AIMC Topic: Cluster Analysis

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Knowledge-based classification of fine-grained immune cell types in single-cell RNA-Seq data.

Briefings in bioinformatics
Single-cell RNA sequencing (scRNA-Seq) is an emerging strategy for characterizing immune cell populations. Compared to flow or mass cytometry, scRNA-Seq could potentially identify cell types and activation states that lack precise cell surface marker...

jSRC: a flexible and accurate joint learning algorithm for clustering of single-cell RNA-sequencing data.

Briefings in bioinformatics
Single-cell RNA-sequencing (scRNA-seq) explores the transcriptome of genes at cell level, which sheds light on revealing the heterogeneity and dynamics of cell populations. Advances in biotechnologies make it possible to generate scRNA-seq profiles f...

Hidden coexisting firings in fractional-order hyperchaotic memristor-coupled HR neural network with two heterogeneous neurons and its applications.

Chaos (Woodbury, N.Y.)
The firing patterns of each bursting neuron are different because of the heterogeneity, which may be derived from the different parameters or external drives of the same kind of neurons, or even neurons with different functions. In this paper, the di...

Unsupervised flow cytometry analysis in hematological malignancies: A new paradigm.

International journal of laboratory hematology
Ever since hematopoietic cells became "events" enumerated and characterized in suspension by cell counters or flow cytometers, researchers and engineers have strived to refine the acquisition and display of the electronic signals generated. A large a...

A data-driven approach to post-stroke aphasia classification and lesion-based prediction.

Brain : a journal of neurology
Aphasia is an acquired impairment in the production or comprehension of language, typically caused by left hemisphere stroke. The subtyping framework used in clinical aphasiology today is based on the Wernicke-Lichtheim model of aphasia formulated in...

Clustering Results Interpretation of Continuous Variables Using Bayesian Inference.

Studies in health technology and informatics
The present study is devoted to interpretable artificial intelligence in medicine. In our previous work we proposed an approach to clustering results interpretation based on Bayesian Inference. As an application case we used clinical pathways cluster...

Semantic Anomaly Detection in Medical Time Series.

Studies in health technology and informatics
The main goal of this project was to define and evaluate a new unsupervised deep learning approach that can differentiate between normal and anomalous intervals of signals like the electrical activity of the heart (ECG). Denoising autoencoders based ...

FS-GBDT: identification multicancer-risk module via a feature selection algorithm by integrating Fisher score and GBDT.

Briefings in bioinformatics
Cancer is a highly heterogeneous disease caused by dysregulation in different cell types and tissues. However, different cancers may share common mechanisms. It is critical to identify decisive genes involved in the development and progression of can...

A novel deep learning method for predictive modeling of microbiome data.

Briefings in bioinformatics
With the development and decreasing cost of next-generation sequencing technologies, the study of the human microbiome has become a rapid expanding research field, which provides an unprecedented opportunity in various clinical applications such as d...

Merged Affinity Network Association Clustering: Joint multi-omic/clinical clustering to identify disease endotypes.

Cell reports
Although clinical and laboratory data have long been used to guide medical practice, this information is rarely integrated with multi-omic data to identify endotypes. We present Merged Affinity Network Association Clustering (MANAclust), a coding-fre...