AIMC Topic: Machine Learning

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A framework for combining a motion atlas with non-motion information to learn clinically useful biomarkers: Application to cardiac resynchronisation therapy response prediction.

Medical image analysis
We present a framework for combining a cardiac motion atlas with non-motion data. The atlas represents cardiac cycle motion across a number of subjects in a common space based on rich motion descriptors capturing 3D displacement, velocity, strain and...

Prediction of Incident Diabetes in the Jackson Heart Study Using High-Dimensional Machine Learning.

PloS one
Statistical models to predict incident diabetes are often based on limited variables. Here we pursued two main goals: 1) investigate the relative performance of a machine learning method such as Random Forests (RF) for detecting incident diabetes in ...

Activity Recognition for Diabetic Patients Using a Smartphone.

Journal of medical systems
Diabetes is a disease that has to be managed through appropriate lifestyle. Technology can help with this, particularly when it is designed so that it does not impose an additional burden on the patient. This paper presents an approach that combines ...

Inferring Unknown Biological Function by Integration of GO Annotations and Gene Expression Data.

IEEE/ACM transactions on computational biology and bioinformatics
Characterizing genes with semantic information is an important process regarding the description of gene products. In spite that complete genomes of many organisms have been already sequenced, the biological functions of all of their genes are still ...

Machine learning-based 3-D geometry reconstruction and modeling of aortic valve deformation using 3-D computed tomography images.

International journal for numerical methods in biomedical engineering
To conduct a patient-specific computational modeling of the aortic valve, 3-D aortic valve anatomic geometries of an individual patient need to be reconstructed from clinical 3-D cardiac images. Currently, most of computational studies involve manual...

From "ear" to there: a review of biorobotic models of auditory processing in lizards.

Biological cybernetics
The peripheral auditory system of lizards has been extensively studied, because of its remarkable directionality. In this paper, we review the research that has been performed on this system using a biorobotic approach. The various robotic implementa...

Multi-parameter machine learning approach to the neuroanatomical basis of developmental dyslexia.

Human brain mapping
Despite decades of research, the anatomical abnormalities associated with developmental dyslexia are still not fully described. Studies have focused on between-group comparisons in which different neuroanatomical measures were generally explored in i...

Beatquency domain and machine learning improve prediction of cardiovascular death after acute coronary syndrome.

Scientific reports
Frequency domain measures of heart rate variability (HRV) are associated with adverse events after a myocardial infarction. However, patterns in the traditional frequency domain (measured in Hz, or cycles per second) may capture different cardiac phe...

HEMEsPred: Structure-Based Ligand-Specific Heme Binding Residues Prediction by Using Fast-Adaptive Ensemble Learning Scheme.

IEEE/ACM transactions on computational biology and bioinformatics
Heme is an essential biomolecule that widely exists in numerous extant organisms. Accurately identifying heme binding residues (HEMEs) is of great importance in disease progression and drug development. In this study, a novel predictor named HEMEsPre...

Individual prediction of long-term outcome in adolescents at ultra-high risk for psychosis: Applying machine learning techniques to brain imaging data.

Human brain mapping
An important focus of studies of individuals at ultra-high risk (UHR) for psychosis has been to identify biomarkers to predict which individuals will transition to psychosis. However, the majority of individuals will prove to be resilient and go on t...