AIMC Topic: Unsupervised Machine Learning

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Characterization of clinical patterns of dengue patients using an unsupervised machine learning approach.

BMC infectious diseases Jul 22, 2019
BACKGROUND: Despite the greater sensitivity of the new dengue clinical classification proposed by the World Health Organization (WHO) in 2009, there is a need for a better definition of warning signs and clinical progression of dengue cases. Classic ...
Adult Stochastic Processes Cross-Sectional Studies Dengue Male Age Distribution Unsupervised Machine Learning Retrospective Studies Abdominal Pain Female Aged Middle Aged Platelet Count Adolescent Brazil Humans Infant Child, Preschool Algorithms Child
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AULD: Large Scale Suspicious DNS Activities Detection via Unsupervised Learning in Advanced Persistent Threats.

Sensors (Basel, Switzerland) Jul 19, 2019
In recent years, sensors in the Internet of things have been commonly used in Human's life. APT (Advanced Persistent Threats) has caused serious damage to network security and the sensors play an important role in the attack process. For a long time,...
Humans Software Algorithms Unsupervised Machine Learning Computer Security
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Unsupervised Learning Techniques for the Investigation of Chronic Rhinosinusitis.

The Annals of otology, rhinology, and laryngology Jul 18, 2019
OBJECTIVES: This article reviews the principles of unsupervised learning, a novel technique which has increasingly been reported as a tool for the investigation of chronic rhinosinusitis (CRS). It represents a paradigm shift from the traditional appr...
Humans Deep Learning Unsupervised Machine Learning Chronic Disease Sinusitis Rhinitis
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A demonstration of unsupervised machine learning in species delimitation.

Molecular phylogenetics and evolution Jul 16, 2019
One major challenge to delimiting species with genetic data is successfully differentiating population structure from species-level divergence, an issue exacerbated in taxa inhabiting naturally fragmented habitats. Many fields of science are now usin...
Animals Unsupervised Machine Learning Principal Component Analysis Phylogeny Arachnida Polymorphism, Single Nucleotide Cluster Analysis
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Unsupervised learning of probabilistic diffeomorphic registration for images and surfaces.

Medical image analysis Jul 12, 2019
Classical deformable registration techniques achieve impressive results and offer a rigorous theoretical treatment, but are computationally intensive since they solve an optimization problem for each image pair. Recently, learning-based methods have ...
Magnetic Resonance Imaging Models, Statistical Unsupervised Machine Learning Brain Pattern Recognition, Automated Imaging, Three-Dimensional Humans Image Processing, Computer-Assisted Neural Networks, Computer Algorithms
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PolyCRACKER, a robust method for the unsupervised partitioning of polyploid subgenomes by signatures of repetitive DNA evolution.

BMC genomics Jul 12, 2019
BACKGROUND: Our understanding of polyploid genomes is limited by our inability to definitively assign sequences to a specific subgenome without extensive prior knowledge like high resolution genetic maps or genome sequences of diploid progenitors. In...
DNA, Plant Polyploidy Repetitive Sequences, Nucleic Acid Triticum Nicotiana Genome, Plant Genomics Phylogeny Evolution, Molecular Unsupervised Machine Learning
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Integrating biomedical research and electronic health records to create knowledge-based biologically meaningful machine-readable embeddings.

Nature communications Jul 10, 2019
In order to advance precision medicine, detailed clinical features ought to be described in a way that leverages current knowledge. Although data collected from biomedical research is expanding at an almost exponential rate, our ability to transform ...
Biomedical Research Unsupervised Machine Learning Data Collection Data Analysis Precision Medicine Electronic Health Records
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Deconvolution of autoencoders to learn biological regulatory modules from single cell mRNA sequencing data.

BMC bioinformatics Jul 8, 2019
BACKGROUND: Unsupervised machine learning methods (deep learning) have shown their usefulness with noisy single cell mRNA-sequencing data (scRNA-seq), where the models generalize well, despite the zero-inflation of the data. A class of neural network...
Neural Networks, Computer Gene Expression Profiling Single-Cell Analysis Unsupervised Machine Learning RNA, Messenger Sequence Analysis, RNA Cluster Analysis
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Unsupervised word embeddings capture latent knowledge from materials science literature.

Nature Jul 3, 2019
The overwhelming majority of scientific knowledge is published as text, which is difficult to analyse by either traditional statistical analysis or modern machine learning methods. By contrast, the main source of machine-interpretable data for the ma...
Research Temperature Electric Conductivity Materials Science Data Mining Research Report Semantics Terminology as Topic Magnetics Knowledge Natural Language Processing Iron Reproducibility of Results Electrodes Lithium Unsupervised Machine Learning
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Learning Compositional Representations of Interacting Systems with Restricted Boltzmann Machines: Comparative Study of Lattice Proteins.

Neural computation Jul 1, 2019
A restricted Boltzmann machine (RBM) is an unsupervised machine learning bipartite graphical model that jointly learns a probability distribution over data and extracts their relevant statistical features. RBMs were recently proposed for characterizi...
Models, Statistical Probability Proteins Static Electricity Principal Component Analysis Stochastic Processes Unsupervised Machine Learning Models, Molecular Computer Simulation Sequence Alignment Amino Acid Sequence
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