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Multivariate Analysis

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Defining the biomarkers in anti-MRSA fractions of soil Streptomycetes by multivariate analysis.

Antonie van Leeuwenhoek
Methicillin-resistant Staphylococcus aureus (MRSA) is one of the most alarming antibiotic-resistant pathogens causing nosocomial and community-acquired infections. Actinomycetes, particularly Streptomycetes, have historically been a major source of n...

Leveraging infrared spectroscopy for cocoa content prediction: A dual approach with Kohonen neural network and multivariate modeling.

Spectrochimica acta. Part A, Molecular and biomolecular spectroscopy
People of all ages enjoy chocolate, and its popularity is attributed to its pleasant taste and aroma, as well as its associated health benefits. Produced through both artisanal and industrial processes, which involve harvesting, selecting, fermenting...

Rapid discrimination of different primary processing Arabica coffee beans using FT-IR and machine learning.

Food research international (Ottawa, Ont.)
In this study, fourier transform infrared spectroscopy (FT-IR) analysis was combined with machine learning, while various analytical techniques such as colorimetry, low-field nuclear magnetic resonance spectroscopy, scanning electron microscope, two-...

Dynamic graph-based bilateral recurrent imputation network for multivariate time series.

Neural networks : the official journal of the International Neural Network Society
Multivariate time series imputation using graph neural networks (GNNs) has gained significant attention, where the variables and their correlations are depicted as the graph nodes and edges, offering a structured way to understand the intricacies of ...

Machine Learning-Aided Intelligent Monitoring of Multivariate miRNA Biomarkers Using Bipolar Self-powered Sensors.

ACS nano
Breast cancer has become the most prevalent form of cancer among women on a global scale. The early and timely diagnosis of breast cancer is of the utmost importance for improving the survival rate of patients with this disease. The occurrence of bre...

Unveiling the potential of Brachiaria ruziziensis: Comparative analysis of multivariate and machine learning models for biomass and NPK prediction using Vis-NIR-SWIR spectroscopy.

Spectrochimica acta. Part A, Molecular and biomolecular spectroscopy
This study investigated the development and validation of predictive models for estimating foliar nitrogen (N), phosphorus (P), and potassium (K) contents, along with shoot dry mass (SDM) of Brachiaria ruziziensis L. The approach utilized Vis-NIR-SWI...

Hyperbolic multivariate feature learning in higher-order heterogeneous networks for drug-disease prediction.

Artificial intelligence in medicine
New drug discovery has always been a costly, time-consuming process with a high failure rate. Repurposing existing drugs offers a valuable alternative and reduces the risks associated with developing new drugs. Various experimental methods have been ...

Automated deep learning-based assessment of tumour-infiltrating lymphocyte density determines prognosis in colorectal cancer.

Journal of translational medicine
BACKGROUND: The presence of tumour-infiltrating lymphocytes (TILs) is a well-established prognostic biomarker across multiple cancer types, with higher TIL counts being associated with lower recurrence rates and improved patient survival. We aimed to...

Analysis of the most influential factors affecting outcomes of lung transplant recipients: a multivariate prediction model based on UNOS Data.

BMJ open
OBJECTIVES: In lung transplantation (LTx), a priority is assigned to each candidate on the waiting list. Our primary objective was to identify the key factors that influence the allocation of priorities in LTx using machine learning (ML) techniques t...