IEEE BIBM Workshop 2020 - 1st International Workshop on Deep Learning Techniques for Bioinformatics and Biomedicine

Scope
The deep learning techniques (e.g. neural network (NN), convolutional neural network (CNN), recurrent neural network (RNN), long short-term memory (LSTM) networks) have been the powerful tools for various bioinformatics and biomedicine applications. These techniques can be used to support the disease detection and prevention, cancer detection and prevention, disease prediction, experimental medicine, emergency, medication management, healthcare management, etc. For instance, the CNN has been used to recognize the images of x-ray photographs for fibrosis detection and cancer classification. The image features of fibrosis can be filtered by convolutional layers in the CNN. Furthermore, the unsupervised learning techniques (e.g. auto-encoder (AE), de-noise auto-encoder (DAE), restricted Boltzmann machine (RBM), deep belief network (DBN), and deep Boltzmann machine (RBM)) and deep learning frameworks (e.g. reinforcement learning and federated learning) have been proposed to represent data and extract data features for overfitting prevention and secured data service production. Therefore, the deep learning techniques and frameworks can provide the precise detection and prediction for healthcare applications.
While the area of deep learning methods for bioinformatics and biomedicine is a rapidly expanding field of scientific research, several open research questions are still needed to be discussed and studied. For instance, using and improving deep learning methods for disease detection and prevention, cancer detection and prevention, disease prediction, experimental medicine, emergency, medication management, and healthcare management. This workshop named "Deep Learning Techniques for Bioinformatics and Biomedicine" in conjunction with the 2020 IEEE International Conference on Bioinformatics and Biomedicine (BIBM 2020) will solicit papers on various disciplines of bioinformatics and biomedicine.
Topics
Ø Deep learning for disease detection and prevention
Ø Deep learning for cancer detection and prevention
Ø Deep learning for disease prediction
Ø Deep learning for experimental medicine
Ø Deep learning for emergency
Ø Deep learning for medication management
Ø Deep learning for healthcare management
Ø Bioinformatics and biomedicine applications based on deep learning
Ø Bioinformatics and biomedicine applications based on reinforcement learning
Ø Bioinformatics and biomedicine applications based on federated learning
Ø Bioinformatics and biomedicine applications based on machine learning