AI Medical Compendium Topic:
Models, Statistical

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In Silico Prediction of Gamma-Aminobutyric Acid Type-A Receptors Using Novel Machine-Learning-Based SVM and GBDT Approaches.

BioMed research international
Gamma-aminobutyric acid type-A receptors (GABAARs) belong to multisubunit membrane spanning ligand-gated ion channels (LGICs) which act as the principal mediators of rapid inhibitory synaptic transmission in the human brain. Therefore, the category p...

Statistical learning theory for high dimensional prediction: Application to criterion-keyed scale development.

Psychological methods
Statistical learning theory (SLT) is the statistical formulation of machine learning theory, a body of analytic methods common in "big data" problems. Regression-based SLT algorithms seek to maximize predictive accuracy for some outcome, given a larg...

Round Randomized Learning Vector Quantization for Brain Tumor Imaging.

Computational and mathematical methods in medicine
Brain magnetic resonance imaging (MRI) classification into normal and abnormal is a critical and challenging task. Owing to that, several medical imaging classification techniques have been devised in which Learning Vector Quantization (LVQ) is among...

Machine Learning Approach to Automated Quality Identification of Human Induced Pluripotent Stem Cell Colony Images.

Computational and mathematical methods in medicine
The focus of this research is on automated identification of the quality of human induced pluripotent stem cell (iPSC) colony images. iPS cell technology is a contemporary method by which the patient's cells are reprogrammed back to stem cells and ar...

Prediction of air pollutant concentration based on sparse response back-propagation training feedforward neural networks.

Environmental science and pollution research international
In this paper, we predict air pollutant concentration using a feedforward artificial neural network inspired by the mechanism of the human brain as a useful alternative to traditional statistical modeling techniques. The neural network is trained bas...

A Fuzzy Permutation Method for False Discovery Rate Control.

Scientific reports
Biomedical researchers often encounter the large-p-small-n situations-a great number of variables are measured/recorded for only a few subjects. The authors propose a fuzzy permutation method to address the multiple testing problem for small sample s...

Supporting Regularized Logistic Regression Privately and Efficiently.

PloS one
As one of the most popular statistical and machine learning models, logistic regression with regularization has found wide adoption in biomedicine, social sciences, information technology, and so on. These domains often involve data of human subjects...

Classification of Motor Imagery EEG Signals with Support Vector Machines and Particle Swarm Optimization.

Computational and mathematical methods in medicine
Support vector machines are powerful tools used to solve the small sample and nonlinear classification problems, but their ultimate classification performance depends heavily upon the selection of appropriate kernel and penalty parameters. In this st...

Adaptive contrast weighted learning for multi-stage multi-treatment decision-making.

Biometrics
Dynamic treatment regimes (DTRs) are sequential decision rules that focus simultaneously on treatment individualization and adaptation over time. To directly identify the optimal DTR in a multi-stage multi-treatment setting, we propose a dynamic stat...