Morning Overview on MSN

Scientists build a ‘periodic table’ for AI models

Scientists are trying to tame the chaos of modern artificial intelligence by doing something very old fashioned: drawing a ...
eLife

Separating selection from mutation in antibody language models

This important study introduces a new biology-informed strategy for deep learning models aiming to predict mutational effects in antibody sequences. It provides solid evidence that separating ...
ahajournals.org

Abstract 4361575: Phenotyping Cardiogenic Shock Using Unsupervised Machine Learning: A Nationwide Cluster Analysis from National Inpatient Sample

Introduction: Cardiogenic shock (CS) is a heterogeneous clinical syndrome, with varied clinical outcomes driven by hemodynamic states, and initial presentation. However, unsupervised machine learning ...
Frontiers

The value of unsupervised machine learning algorithms based on CT and MRI for predicting sarcopenia

Objectives: This study aims to investigate the efficacy of unsupervised machine learning algorithms, specifically the Gaussian Mixture Model (GMM), K-means clustering, and Otsu automatic threshold ...
Scientific Research Publishing

Chaudhry, M., Shafi, I., Mahnoor, M., Vargas, D.L.R., Thompson, E.B. and Ashraf, I. (2023) A Systematic Literature Review on Identifying Patterns Using Unsupervised Clustering ...

ABSTRACT: Doping is an issue associated with elite sports as athletes attempt to enhance their performance to gain an edge over other athletes. However, the prevalence of doping is continuously ...
C&EN

Cluster-Based Machine Learning Potentials to Describe Disordered Metal–Organic Frameworks up to the Mesoscale

Article subjects are automatically applied from the ACS Subject Taxonomy and describe the scientific concepts and themes of the article. To achieve our goal, we introduce the chemical environment of ...
IEEE

An Unsupervised Machine Learning to Optimize Hybrid Quantum Noise Clusters for Gaussian Quantum Channel

Abstract: Quantum communication systems are essential for secure information transmission, but their performance is significantly impeded by complex hybrid quantum noise (HQN) in Gaussian quantum ...
IEEE

An Unsupervised Correlation Learning-Based Clustering Model for Multiple Complex Lesions Evaluation

Lesion morphology and quantity evaluation in computer tomography (CT) images are critical for precise disease diagnosis. Most existing methods employ machine learning-based methods to separately ...
Frontiers

Machine learning-driven prediction model for cuproptosis-related genes in spinal cord injury: construction and experimental validation

Introduction: Spinal cord injury (SCI) severely affects the central nervous system. Copper homeostasis is closely related to mitochondrial regulation, and cuproptosis is a novel form of cell death ...