AI Medical Compendium Topic:
Supervised Machine Learning

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Using machine learning to model problematic smartphone use severity: The significant role of fear of missing out.

Addictive behaviors
We examined a model of psychopathology variables, age and sex as correlates of problematic smartphone use (PSU) severity using supervised machine learning in a sample of Chinese undergraduate students. A sample of 1097 participants completed measures...

IILLS: predicting virus-receptor interactions based on similarity and semi-supervised learning.

BMC bioinformatics
BACKGROUND: Viral infectious diseases are the serious threat for human health. The receptor-binding is the first step for the viral infection of hosts. To more effectively treat human viral infectious diseases, the hidden virus-receptor interactions ...

Automatic Identification of Breast Ultrasound Image Based on Supervised Block-Based Region Segmentation Algorithm and Features Combination Migration Deep Learning Model.

IEEE journal of biomedical and health informatics
Breast cancer is a high-incidence type of cancer for women. Early diagnosis plays a crucial role in the successful treatment of the disease and the effective reduction of deaths. In this paper, deep learning technology combined with ultrasound imagin...

Supervised and unsupervised algorithms for bioinformatics and data science.

Progress in biophysics and molecular biology
Bioinformatics refers to an ever evolving huge field of research based on millions of algorithms, designated to several data banks. Such algorithms are either supervised or unsupervised. In this article, a detailed overview of the supervised and unsu...

Cell Line Classification Using Electric Cell-Substrate Impedance Sensing (ECIS).

The international journal of biostatistics
We present new methods for cell line classification using multivariate time series bioimpedance data obtained from electric cell-substrate impedance sensing (ECIS) technology. The ECIS technology, which monitors the attachment and spreading of mammal...

Incremental Learning to Personalize Human Activity Recognition Models: The Importance of Human AI Collaboration.

Sensors (Basel, Switzerland)
This study presents incremental learning based methods to personalize human activity recognition models. Initially, a user-independent model is used in the recognition process. When a new user starts to use the human activity recognition application,...

Ranking of non-coding pathogenic variants and putative essential regions of the human genome.

Nature communications
A gene is considered essential if loss of function results in loss of viability, fitness or in disease. This concept is well established for coding genes; however, non-coding regions are thought less likely to be determinants of critical functions. H...

Electrophysiological assessment of plant status outside a Faraday cage using supervised machine learning.

Scientific reports
Living organisms have evolved complex signaling networks to drive appropriate physiological processes in response to changing environmental conditions. Amongst them, electric signals are a universal method to rapidly transmit information. In animals,...

Deep supervised learning with mixture of neural networks.

Artificial intelligence in medicine
Deep Neural Network (DNN), as a deep architectures, has shown excellent performance in classification tasks. However, when the data has different distributions or contains some latent non-observed factors, it is difficult for DNN to train a single mo...

Muscle endurance time estimation during isometric training using electromyogram and supervised learning.

Journal of electromyography and kinesiology : official journal of the International Society of Electrophysiological Kinesiology
UNLABELLED: Constant-force isometric muscle training is useful for increasing the maximal strength , rehabilitation and work-fatigue assessment. Earlier studies have shown that muscle fatigue characteristics can be used for evaluating muscle enduranc...