Measuring drug-drug similarity is important but challenging. Significant progresses have been made in drugs whose labeled training data is sufficient and available. However, handling data skewness and incompleteness with domain-specific knowledge gra...
OBJECTIVES: To assess the performance of feed-forward back-propagation artificial neural networks (ANNs) in detecting field defects caused by pituitary disease from among a glaucomatous population.
The histological analysis of tissue samples, widely used for disease diagnosis, involves lengthy and laborious tissue preparation. Here, we show that a convolutional neural network trained using a generative adversarial-network model can transform wi...
OBJECTIVE: Direct synthesis of speech from neural signals could provide a fast and natural way of communication to people with neurological diseases. Invasively-measured brain activity (electrocorticography; ECoG) supplies the necessary temporal and ...
Australasian physical & engineering sciences in medicine
Mar 4, 2019
White blood cells play a vital role in monitoring health condition of a person. Change in count and/or appearance of these cells indicate hematological disorders. Manual microscopic evaluation of white blood cells is the gold standard method, but the...
Electroencephalography (EEG) is a powerful, noninvasive tool that provides a high temporal resolution to directly reflect brain activities. Conventional electrodes require skin preparation and the use of conductive gels, while subjects must wear unco...
Classification of histologic patterns in lung adenocarcinoma is critical for determining tumor grade and treatment for patients. However, this task is often challenging due to the heterogeneous nature of lung adenocarcinoma and the subjective criteri...
Accurate detection of somatic mutations is still a challenge in cancer analysis. Here we present NeuSomatic, the first convolutional neural network approach for somatic mutation detection, which significantly outperforms previous methods on different...
BACKGROUND: Deep learning has made tremendous successes in numerous artificial intelligence applications and is unsurprisingly penetrating into various biomedical domains. High-throughput omics data in the form of molecular profile matrices, such as ...
Determining the signal quality of surface electromyography (sEMG) recordings is time consuming and requires the judgement of trained observers. An automated procedure to evaluate sEMG quality would streamline data processing and reduce time demands. ...
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