AIMC Topic: Machine Learning

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GAERF: predicting lncRNA-disease associations by graph auto-encoder and random forest.

Briefings in bioinformatics
Predicting disease-related long non-coding RNAs (lncRNAs) is beneficial to finding of new biomarkers for prevention, diagnosis and treatment of complex human diseases. In this paper, we proposed a machine learning techniques-based classification appr...

Towards deep phenotyping pregnancy: a systematic review on artificial intelligence and machine learning methods to improve pregnancy outcomes.

Briefings in bioinformatics
OBJECTIVE: Development of novel informatics methods focused on improving pregnancy outcomes remains an active area of research. The purpose of this study is to systematically review the ways that artificial intelligence (AI) and machine learning (ML)...

Application of supervised machine learning as a method for identifying DEM contact law parameters.

Mathematical biosciences and engineering : MBE
Calibration of Discrete Element Method (DEM) models is an iterative process of adjusting input parameters such that the macroscopic results of simulations and experiments are similar. Therefore, selecting appropriate input parameters of a model effec...

Two-Stage Approaches to Accounting for Patient Heterogeneity in Machine Learning Risk Prediction Models in Oncology.

JCO clinical cancer informatics
PURPOSE: Machine learning models developed from electronic health records data have been increasingly used to predict risk of mortality for general oncology patients. But these models may have suboptimal performance because of patient heterogeneity. ...

Applying Machine Learning to Stem Cell Culture and Differentiation.

Current protocols
Machine learning techniques are increasingly becoming incorporated into biological research workflows in a variety of disciplines, most notably cancer research and drug discovery. Efforts in stem cell research comparatively lag behind. We detail key ...

Paediatric/young versus adult patients with long QT syndrome.

Open heart
INTRODUCTION: Long QT syndrome (LQTS) is a less prevalent cardiac ion channelopathy than Brugada syndrome in Asia. The present study compared the outcomes between paediatric/young and adult LQTS patients.

A modified binary particle swarm optimization with a machine learning algorithm and molecular docking for QSAR modelling of cholinesterase inhibitors.

SAR and QSAR in environmental research
The acetylcholinesterase (AChE) and butyrylcholinesterase (BuChE) inhibitors play a key role in treating Alzheimer's disease. This study proposes an approach that integrates a modified binary particle swarm optimization (PSO) with a machine learning ...