AIMC Topic: Machine Learning

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Molecular Insights from Conformational Ensembles via Machine Learning.

Biophysical journal
Biomolecular simulations are intrinsically high dimensional and generate noisy data sets of ever-increasing size. Extracting important features from the data is crucial for understanding the biophysical properties of molecular processes, but remains ...

Potential Confounders in the Analysis of Brazilian Adolescent's Health: A Combination of Machine Learning and Graph Theory.

International journal of environmental research and public health
The prevalence of health problems during childhood and adolescence is high in developing countries such as Brazil. Social inequality, violence, and malnutrition have strong impact on youth health. To better understand these issues we propose to combi...

Gender and active travel: a qualitative data synthesis informed by machine learning.

The international journal of behavioral nutrition and physical activity
BACKGROUND: Innovative approaches are required to move beyond individual approaches to behaviour change and develop more appropriate insights for the complex challenge of increasing population levels of activity. Recent research has drawn on social p...

Ultrasonic liver steatosis quantification by a learning-based acoustic model from a novel shear wave sequence.

Biomedical engineering online
BACKGROUND: An efficient and accurate approach to quantify the steatosis extent of liver is important for clinical practice. For the purpose, we propose a specific designed ultrasound shear wave sequence to estimate ultrasonic and shear wave physical...

Comparing different supervised machine learning algorithms for disease prediction.

BMC medical informatics and decision making
BACKGROUND: Supervised machine learning algorithms have been a dominant method in the data mining field. Disease prediction using health data has recently shown a potential application area for these methods. This study ai7ms to identify the key tren...

Ensemble Learning for Early-Response Prediction of Antidepressant Treatment in Major Depressive Disorder.

Journal of magnetic resonance imaging : JMRI
BACKGROUND: In order to reduce unsuccessful treatment trials for depression, neuroimaging and genetic information can be considered as biomarkers. Together with machine-learning methods, prediction models have proved to be valuable for baseline predi...

ELM embedded discriminative dictionary learning for image classification.

Neural networks : the official journal of the International Neural Network Society
Dictionary learning is a widely adopted approach for image classification. Existing methods focus either on finding a dictionary that produces discriminative sparse representation, or on enforcing priors that best describe the dataset distribution. I...

Toward safer highways, application of XGBoost and SHAP for real-time accident detection and feature analysis.

Accident; analysis and prevention
Detecting traffic accidents as rapidly as possible is essential for traffic safety. In this study, we use eXtreme Gradient Boosting (XGBoost)-a Machine Learning (ML) technique-to detect the occurrence of accidents using a set of real time data compri...

Computational prediction of cytochrome P450 inhibition and induction.

Drug metabolism and pharmacokinetics
Cytochrome P450 (CYP) enzymes play an important role in the phase I metabolism of many xenobiotics. Most drug-drug interactions (DDIs) associated with CYP are caused by either CYP inhibition or induction. The early detection of potential DDIs is high...

Data-mining Techniques for Image-based Plant Phenotypic Traits Identification and Classification.

Scientific reports
Statistical data-mining (DM) and machine learning (ML) are promising tools to assist in the analysis of complex dataset. In recent decades, in the precision of agricultural development, plant phenomics study is crucial for high-throughput phenotyping...