International journal of computer assisted radiology and surgery
May 15, 2018
PURPOSE: Most of the existing convolutional neural network (CNN)-based medical image segmentation methods are based on methods that have originally been developed for segmentation of natural images. Therefore, they largely ignore the differences betw...
Computational intelligence and neuroscience
May 15, 2018
Feature of event-related potential (ERP) has not been completely understood and illiteracy problem remains unsolved. To this end, P300 peak has been used as the feature of ERP in most brain-computer interface applications, but subjects who do not sho...
Malonylation is a recently discovered post-translational modification (PTM) in which a malonyl group attaches to a lysine (K) amino acid residue of a protein. In this work, a novel machine learning model, SPRINT-Mal, is developed to predict malonylat...
The aim of this study was to evaluate the performance of models predicting in-hospital mortality in critically ill children undergoing continuous electroencephalography (cEEG) in the intensive care unit (ICU). We evaluated the performance of machine ...
International journal of computer assisted radiology and surgery
May 12, 2018
PURPOSE: In cardiac interventions, such as cardiac resynchronization therapy (CRT), image guidance can be enhanced by involving preoperative models. Multimodality 3D/2D registration for image guidance, however, remains a significant research challeng...
BACKGROUND: Identifying new potential treatment options for medical conditions that cause human disease burden is a central task of biomedical research. Since all candidate drugs cannot be tested with animal and clinical trials, in vitro approaches a...
Accurate and immediate diagnosis of malaria is important for medication of the infectious disease. Conventional methods for diagnosing malaria are time consuming and rely on the skill of experts. Therefore, an automatic and simple diagnostic modality...
Plant-pathogenic fungi secrete effector proteins to facilitate infection. We describe extensive improvements to EffectorP, the first machine learning classifier for fungal effector prediction. EffectorP 2.0 is now trained on a larger set of effectors...
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