AIMC Topic: Multivariate Analysis

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Spectral resolution of quaternary components in a sinus and congestion mixture; Multivariate algorithms to approach extremes of concentration levels.

Spectrochimica acta. Part A, Molecular and biomolecular spectroscopy May 16, 2020
Sinus and congestion mixture of three drugs and an impurity was studied for their spectral resolution using four multivariate algorithms. The studied drugs present in extremes of low and high concentrations. Low concentration levels of phenylephrine ...
Least-Squares Analysis Calibration Multivariate Analysis Acetaminophen Neural Networks, Computer Algorithms
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Multivariate patterns of EEG microstate parameters and their role in the discrimination of patients with schizophrenia from healthy controls.

Psychiatry research Apr 6, 2020
Quasi-stable electrical fields in the EEG, called microstates carry information on the dynamics of large scale brain networks. Using machine learning techniques, we explored whether abnormalities in microstates can be used to classify patients with s...
Schizophrenia Multivariate Analysis Schizophrenic Psychology Young Adult Adult Male Diagnosis, Differential Electroencephalography Middle Aged Brain Mapping Machine Learning Brain Female Humans
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Simultaneous ultra-trace quantitative colorimetric determination of antidiabetic drugs based on gold nanoparticles aggregation using multivariate calibration and neural network methods.

Spectrochimica acta. Part A, Molecular and biomolecular spectroscopy Mar 12, 2020
In this study, a simple and rapid method was investigated for the simultaneous ultra-trace colorimetric determination of Metformin (MET) and Sitagliptin (STG) based on the aggregation of gold nanoparticles (AuNPs). The Morphology and size distributio...
Multivariate Analysis Neural Networks, Computer Analysis of Variance Reproducibility of Results Spectrophotometry, Ultraviolet Gold Least-Squares Analysis Metal Nanoparticles Metformin Colorimetry Calibration Sitagliptin Phosphate Hypoglycemic Agents
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Identifying the pulsed neuron networks' structures by a nonlinear Granger causality method.

BMC neuroscience Feb 12, 2020
BACKGROUND: It is a crucial task of brain science researches to explore functional connective maps of Biological Neural Networks (BNN). The maps help to deeply study the dominant relationship between the structures of the BNNs and their network funct...
Humans Neural Networks, Computer Algorithms Neurons Nonlinear Dynamics Multivariate Analysis Neural Pathways Brain Models, Neurological Brain Mapping
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DeepWAS: Multivariate genotype-phenotype associations by directly integrating regulatory information using deep learning.

PLoS computational biology Feb 3, 2020
Genome-wide association studies (GWAS) identify genetic variants associated with traits or diseases. GWAS never directly link variants to regulatory mechanisms. Instead, the functional annotation of variants is typically inferred by post hoc analyses...
Quantitative Trait Loci Polymorphism, Single Nucleotide Humans Genetic Association Studies Deep Learning Multivariate Analysis Genome-Wide Association Study
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Application of artificial neural network and multiple linear regression in modeling nutrient recovery in vermicompost under different conditions.

Bioresource technology Jan 29, 2020
Vermicomposting is one of the best technologies for nutrient recovery from solid waste. This study aims to assess the efficiency of Artificial Neural Network (ANN) and Multiple Linear Regression (MLR) models in predicting nutrient recovery from solid...
Linear Models Multivariate Analysis Nutrients Phosphorus Neural Networks, Computer
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Development of a Novel Prognostic Model to Predict 6-Month Swallowing Recovery After Ischemic Stroke.

Stroke Dec 30, 2019
Background and Purpose- The aim of this study was to explore clinical and radiological prognostic factors for long-term swallowing recovery in patients with poststroke dysphagia and to develop and validate a prognostic model using a machine learning ...
Multivariate Analysis Aged Brain Stem Sex Factors Cerebral Cortex Male White Matter Prognosis Internal Capsule Deglutition Disorders Middle Aged Enteral Nutrition Brain Ischemia Female Fluoroscopy Magnetic Resonance Imaging Basal Ganglia Proportional Hazards Models Humans Reproducibility of Results Stroke Recovery of Function Machine Learning Algorithms
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Predicting the individual risk of psychosis conversion in at-risk mental state (ARMS): a multivariate model reveals the influence of nonpsychotic prodromal symptoms.

European child & adolescent psychiatry Dec 23, 2019
To improve the prediction of the individual risk of conversion to psychosis in UHR subjects, by considering all CAARMS' symptoms at first presentation and using a multivariate machine learning method known as logistic regression with Elastic-net shri...
Psychotic Disorders Multivariate Analysis Prodromal Symptoms Humans Male Female Machine Learning Psychiatric Status Rating Scales Risk Factors Adolescent Young Adult
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Application of a kNN-based similarity method to biopharmaceutical manufacturing.

Biotechnology progress Dec 16, 2019
Machine learning-based similarity analysis is commonly found in many artificial intelligence applications like the one utilized in e-commerce and digital marketing. In this study, a kNN-based (k-nearest neighbors) similarity method is proposed for ra...
Biological Products Drug Industry Humans Multivariate Analysis Machine Learning
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A hybrid self-attention deep learning framework for multivariate sleep stage classification.

BMC bioinformatics Dec 2, 2019
BACKGROUND: Sleep is a complex and dynamic biological process characterized by different sleep patterns. Comprehensive sleep monitoring and analysis using multivariate polysomnography (PSG) records has achieved significant efforts to prevent sleep-re...
Databases as Topic Deep Learning Multivariate Analysis Sleep Stages ROC Curve Humans Electroencephalography Polysomnography Algorithms
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