AIMC Topic: Cluster Analysis

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Hypothesis generation for rare and undiagnosed diseases through clustering and classifying time-versioned biological ontologies.

PloS one
Rare diseases affect 1-in-10 people in the United States and despite increased genetic testing, up to half never receive a diagnosis. Even when using advanced genome sequencing platforms to discover variants, if there is no connection between the var...

Machine Learning-Driven Identification of Distinct Persistent Atrial Fibrillation Phenotypes: A Cluster Analysis of DECAAF II.

Journal of cardiovascular electrophysiology
INTRODUCTION: Catheter ablation of persistent atrial fibrillation yields sub-optimal success rates partly due to the considerable heterogeneity within the patient population. Identifying distinct patient phenotypes based on post-ablation prognosis co...

NMF typing and machine learning algorithm-based exploration of preeclampsia-related mechanisms on ferroptosis signature genes.

Cell biology and toxicology
BACKGROUND: Globally, pre-eclampsia (PE) poses a major threat to the health and survival of pregnant women and fetuses, contributing significantly to morbidity and mortality. Recent studies suggest a pathological link between PE and ferroptosis. We a...

MFC-ACL: Multi-view fusion clustering with attentive contrastive learning.

Neural networks : the official journal of the International Neural Network Society
Multi-view clustering can better handle high-dimensional data by combining information from multiple views, which is important in big data mining. However, the existing models which simply perform feature fusion after feature extraction for individua...

Personalized Clustering for Emotion Recognition Improvement.

Sensors (Basel, Switzerland)
Emotion recognition through artificial intelligence and smart sensing of physical and physiological signals (affective computing) is achieving very interesting results in terms of accuracy, inference times, and user-independent models. In this sense,...

Detecting cardiovascular diseases using unsupervised machine learning clustering based on electronic medical records.

BMC medical research methodology
BACKGROUND: Electronic medical records (EMR)-trained machine learning models have the potential in CVD risk prediction by integrating a range of medical data from patients, facilitate timely diagnosis and classification of CVDs. We tested the hypothe...

A novel machine learning model for perimeter intrusion detection using intrusion image dataset.

PloS one
Perimeter Intrusion Detection Systems (PIDS) are crucial for protecting any physical locations by detecting and responding to intrusions around its perimeter. Despite the availability of several PIDS, challenges remain in detection accuracy and preci...

SpatialCVGAE: Consensus Clustering Improves Spatial Domain Identification of Spatial Transcriptomics Using VGAE.

Interdisciplinary sciences, computational life sciences
The advent of spatially resolved transcriptomics (SRT) has provided critical insights into the spatial context of tissue microenvironments. Spatial clustering is a fundamental aspect of analyzing spatial transcriptomics data. However, spatial cluster...

Exploring phenotypes to improve long-term mortality risk stratification in obstructive sleep apnea through a machine learning approach: an observational cohort study.

European journal of internal medicine
BACKGROUND: Obstructive sleep apnea (OSA) is a heterogeneous sleep disorder for which the identification of phenotypes might help for risk stratification for long-term mortality. Thus, the aim of the study was to identify distinct phenotypes of OSA a...

Fuzzy-Based Identification of Transition Cells to Infer Cell Trajectory for Single-Cell Transcriptomics.

Journal of computational biology : a journal of computational molecular cell biology
With the continuous evolution of single-cell RNA sequencing technology, it has become feasible to reconstruct cell development processes using computational methods. Trajectory inference is a crucial downstream analytical task that provides valuable ...