AI Medical Compendium Topic:
Cluster Analysis

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Multiview learning for understanding functional multiomics.

PLoS computational biology
The molecular mechanisms and functions in complex biological systems currently remain elusive. Recent high-throughput techniques, such as next-generation sequencing, have generated a wide variety of multiomics datasets that enable the identification ...

Multi-view projected clustering with graph learning.

Neural networks : the official journal of the International Neural Network Society
Graph based multi-view learning is well known due to its effectiveness and good clustering performance. However, most existing methods directly construct graph from original high-dimensional data which always contain redundancy, noise and outlying en...

Machine learning for cluster analysis of localization microscopy data.

Nature communications
Quantifying the extent to which points are clustered in single-molecule localization microscopy data is vital to understanding the spatial relationships between molecules in the underlying sample. Many existing computational approaches are limited in...

Hybrid Fuzzy Clustering Method Based on FCM and Enhanced Logarithmical PSO (ELPSO).

Computational intelligence and neuroscience
Fuzzy c-means (FCM) is one of the best-known clustering methods to organize the wide variety of datasets automatically and acquire accurate classification, but it has a tendency to fall into local minima. For overcoming these weaknesses, some methods...

Soft Clustering for Enhancing the Diagnosis of Chronic Diseases over Machine Learning Algorithms.

Journal of healthcare engineering
Chronic diseases represent a serious threat to public health across the world. It is estimated at about 60% of all deaths worldwide and approximately 43% of the global burden of chronic diseases. Thus, the analysis of the healthcare data has helped h...

An Introduction to Machine Learning.

Clinical pharmacology and therapeutics
In the last few years, machine learning (ML) and artificial intelligence have seen a new wave of publicity fueled by the huge and ever-increasing amount of data and computational power as well as the discovery of improved learning algorithms. However...

Graph embedding and unsupervised learning predict genomic sub-compartments from HiC chromatin interaction data.

Nature communications
Chromatin interaction studies can reveal how the genome is organized into spatially confined sub-compartments in the nucleus. However, accurately identifying sub-compartments from chromatin interaction data remains a challenge in computational biolog...

Fusing Self-Organized Neural Network and Keypoint Clustering for Localized Real-Time Background Subtraction.

International journal of neural systems
Moving object detection in video streams plays a key role in many computer vision applications. In particular, separation between background and foreground items represents a main prerequisite to carry out more complex tasks, such as object classific...

Multiple Discrimination and Pairwise CNN for view-based 3D object retrieval.

Neural networks : the official journal of the International Neural Network Society
With the rapid development and wide application of computer, camera device, network and hardware technology, 3D object (or model) retrieval has attracted widespread attention and it has become a hot research topic in the computer vision domain. Deep ...

Machine learning helps identifying volume-confounding effects in radiomics.

Physica medica : PM : an international journal devoted to the applications of physics to medicine and biology : official journal of the Italian Association of Biomedical Physics (AIFB)
Highlighting the risk of biases in radiomics-based models will help improve their quality and increase usage as decision support systems in the clinic. In this study we use machine learning-based methods to identify the presence of volume-confounding...