AIMC Topic: Biomarkers

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Identifying incipient dementia individuals using machine learning and amyloid imaging.

Neurobiology of aging
Identifying individuals destined to develop Alzheimer's dementia within time frames acceptable for clinical trials constitutes an important challenge to design studies to test emerging disease-modifying therapies. Although amyloid-β protein is the co...

Comparative Simulation Study of Glucose Control Methods Designed for Use in the Intensive Care Unit Setting via a Novel Controller Scoring Metric.

Journal of diabetes science and technology
BACKGROUND: Effective glucose control in the intensive care unit (ICU) setting has the potential to decrease morbidity and mortality rates and thereby decrease health care expenditures. To evaluate what constitutes effective glucose control, typicall...

Towards affordable biomarkers of frontotemporal dementia: A classification study via network's information sharing.

Scientific reports
Developing effective and affordable biomarkers for dementias is critical given the difficulty to achieve early diagnosis. In this sense, electroencephalographic (EEG) methods offer promising alternatives due to their low cost, portability, and growin...

Machine-Learned Data Structures of Lipid Marker Serum Concentrations in Multiple Sclerosis Patients Differ from Those in Healthy Subjects.

International journal of molecular sciences
Lipid metabolism has been suggested to be a major pathophysiological mechanism of multiple sclerosis (MS). With the increasing knowledge about lipid signaling, acquired data become increasingly complex making bioinformatics necessary in lipid researc...

Auditory prediction errors as individual biomarkers of schizophrenia.

NeuroImage. Clinical
Schizophrenia is a complex psychiatric disorder, typically diagnosed through symptomatic evidence collected through patient interview. We aim to develop an objective biologically-based computational tool which aids diagnosis and relies on accessible ...

A multilevel-ROI-features-based machine learning method for detection of morphometric biomarkers in Parkinson's disease.

Neuroscience letters
Machine learning methods have been widely used in recent years for detection of neuroimaging biomarkers in regions of interest (ROIs) and assisting diagnosis of neurodegenerative diseases. The innovation of this study is to use multilevel-ROI-feature...

Anti-inflammatory effect of cinnamaldehyde and linalool from the leaf essential oil of Cinnamomum osmophloeum Kanehira in endotoxin-induced mice.

Journal of food and drug analysis
Cinnamomum osmophloeum Kanehira is a Taiwan native plant that belongs to genus Cinnamomum and is also known as pseudocinnamomum or indigenous cinnamon. Its leaf is traditionally used by local people in cooking and as folk therapy. We previously demon...

Comparison of Machine Learning Approaches for Prediction of Advanced Liver Fibrosis in Chronic Hepatitis C Patients.

IEEE/ACM transactions on computational biology and bioinformatics
BACKGROUND/AIM: Using machine learning approaches as non-invasive methods have been used recently as an alternative method in staging chronic liver diseases for avoiding the drawbacks of biopsy. This study aims to evaluate different machine learning ...

A reductionist approach to extract robust molecular markers from microarray data series - Isolating markers to track osseointegration.

Journal of biomedical informatics
Complexities in the full genome expression studies hinder the extraction of tracker genes to analyze the course of biological events. In this study, we demonstrate the applications of supervised machine learning methods to reduce the irrelevance in m...