AI Medical Compendium Topic:
Supervised Machine Learning

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NCBoost classifies pathogenic non-coding variants in Mendelian diseases through supervised learning on purifying selection signals in humans.

Genome biology
State-of-the-art methods assessing pathogenic non-coding variants have mostly been characterized on common disease-associated polymorphisms, yet with modest accuracy and strong positional biases. In this study, we curated 737 high-confidence pathogen...

Semi-supervised learning to improve generalizability of risk prediction models.

Journal of biomedical informatics
The utility of a prediction model depends on its generalizability to patients drawn from different but related populations. We explored whether a semi-supervised learning model could improve the generalizability of colorectal cancer (CRC) risk predic...

The roles of supervised machine learning in systems neuroscience.

Progress in neurobiology
Over the last several years, the use of machine learning (ML) in neuroscience has been rapidly increasing. Here, we review ML's contributions, both realized and potential, across several areas of systems neuroscience. We describe four primary roles o...

Attribute selection and model evaluation for the maternal and paternal imprinted genes in bovine (Bos Taurus) using supervised machine learning algorithms.

Journal of animal breeding and genetics = Zeitschrift fur Tierzuchtung und Zuchtungsbiologie
Imprinted genes display biased expression of paternal and maternal alleles in mammals. They are marked through epigenetic process during gametogenesis. Characterization of imprinted genes has expanded our understanding of the regulation and function ...

A Semi-Automatic Annotation Approach for Human Activity Recognition.

Sensors (Basel, Switzerland)
Modern smartphones and wearables often contain multiple embedded sensors which generate significant amounts of data. This information can be used for body monitoring-based areas such as healthcare, indoor location, user-adaptive recommendations and t...

A Multi-Label Supervised Topic Model Conditioned on Arbitrary Features for Gene Function Prediction.

Genes
With the continuous accumulation of biological data, more and more machine learning algorithms have been introduced into the field of gene function prediction, which has great significance in decoding the secret of life. Recently, a multi-label super...

Rapid detection of internalizing diagnosis in young children enabled by wearable sensors and machine learning.

PloS one
There is a critical need for fast, inexpensive, objective, and accurate screening tools for childhood psychopathology. Perhaps most compelling is in the case of internalizing disorders, like anxiety and depression, where unobservable symptoms cause c...

Blood Biochemistry Analysis to Detect Smoking Status and Quantify Accelerated Aging in Smokers.

Scientific reports
There is an association between smoking and cancer, cardiovascular disease and all-cause mortality. However, currently, there are no affordable and informative tests for assessing the effects of smoking on the rate of biological aging. In this study ...

3D Human Pose Machines with Self-Supervised Learning.

IEEE transactions on pattern analysis and machine intelligence
Driven by recent computer vision and robotic applications, recovering 3D human poses has become increasingly important and attracted growing interests. In fact, completing this task is quite challenging due to the diverse appearances, viewpoints, occ...

Predicting patient-reported outcomes following hip and knee replacement surgery using supervised machine learning.

BMC medical informatics and decision making
BACKGROUND: Machine-learning classifiers mostly offer good predictive performance and are increasingly used to support shared decision-making in clinical practice. Focusing on performance and practicability, this study evaluates prediction of patient...