AIMC Topic: Biomarkers

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Variable Selection for Time-to-Event Data.

Methods in molecular biology (Clifton, N.J.)
With the increasing availability of large scale biomedical and -omics data, researchers are offered with unprecedented opportunities to discover novel biomarkers for clinical outcomes. At the same time, they are also faced with great challenges to ac...

Brief Report: Can a Composite Heart Rate Variability Biomarker Shed New Insights About Autism Spectrum Disorder in School-Aged Children?

Journal of autism and developmental disorders
Several studies show altered heart rate variability (HRV) in autism spectrum disorder (ASD), but findings are neither universal nor specific to ASD. We apply a set of linear and nonlinear HRV measures-including phase rectified signal averaging-to seg...

Predicting Secukinumab Fast-Responder Profile in Psoriatic Patients: Advanced Application of Artificial-Neural-Networks (ANNs).

Journal of drugs in dermatology : JDD
BACKGROUND: Drug resistance to biologics in psoriasis therapy can occur – it may be acquired during a treatment or else present itself from the beginning. To date, no biomarkers are known that may reliably guide clinicians in predicting respons...

Machine Learning Analysis of Blood microRNA Data in Major Depression: A Case-Control Study for Biomarker Discovery.

The international journal of neuropsychopharmacology
BACKGROUND: There is a lack of reliable biomarkers for major depressive disorder (MDD) in clinical practice. However, several studies have shown an association between alterations in microRNA levels and MDD, albeit none of them has taken advantage of...

Deep learning-level melanoma detection by interpretable machine learning and imaging biomarker cues.

Journal of biomedical optics
SIGNIFICANCE: Melanoma is a deadly cancer that physicians struggle to diagnose early because they lack the knowledge to differentiate benign from malignant lesions. Deep machine learning approaches to image analysis offer promise but lack the transpa...

Prediction of systemic biomarkers from retinal photographs: development and validation of deep-learning algorithms.

The Lancet. Digital health
BACKGROUND: The application of deep learning to retinal photographs has yielded promising results in predicting age, sex, blood pressure, and haematological parameters. However, the broader applicability of retinal photograph-based deep learning for ...

Wavelet Spectral Time-Frequency Training of Deep Convolutional Neural Networks for Accurate Identification of Micro-Scale Sharp Wave Biomarkers in the Post-Hypoxic-Ischemic EEG of Preterm Sheep.

Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference
Neonatal hypoxic-ischemic encephalopathy (HIE) evolves over different phases of time during recovery. Some neuroprotection treatments are only effective for specific, short windows of time during this evolution of injury. Clinically, we often do not ...

Integration of ultra-high-pressure liquid chromatography-tandem mass spectrometry with machine learning for identifying fatty acid metabolite biomarkers of ischemic stroke.

Chemical communications (Cambridge, England)
We report for the first time the integration of ultra-high-pressure liquid chromatography-tandem mass spectrometry with machine learning for identifying fatty acid metabolite biomarkers of ischemic stroke. In particular, we develop an optimal model t...