Minghui Wang

TL;DR: This study proposes a Multimodal Deep Neural Network by integrating Multi-dimensional Data (MDNNMD) for the prognosis prediction of breast cancer and shows that the proposed method achieves a better performance than the prediction methods with single-dimensional data and other existing approaches.

TL;DR: An online web server based on this method has been developed and is freely available to both academic and commercial users, which can be accessed by at and results indicate that LOCSVMPSI is a powerful tool for the prediction of eukaryotic protein subcellular localization.

TL;DR: Quantitative and visual evaluation of the method has revealed the effectiveness of the proposed improvements and indicated that the end-to-end segmentation method can achieve a performance that can compete with state-of-the-art brain tumor segmentation approaches.

TL;DR: DeepPhos, a novel deep learning architecture for prediction of protein phosphorylation, consists of densely connected convolutional neuron network blocks which can capture multiple representations of sequences to make final phosphorylated prediction by intra block concatenation layers and inter block concaternation layers.

TL;DR: Performance analysis of the GPMKL model indicates that the pathological image information plays a critical part in accurately predicting the survival time of breast cancer patients, and suggest that the usefulness and superiority of G PMKL in predicting human breast cancer survival are suggested.