AIMC Topic: Cluster Analysis

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Application of Supervised and Unsupervised Learning Approaches for Mapping Storage Conditions of Biopharmaceutical Product-A Case Study of Human Serum Albumin.

Journal of chromatographic science
The stability of biopharmaceutical therapeutics over the storage period/shelf life has been a challenging concern for manufacturers. A noble strategy for mapping best and suitable storage conditions for recombinant human serum albumin (rHSA) in labor...

A scalable unsupervised learning of scRNAseq data detects rare cells through integration of structure-preserving embedding, clustering and outlier detection.

Briefings in bioinformatics
Single-cell RNA-seq analysis has become a powerful tool to analyse the transcriptomes of individual cells. In turn, it has fostered the possibility of screening thousands of single cells in parallel. Thus, contrary to the traditional bulk measurement...

Clustering Similar Diagnosis Terms.

Studies in health technology and informatics
A large clinical diagnosis list is explored with the goal to cluster syntactic variants. A string similarity heuristic is compared with a deep learning-based approach. Levenshtein distance (LD) applied to common words only (not tolerating deviations ...

DIST: spatial transcriptomics enhancement using deep learning.

Briefings in bioinformatics
Spatially resolved transcriptomics technologies enable comprehensive measurement of gene expression patterns in the context of intact tissues. However, existing technologies suffer from either low resolution or shallow sequencing depth. Here, we pres...

A noise-robust deep clustering of biomolecular ions improves interpretability of mass spectrometric images.

Bioinformatics (Oxford, England)
MOTIVATION: Mass Spectrometry Imaging (MSI) analyzes complex biological samples such as tissues. It simultaneously characterizes the ions present in the tissue in the form of mass spectra, and the spatial distribution of the ions across the tissue in...

Cancer subtyping with heterogeneous multi-omics data via hierarchical multi-kernel learning.

Briefings in bioinformatics
Differentiating cancer subtypes is crucial to guide personalized treatment and improve the prognosis for patients. Integrating multi-omics data can offer a comprehensive landscape of cancer biological process and provide promising ways for cancer dia...

Identification of spinal tuberculosis subphenotypes using routine clinical data: a study based on unsupervised machine learning.

Annals of medicine
OBJECTIVE: The identification of spinal tuberculosis subphenotypes is an integral component of precision medicine. However, we lack proper study models to identify subphenotypes in patients with spinal tuberculosis. Here we identified possible subphe...

Differential Expression, Functional and Machine Learning Analysis of High-Throughput -Omics Data Using Open-Source Tools.

Methods in molecular biology (Clifton, N.J.)
Today, -omics analyses, including the systematic cataloging of messenger RNA and microRNA sequences or DNA methylation patterns in a cell population, organ or tissue sample, allow for an unbiased, comprehensive genome-level analysis of complex diseas...

Morphologic clustering of earcanals using deep learning algorithm to design artificial ears dedicated to earplug attenuation measurement.

The Journal of the Acoustical Society of America
Designing earplugs adapted for the widest number of earcanals requires acoustical test fixtures (ATFs) geometrically representative of the population. Most existing ATFs are equipped with unique sized straight cylindrical earcanals, considered repres...

BindWeb: A web server for ligand binding residue and pocket prediction from protein structures.

Protein science : a publication of the Protein Society
Knowledge of protein-ligand interactions is beneficial for biological process analysis and drug design. Given the complexity of the interactions and the inadequacy of experimental data, accurate ligand binding residue and pocket prediction remains ch...