AIMC Topic:
Cluster Analysis

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Model-based unsupervised learning informs metformin-induced cell-migration inhibition through an AMPK-independent mechanism in breast cancer.

Oncotarget Apr 18, 2017
We demonstrate that model-based unsupervised learning can uniquely discriminate single-cell subpopulations by their gene expression distributions, which in turn allow us to identify specific genes for focused functional studies. This method was appli...
Cell Line, Tumor Humans Computational Biology Gene Expression Regulation, Neoplastic Signal Transduction AMP-Activated Protein Kinases Female Metformin Breast Neoplasms Gene Knockdown Techniques Gene Expression Profiling cdc42 GTP-Binding Protein Cell Movement Cluster Analysis Unsupervised Machine Learning
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Unsupervised consensus cluster analysis of [18F]-fluoroethyl-L-tyrosine positron emission tomography identified textural features for the diagnosis of pseudoprogression in high-grade glioma.

Oncotarget Jan 31, 2017
RATIONALE: Timely detection of pseudoprogression (PSP) is crucial for the management of patients with high-grade glioma (HGG) but remains difficult. Textural features of O-(2-[18F]fluoroethyl)-L-tyrosine positron emission tomography (FET-PET) mirror ...
Cluster Analysis Unsupervised Machine Learning Predictive Value of Tests Positron Emission Tomography Computed Tomography Treatment Outcome Glioma Magnetic Resonance Imaging Disease-Free Survival Kaplan-Meier Estimate Tyrosine Neoplasm Grading Radiographic Image Interpretation, Computer-Assisted Radiopharmaceuticals Adult Brain Neoplasms Male Pilot Projects Time Factors Middle Aged Disease Progression Female Reproducibility of Results Aged Humans Retrospective Studies
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Applying Risk Models on Patients with Unknown Predictor Values: An Incremental Learning Approach.

Studies in health technology and informatics Jan 1, 2017
In clinical practice, many patients may have unknown or missing values for some predictors, causing that the developed risk models cannot be directly applied on these patients. In this paper, we propose an incremental learning approach to apply a dev...
Machine Learning Electronic Health Records Humans Learning Female Risk Diabetes Mellitus, Type 2 Male Cluster Analysis
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Precision Cohort Finding with Outcome-Driven Similarity Analytics: A Case Study of Patients with Atrial Fibrillation.

Studies in health technology and informatics Jan 1, 2017
Dividing patients into similar groups plays a significant role in implementing personalized care. Clinicians and researchers have been applying patient grouping techniques in disease phenotyping, risk stratification, and personalized medicine. Howeve...
Atrial Fibrillation Cluster Analysis Humans Brain Ischemia Anticoagulants Risk Factors Stroke Cohort Studies
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A Novel Cluster Head Selection Algorithm Based on Fuzzy Clustering and Particle Swarm Optimization.

IEEE/ACM transactions on computational biology and bioinformatics Jan 1, 2017
An important objective of wireless sensor network is to prolong the network life cycle, and topology control is of great significance for extending the network life cycle. Based on previous work, for cluster head selection in hierarchical topology co...
Crowding Computer Simulation Fuzzy Logic Cluster Analysis Computer Communication Networks Wireless Technology Models, Statistical Biomimetics Algorithms
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Exploring Genome-Wide Expression Profiles Using Machine Learning Techniques.

Methods in molecular biology (Clifton, N.J.) Jan 1, 2017
Although contemporary high-throughput -omics methods produce high-dimensional data, the resulting wealth of information is difficult to assess using traditional statistical procedures. Machine learning methods facilitate the detection of additional p...
Humans Genomics Computational Biology Cluster Analysis Gene Expression Regulation Periodontitis Periodontal Diseases Machine Learning Phenotype Software Transcriptome Gene Expression Profiling Algorithms Genome-Wide Association Study
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DeepChrome: deep-learning for predicting gene expression from histone modifications.

Bioinformatics (Oxford, England) Sep 1, 2016
MOTIVATION: Histone modifications are among the most important factors that control gene regulation. Computational methods that predict gene expression from histone modification signals are highly desirable for understanding their combinatorial effec...
Support Vector Machine Cluster Analysis Gene Expression Regulation Histone Code Neural Networks, Computer Gene Regulatory Networks Humans Computational Biology Epigenesis, Genetic
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Ensemble statistical and subspace clustering model for analysis of autism spectrum disorder phenotypes.

Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference Aug 1, 2016
Heterogeneity in Autism Spectrum Disorder (ASD) is complex including variability in behavioral phenotype as well as clinical, physiologic, and pathologic parameters. The fifth edition of the Diagnostic and Statistical Manual of Mental Disorders (DSM-...
Cluster Analysis Diagnostic and Statistical Manual of Mental Disorders Humans Phenotype Machine Learning Autism Spectrum Disorder Databases, Factual Algorithms Prognosis Reproducibility of Results
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An unsupervised subject identification technique using EEG signals.

Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference Aug 1, 2016
In this work, EEG spectral features of different subjects are uniquely mapped into a 2D feature space. Such distinctive 2D features pave the way to identify subjects from their EEG spectral characteristics in an unsupervised manner without any prior ...
Unsupervised Machine Learning Electroencephalography Humans Cluster Analysis
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[The Identification of Lettuce Varieties by Using Unsupervised Possibilistic Fuzzy Learning Vector Quantization and Near Infrared Spectroscopy].

Guang pu xue yu guang pu fen xi = Guang pu Mar 1, 2016
To solve the noisy sensitivity problem of fuzzy learning vector quantization (FLVQ), unsupervised possibilistic fuzzy learning vector quantization (UPFLVQ) was proposed based on unsupervised possibilistic fuzzy clustering (UPFC). UPFLVQ aimed to use ...
Machine Learning Principal Component Analysis Spectroscopy, Near-Infrared Lactuca Algorithms Fuzzy Logic Cluster Analysis
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