AIMC Topic: Machine Learning

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Automatic recognition of gait patterns in human motor disorders using machine learning: A review.

Medical engineering & physics
BACKGROUND: automatic recognition of human movement is an effective strategy to assess abnormal gait patterns. Machine learning approaches are mainly applied due to their ability to work with multidimensional nonlinear features.

Application of a Deep Neural Network to Metabolomics Studies and Its Performance in Determining Important Variables.

Analytical chemistry
Deep neural networks (DNNs), which are kinds of the machine learning approaches, are powerful tools for analyzing big sets of data derived from biological and environmental systems. However, DNNs are not applicable to metabolomics studies because the...

Artificial Intelligence Estimation of Carotid-Femoral Pulse Wave Velocity using Carotid Waveform.

Scientific reports
In this article, we offer an artificial intelligence method to estimate the carotid-femoral Pulse Wave Velocity (PWV) non-invasively from one uncalibrated carotid waveform measured by tonometry and few routine clinical variables. Since the signal pro...

A method of gene expression data transfer from cell lines to cancer patients for machine-learning prediction of drug efficiency.

Cell cycle (Georgetown, Tex.)
Personalized medicine implies that distinct treatment methods are prescribed to individual patients according several features that may be obtained from, e.g., gene expression profile. The majority of machine learning methods suffer from the deficien...

Developing Deep Learning Applications for Life Science and Pharma Industry.

Drug research
Deep Learning has boosted artificial intelligence over the past 5 years and is seen now as one of the major technological innovation areas, predicted to replace lots of repetitive, but complex tasks of human labor within the next decade. It is also e...

Machine-learned analysis of quantitative sensory testing responses to noxious cold stimulation in healthy subjects.

European journal of pain (London, England)
BACKGROUND: Pain in response to noxious cold has a complex molecular background probably involving several types of sensors. A recent observation has been the multimodal distribution of human cold pain thresholds. This study aimed at analysing reprod...

Prediction of activation patterns preceding hallucinations in patients with schizophrenia using machine learning with structured sparsity.

Human brain mapping
Despite significant progress in the field, the detection of fMRI signal changes during hallucinatory events remains difficult and time-consuming. This article first proposes a machine-learning algorithm to automatically identify resting-state fMRI pe...

Artificial intelligence exploration of unstable protocells leads to predictable properties and discovery of collective behavior.

Proceedings of the National Academy of Sciences of the United States of America
Protocell models are used to investigate how cells might have first assembled on Earth. Some, like oil-in-water droplets, can be seemingly simple models, while able to exhibit complex and unpredictable behaviors. How such simple oil-in-water systems ...

WorMachine: machine learning-based phenotypic analysis tool for worms.

BMC biology
BACKGROUND: Caenorhabditis elegans nematodes are powerful model organisms, yet quantification of visible phenotypes is still often labor-intensive, biased, and error-prone. We developed WorMachine, a three-step MATLAB-based image analysis software th...

A Risk Stratification Model for Lung Cancer Based on Gene Coexpression Network and Deep Learning.

BioMed research international
Risk stratification model for lung cancer with gene expression profile is of great interest. Instead of previous models based on individual prognostic genes, we aimed to develop a novel system-level risk stratification model for lung adenocarcinoma b...