AIMC Topic: Cluster Analysis

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SIGNET: single-cell RNA-seq-based gene regulatory network prediction using multiple-layer perceptron bagging.

Briefings in bioinformatics
High-throughput single-cell RNA-seq data have provided unprecedented opportunities for deciphering the regulatory interactions among genes. However, such interactions are complex and often nonlinear or nonmonotonic, which makes their inference using ...

Deep learning tackles single-cell analysis-a survey of deep learning for scRNA-seq analysis.

Briefings in bioinformatics
Since its selection as the method of the year in 2013, single-cell technologies have become mature enough to provide answers to complex research questions. With the growth of single-cell profiling technologies, there has also been a significant incre...

A comparison of deep learning-based pre-processing and clustering approaches for single-cell RNA sequencing data.

Briefings in bioinformatics
The emergence of single cell RNA sequencing has facilitated the studied of genomes, transcriptomes and proteomes. As available single-cell RNA-seq datasets are released continuously, one of the major challenges facing traditional RNA analysis tools i...

Using Machine Learning to Improve Personalised Prediction: A Data-Driven Approach to Segment and Stratify Populations for Healthcare.

Studies in health technology and informatics
Population Health Management typically relies on subjective decisions to segment and stratify populations. This study combines unsupervised clustering for segmentation and supervised classification, personalised to clusters, for stratification. An in...

Deep neural learning based protein function prediction.

Mathematical biosciences and engineering : MBE
It is vital for the annotation of uncharacterized proteins by protein function prediction. At present, Deep Neural Network based protein function prediction is mainly carried out for dataset of small scale proteins or Gene Ontology, and usually explo...

SUBPLEX: A Visual Analytics Approach to Understand Local Model Explanations at the Subpopulation Level.

IEEE computer graphics and applications
Understanding the interpretation of machine learning (ML) models has been of paramount importance when making decisions with societal impacts, such as transport control, financial activities, and medical diagnosis. While local explanation techniques ...

Introduction to Machine Learning in Neuroimaging.

Acta neurochirurgica. Supplement
Advancements in neuroimaging and the availability of large-scale datasets enable the use of more sophisticated machine learning algorithms. In this chapter, we non-exhaustively discuss relevant analytical steps for the analysis of neuroimaging data u...

Machine Learning-Based Clustering Analysis: Foundational Concepts, Methods, and Applications.

Acta neurochirurgica. Supplement
Unsupervised learning, the task of clustering observations in such a way that observations within cluster are more similar than those assigned to other clusters is one the central tasks of data science. Its exploratory and descriptive nature make it ...

Hubness weighted SVM ensemble for prediction of breast cancer subtypes.

Technology and health care : official journal of the European Society for Engineering and Medicine
BACKGROUND: Breast cancer is a major disease causing panic among women worldwide. Since gene mutations are the root cause for cancer development, analyzing gene expressions can give more insights into various phenotype of cancer treatments. Breast Ca...