AIMC Topic: Cluster Analysis

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Multi-omic integration by machine learning (MIMaL).

Bioinformatics (Oxford, England)
MOTIVATION: Cells respond to environments by regulating gene expression to exploit resources optimally. Recent advances in technologies allow for measuring the abundances of RNA, proteins, lipids and metabolites. These highly complex datasets reflect...

Guided interactive image segmentation using machine learning and color-based image set clustering.

Bioinformatics (Oxford, England)
MOTIVATION: Over the last decades, image processing and analysis have become one of the key technologies in systems biology and medicine. The quantification of anatomical structures and dynamic processes in living systems is essential for understandi...

Programmed for Automatic Bone Disorder Clustering Based on Cumulative Calcium Prediction for Feature Extraction.

Clinical laboratory
BACKGROUND: The prediction of bone disorders varies between ortho-physicians. A precise bone disorder cataloging system is proposed based on a renewed method for estimating calcium value from a radiological image of the bone.

[A protein complex recognition method based on spatial-temporal graph convolution neural network].

Nan fang yi ke da xue xue bao = Journal of Southern Medical University
OBJECTIVE: To propose a new method for mining complexes in dynamic protein network using spatiotemporal convolution neural network.

Comparing Prediction of Early TBI Mortality with Multilayer Perceptron Neural Network and Convolutional Neural Network.

Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference
In this work, we compare the performance of a multilayer perceptron neural network and convolutional networks for the prediction of 14-day mortality in patients with TBI, using a database obtained in a low-and middle-income country, with 529 records ...

Clustering Nursing Sentences - Comparing Three Sentence Embedding Methods.

Studies in health technology and informatics
In health sciences, high-quality text embeddings may augment qualitative data analysis of large amounts of text by enabling, e.g., searching and clustering of health information. This study aimed to evaluate three different sentence-level embedding m...

Dynamic community detection over evolving networks based on the optimized deep graph infomax.

Chaos (Woodbury, N.Y.)
As complex systems, dynamic networks have obvious nonlinear features. Detecting communities in dynamic networks is of great importance for understanding the functions of networks and mining evolving relationships. Recently, some network embedding-bas...

Deep structural clustering for single-cell RNA-seq data jointly through autoencoder and graph neural network.

Briefings in bioinformatics
Single-cell RNA sequencing (scRNA-seq) permits researchers to study the complex mechanisms of cell heterogeneity and diversity. Unsupervised clustering is of central importance for the analysis of the scRNA-seq data, as it can be used to identify put...

A deep learning framework for characterization of genotype data.

G3 (Bethesda, Md.)
Dimensionality reduction is a data transformation technique widely used in various fields of genomics research. The application of dimensionality reduction to genotype data is known to capture genetic similarity between individuals, and is used for v...