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HaploCoV: unsupervised classification and rapid detection of novel emerging variants of SARS-CoV-2
Accurate and timely monitoring of the evolution of SARS-CoV-2 is crucial for identifying and tracking potentially more transmissible/virulent viral...
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Unsupervised encoding selection through ensemble pruning for biomedical classification
BackgroundOwing to the rising levels of multi-resistant pathogens, antimicrobial peptides, an alternative strategy to classic antibiotics, got more...
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Identifying strawberry appearance quality based on unsupervised deep learning
The strawberry appearance is an essential standard for judging the quality, so it is crucial to accurately identify the strawberry appearance quality...
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Binning Metagenomic Contigs Using Unsupervised Clustering and Reference Databases
Metagenomics can directly extract the genetic material of all microorganisms from the environment, and obtain metagenomic samples with a large number...
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Unsupervised and supervised discovery of tissue cellular neighborhoods from cell phenotypes
It is poorly understood how different cells in a tissue organize themselves to support tissue functions. We describe the CytoCommunity algorithm for...
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Unsupervised deep representation learning enables phenotype discovery for genetic association studies of brain imaging
Understanding the genetic architecture of brain structure is challenging, partly due to difficulties in designing robust, non-biased descriptors of...
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Neural encoding with unsupervised spiking convolutional neural network
Accurately predicting the brain responses to various stimuli poses a significant challenge in neuroscience. Despite recent breakthroughs in neural...
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Building RadiologyNET: an unsupervised approach to annotating a large-scale multimodal medical database
BackgroundThe use of machine learning in medical diagnosis and treatment has grown significantly in recent years with the development of...
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Hierarchical classification-based pan-cancer methylation analysis to classify primary cancer
Hierarchical classification offers a more specific categorization of data and breaks down large classification problems into subproblems, providing...
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Improvement of gram staining effect by ethanol pretreatment and quantization of staining image by unsupervised machine learning
In this study, we propose an Ethanol Pretreatment Gram staining method that significantly enhances the color contrast of the stain, thereby improving...
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A robust approach to 3D neuron shape representation for quantification and classification
We consider the problem of finding an accurate representation of neuron shapes, extracting sub-cellular features, and classifying neurons based on...
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Unsupervised Classification of Some Bacteria with 16S RNA Genes
We used unsupervised nonlinear clustering to reveal the interplay between structure of nucleotide sequences and the taxonomy of their bearers....
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Canopy defoliation by leaf-cutting ants in eucalyptus plantations inferred by unsupervised machine learning applied to remote sensing
Defoliation by leaf-cutting ants alters the physiological processes of plants, and this defoliation can be inferred from satellite imagery used to...
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HycDemux: a hybrid unsupervised approach for accurate barcoded sample demultiplexing in nanopore sequencing
DNA barcodes enable Oxford Nanopore sequencing to sequence multiple barcoded DNA samples on a single flow cell. DNA sequences with the same barcode...
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Unsupervised modeling of mutational landscapes of adeno-associated viruses viability
Adeno-associated viruses 2 (AAV2) are minute viruses renowned for their capacity to infect human cells and akin organisms. They have recently emerged...
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DiviK: divisive intelligent K-means for hands-free unsupervised clustering in big biological data
BackgroundInvestigating molecular heterogeneity provides insights into tumour origin and metabolomics. The increasing amount of data gathered makes...
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An unsupervised image segmentation algorithm for coronary angiography
Computer visual systems can rapidly obtain a large amount of data and automatically process them with ease. These characteristics constitute...
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Unsupervised Clustering at the Service of Automatic Anomaly Detection in Industry 4.0
Industrial processes are among the most complex systems, for they are dynamic, nonlinear and comprise many interdependent parts. In the scope of the...
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Unsupervised explainable AI for molecular evolutionary study of forty thousand SARS-CoV-2 genomes
BackgroundUnsupervised AI (artificial intelligence) can obtain novel knowledge from big data without particular models or prior knowledge and is...
