Chromosomal inversions are important structural changes that may facilitate divergent selection when they capture co-adaptive loci in the face of gene flow. However, identifying selection targets within inversions can be challenging. The high degrees...
Journal of evaluation in clinical practice
Apr 19, 2016
RATIONALE, AIMS AND OBJECTIVES: Single-group interrupted time series analysis (ITSA) is a popular evaluation methodology in which a single unit of observation is being studied, the outcome variable is serially ordered as a time series and the interve...
BACKGROUND: Brain MRI holds promise to gauge different aspects of Parkinson's disease (PD)-related pathological changes. Its analysis, however, is hindered by the high-dimensional nature of the data.
Computational intelligence and neuroscience
Apr 19, 2016
Lifelong machine learning (LML) models learn with experience maintaining a knowledge-base, without user intervention. Unlike traditional single-domain models they can easily scale up to explore big data. The existing LML models have high data depende...
Cascading overload failures are widely found in large-scale parallel systems and remain a major threat to system reliability; therefore, they are of great concern to maintainers and managers of different systems. Accurate cascading failure prediction...
Two new studies highlight the potential of neuroimaging to aid the differential diagnosis of neurodegenerative disease, for both clinical practice and emerging trials. Although this approach holds great promise, meaningful implementation of neuroimag...
This paper performs a comprehensive study on the deep-learning-based computer-aided diagnosis (CADx) for the differential diagnosis of benign and malignant nodules/lesions by avoiding the potential errors caused by inaccurate image processing results...
IEEE transactions on neural networks and learning systems
Apr 14, 2016
Jensen-type [Jensen-Shannon (JS) and Jensen-Tsallis] kernels were first proposed by Martins et al. (2009). These kernels are based on JS divergences that originated in the information theory. In this paper, we extend the Jensen-type kernels on probab...
Ensembles are a well established machine learning paradigm, leading to accurate and robust models, predominantly applied to predictive modeling tasks. Ensemble models comprise a finite set of diverse predictive models whose combined output is expecte...
Join thousands of healthcare professionals staying informed about the latest AI breakthroughs in medicine. Get curated insights delivered to your inbox.