characterized by seizures. Electroencephalograms (EEGs), which capture brain neural activity, can detect epilepsy. Traditional methods for analyzing an EEG signal for epileptic seizure detection are ti...