AIMC Topic: Unsupervised Machine Learning
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Pathway Enrichment-Based Unsupervised Learning Identifies Novel Subtypes of Cancer-Associated Fibroblasts in Pancreatic Ductal Adenocarcinoma.
Interdisciplinary sciences, computational life sciences
Jun 1, 2025
Existing single-cell clustering methods are based on gene expressions that are susceptible to dropout events in single-cell RNA sequencing (scRNA-seq) data. To overcome this limitation, we proposed a pathway-based clustering method for single cells (...
GAN-based synthetic FDG PET images from T1 brain MRI can serve to improve performance of deep unsupervised anomaly detection models.
Computer methods and programs in biomedicine
Jun 1, 2025
BACKGROUND AND OBJECTIVE: Research in the cross-modal medical image translation domain has been very productive over the past few years in tackling the scarce availability of large curated multi-modality datasets with the promising performance of GAN...
scDMSC: Deep Multi-View Subspace Clustering for Single-Cell Multi-Omics Data.
IEEE journal of biomedical and health informatics
Jun 1, 2025
Single-cell multi-omics sequencing technology comprehensively considers various molecular features to reveal the complexity of cells information. The clustering analysis of multi-omics data provides new insight into cellular heterogeneity. However, m...
ISGAN: Unsupervised Domain Adaptation With Improved Symmetric GAN for Cross-Modality Multi-Organ Segmentation.
IEEE journal of biomedical and health informatics
Jun 1, 2025
The differences between cross-modality medical images are significant, so several studies are working on unsupervised domain adaptation (UDA) segmentation, which aims to adapt a segmentation model trained on a labeled source domain to an unlabeled ta...
Explicit Abnormality Extraction for Unsupervised Motion Artifact Reduction in Magnetic Resonance Imaging.
IEEE journal of biomedical and health informatics
Jun 1, 2025
Motion artifacts compromise the quality of magnetic resonance imaging (MRI) and pose challenges to achieving diagnostic outcomes and image-guided therapies. In recent years, supervised deep learning approaches have emerged as successful solutions for...
Unsupervised post-training learning in spiking neural networks.
Scientific reports
May 21, 2025
The human brain is a dynamic system that is constantly learning. It employs a combination of various learning strategies to facilitate complex learning processes. However, implementing biological learning mechanisms into Spiking Neural Networks (SNNs...
Characterizing ASD Subtypes Using Morphological Features from sMRI with Unsupervised Learning.
Studies in health technology and informatics
May 15, 2025
In this study, we attempted to identify the subtypes of autism spectrum disorder (ASD) with the help of anatomical alterations found in structural magnetic resonance imaging (sMRI) data of the ASD brain and machine learning tools. Initially, the sMRI...
Optimizing unsupervised feature engineering and classification pipelines for differentiated thyroid cancer recurrence prediction.
BMC medical informatics and decision making
May 13, 2025
BACKGROUND: Differentiated thyroid cancer (DTC) is a common endocrine malignancy with rising incidence and frequent recurrence, despite a generally favorable prognosis. Accurate recurrence prediction is critical for guiding post-treatment strategies....
Unsupervised machine learning-based stratification and immune deconvolution of liver hepatocellular carcinoma.
BMC cancer
May 10, 2025
BACKGROUND: Hepatocellular carcinoma (HCC) is the most prevalent type of liver cancer and a leading cause of cancer-related deaths globally. The tumour microenvironment (TME) influences treatment response and prognosis, yet its heterogeneity remains ...
Rhythm profiling using COFE reveals multi-omic circadian rhythms in human cancers in vivo.
PLoS biology
May 1, 2025
The study of ubiquitous circadian rhythms in human physiology requires regular measurements across time. Repeated sampling of the different internal tissues that house circadian clocks is both practically and ethically infeasible. Here, we present a ...
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