AIMC Topic: Machine Learning

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Probabilistic hazard assessment for skin sensitization potency by dose-response modeling using feature elimination instead of quantitative structure-activity relationships.

Journal of applied toxicology : JAT
Supervised learning methods promise to improve integrated testing strategies (ITS), but must be adjusted to handle high dimensionality and dose-response data. ITS approaches are currently fueled by the increasing mechanistic understanding of adverse ...

Compressed Sensing Reconstruction of 3D Ultrasound Data Using Dictionary Learning and Line-Wise Subsampling.

IEEE transactions on medical imaging
In this paper we present a compressed sensing (CS) method adapted to 3D ultrasound imaging (US). In contrast to previous work, we propose a new approach based on the use of learned overcomplete dictionaries that allow for much sparser representations...

Revisiting Warfarin Dosing Using Machine Learning Techniques.

Computational and mathematical methods in medicine
Determining the appropriate dosage of warfarin is an important yet challenging task. Several prediction models have been proposed to estimate a therapeutic dose for patients. The models are either clinical models which contain clinical and demographi...

A Markov random field approach to group-wise registration/mosaicing with application to ultrasound.

Medical image analysis
In this paper we present a group-wise non-rigid registration/mosaicing algorithm based on block-matching, which is developed within a probabilistic framework. The discrete form of its energy functional is linked to a Markov Random Field (MRF) contain...

Robust muscle activity onset detection using an unsupervised electromyogram learning framework.

PloS one
Accurate muscle activity onset detection is an essential prerequisite for many applications of surface electromyogram (EMG). This study presents an unsupervised EMG learning framework based on a sequential Gaussian mixture model (GMM) to detect muscl...

Control of a Wheelchair in an Indoor Environment Based on a Brain-Computer Interface and Automated Navigation.

IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society
The concept of controlling a wheelchair using brain signals is promising. However, the continuous control of a wheelchair based on unstable and noisy electroencephalogram signals is unreliable and generates a significant mental burden for the user. A...

Image Quality Assessment Using Human Visual DOG Model Fused With Random Forest.

IEEE transactions on image processing : a publication of the IEEE Signal Processing Society
Objective image quality assessment (IQA) plays an important role in the development of multimedia applications. Prediction of IQA metric should be consistent with human perception. The release of the newest IQA database (TID2013) challenges most of t...

Cortical feature analysis and machine learning improves detection of "MRI-negative" focal cortical dysplasia.

Epilepsy & behavior : E&B
Focal cortical dysplasia (FCD) is the most common cause of pediatric epilepsy and the third most common lesion in adults with treatment-resistant epilepsy. Advances in MRI have revolutionized the diagnosis of FCD, resulting in higher success rates fo...

Development and validation of a brain maturation index using longitudinal neuroanatomical scans.

NeuroImage
BACKGROUND: Major psychiatric disorders are increasingly being conceptualized as 'neurodevelopmental', because they are associated with aberrant brain maturation. Several studies have hypothesized that a brain maturation index integrating patterns of...

Recent progresses in the exploration of machine learning methods as in-silico ADME prediction tools.

Advanced drug delivery reviews
In-silico methods have been explored as potential tools for assessing ADME and ADME regulatory properties particularly in early drug discovery stages. Machine learning methods, with their ability in classifying diverse structures and complex mechanis...