BACKGROUND: Alzheimer's disease (AD) is a complex progressive neurodegenerative disorder commonly characterized by short term memory loss. Presently no effective therapeutic treatments exist that can completely cure this disease. The cause of Alzheim...
Transmembrane proteins (TMPs) are important drug targets because they are essential for signaling, regulation, and transport. Despite important breakthroughs, experimental structure determination remains challenging for TMPs. Various methods have bri...
Journal of computational biology : a journal of computational molecular cell biology
Aug 16, 2016
BACKGROUND: There are many computational approaches to predict the protein-protein interactions using support vector machines (SVMs) with high performance. In fact, performance of currently reported methods are significantly over-estimated and affect...
The determination of the molecular formula is one of the earliest and most important steps when investigating the chemical nature of an unknown compound. Common approaches use the isotopic pattern of a compound measured using mass spectrometry. Compu...
Alzheimer's disease (AD) is an irreversible neurodegenerative disease and affects a large population in the world. Cognitive scores at multiple time points can be reliably used to evaluate the progression of the disease clinically. In recent studies,...
Psychiatric research has entered the age of 'Big Data'. Datasets now routinely involve thousands of heterogeneous variables, including clinical, neuroimaging, genomic, proteomic, transcriptomic and other 'omic' measures. The analysis of these dataset...
International journal of neural systems
Jun 26, 2016
Cognitive fault detection and diagnosis systems are systems able to provide timely information about possibly occurring faults without requiring any a priori knowledge about the process generating the data or the possible faults. This ability is cruc...
This article discusses the application of machine learning for the analysis of medical images. Specifically: (i) We show how a special type of learning models can be thought of as automatically optimized, hierarchically-structured, rule-based algorit...
Computational intelligence and neuroscience
Jun 1, 2016
Heavily occluded objects are more difficult for classification algorithms to identify correctly than unoccluded objects. This effect is rare and thus hard to measure with datasets like ImageNet and PASCAL VOC, however, owing to biases in human-genera...
Computational intelligence and neuroscience
May 9, 2016
Traditional multiple kernel dimensionality reduction models are generally based on graph embedding and manifold assumption. But such assumption might be invalid for some high-dimensional or sparse data due to the curse of dimensionality, which has a ...
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