AIMC Topic: Multivariate Analysis

Clear Filters Showing 1 to 10 of 262 articles

Machine learning-based integration of tumor deposit molecular signatures improves prognostic stratification in colon adenocarcinoma.

International journal of colorectal disease
BACKGROUND: Colon adenocarcinoma (COAD) remains a leading cause of cancer-related mortality worldwide. Although tumor deposits (TDs) are established prognostic indicators, their molecular characteristics and potential for improving risk stratificatio...

ML-MAGES enables multivariate genetic association analyses with genes and effect size shrinkage.

Genome research
A fundamental goal of genetics is to identify which and how genetic variants are associated with a trait, often using the regression results from genome-wide association (GWA) studies. Important methodological challenges account for inflation in GWA ...

Multivariate Functional Data Analysis Uncovers Behavioral Fingerprints in Invertebrate Locomotor Response to Micropollutants.

Environmental science & technology
The need for effective biomonitoring in wastewater has become clear due to the impracticality of continuously tracking all chemicals and emerging contaminants in the aquatic exposome. Effect-based biomonitoring provides a cost-effective solution. The...

A frugal Spiking Neural Network for unsupervised multivariate temporal pattern classification and multichannel spike sorting.

Nature communications
Advanced large-scale neural interfaces call for efficient algorithms to automatically process and optimally exploit the richness of their heavy continuous flow of data. In this context, we introduce here a very frugal generic single-layer Spiking Neu...

Surface water quality evaluation impacting drinking water sources and sanitation using water quality index, multivariate techniques, and interpretable machine learning models in Mahanadi River, Odisha (India).

Environmental geochemistry and health
Water quality and quantity affect crop productivity, with surface water quality having a significant impact. The amount of surface water being used for drinking is gradually rising. Thus, assessing surface water quality and related hydro-chemical cha...

Determination of geographical origin of Hovenia dulcis found in Korea and China via inorganic element analysis using inductively coupled plasma spectroscopy and multivariate statistical analysis.

Food chemistry
The fruit of Hovenia dulcis, a popular Korean hangover remedy, is marketed as Korean and Chinese products. To protect domestic producers, ensure accurate labeling, and safeguard consumers, this study discriminated origin through inorganic element ana...

Revealing gait as a murine biomarker of injury, disease, and age with multivariate statistics and machine learning.

Scientific reports
Hundreds of rodent gait studies have been published over the past two decades, according to a PubMed search. Treadmill gait data, for example from the DigiGait system, generates over 30 + spatial and temporal measures. Despite this multi-dimensional ...

Classification and quantification of sesame oil in edible oils and adulterated mixtures using H NMR spectroscopy combined with multivariate, machine learning, and deep learning models.

Food chemistry
Sesame oil is often adulterated with cheaper oils, necessitating accurate authentication and quantification methods. This study investigates the performance of AI-based models using H NMR spectral data for edible oil classification and sesame oil qua...

A data-driven approach to forest health assessment through multivariate analysis and machine learning techniques.

BMC plant biology
BACKGROUND: Himalayan forests are fragile, rich in biodiversity, and face increasing threats from anthropogenic pressures and climate change. Assessing their health is critical for sustainable forest management. This study integrated ecological indic...