AI Medical Compendium Topic:
Cluster Analysis

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Logic Learning Machine and standard supervised methods for Hodgkin's lymphoma prognosis using gene expression data and clinical variables.

Health informatics journal
This study evaluates the performance of a set of machine learning techniques in predicting the prognosis of Hodgkin's lymphoma using clinical factors and gene expression data. Analysed samples from 130 Hodgkin's lymphoma patients included a small set...

Classification of adult human dentate nucleus border neurons: Artificial neural networks and multidimensional approach.

Journal of theoretical biology
AIMS: Primary aim in this study is to investigate whether external and internal border neurons of adult human dentate nucleus express the same neuromorphological features or belong to a different morphological types i.e. whether can be classified not...

A segmentation and classification scheme for single tooth in MicroCT images based on 3D level set and k-means+.

Computerized medical imaging and graphics : the official journal of the Computerized Medical Imaging Society
Accurate classification of different anatomical structures of teeth from medical images provides crucial information for the stress analysis in dentistry. Usually, the anatomical structures of teeth are manually labeled by experienced clinical doctor...

Information bottleneck based incremental fuzzy clustering for large biomedical data.

Journal of biomedical informatics
Incremental fuzzy clustering combines advantages of fuzzy clustering and incremental clustering, and therefore is important in classifying large biomedical literature. Conventional algorithms, suffering from data sparsity and high-dimensionality, oft...

A kernel-based clustering method for gene selection with gene expression data.

Journal of biomedical informatics
Gene selection is important for cancer classification based on gene expression data, because of high dimensionality and small sample size. In this paper, we present a new gene selection method based on clustering, in which dissimilarity measures are ...

Representing higher-order dependencies in networks.

Science advances
To ensure the correctness of network analysis methods, the network (as the input) has to be a sufficiently accurate representation of the underlying data. However, when representing sequential data from complex systems, such as global shipping traffi...

Link-Prediction Enhanced Consensus Clustering for Complex Networks.

PloS one
Many real networks that are collected or inferred from data are incomplete due to missing edges. Missing edges can be inherent to the dataset (Facebook friend links will never be complete) or the result of sampling (one may only have access to a port...

Clustering Single-Cell Expression Data Using Random Forest Graphs.

IEEE journal of biomedical and health informatics
Complex tissues such as brain and bone marrow are made up of multiple cell types. As the study of biological tissue structure progresses, the role of cell-type-specific research becomes increasingly important. Novel sequencing technology such as sing...

Evaluating clustering methods within the Artificial Ecosystem Algorithm and their application to bike redistribution in London.

Bio Systems
This paper proposes and evaluates a solution to the truck redistribution problem prominent in London's Santander Cycle scheme. Due to the complexity of this NP-hard combinatorial optimisation problem, no efficient optimisation techniques are known to...

Pattern Classification of Instantaneous Cognitive Task-load Through GMM Clustering, Laplacian Eigenmap, and Ensemble SVMs.

IEEE/ACM transactions on computational biology and bioinformatics
The identification of the temporal variations in human operator cognitive task-load (CTL) is crucial for preventing possible accidents in human-machine collaborative systems. Recent literature has shown that the change of discrete CTL level during hu...