Representation learning of drug and disease terms for drug repositioning

Drug repositioning (DR) refers to identification of novel indications for the approved drugs. The requirement of huge investment of time as well as money and risk of failure in clinical trials have led to surge in interest in drug repositioning. DR exploits two major aspects associated with drugs and diseases: existence of similarity among drugs and among diseases due to their shared involved genes or pathways or common biological effects. Existing methods of identifying drug-disease association majorly rely on the information available in the structured databases only. On the other hand, abundant information available in form of free texts in biomedical research articles are not being fully exploited. Word-embedding or obtaining vector representation of words from a large corpora of free texts using neural network methods have been shown to give significant performance for several natural language processing tasks. In this work we propose a novel way of representation learning to obtain features of drugs and diseases by combining complementary information available in unstructured texts and structured datasets. Next we use matrix completion approach on these feature vectors to learn projection matrix between drug and disease vector spaces. The proposed method has shown competitive performance with state-of-the-art methods. Further, the case studies on Alzheimer's and Hypertension diseases have shown that the predicted associations are matching with the existing knowledge.Comment: Accepted to appear in 3rd IEEE International Conference on Cybernetics (Spl Session: Deep Learning for Prediction and Estimation

Industrial Cyber-Physical Systems-based Cloud IoT Edge for Federated Heterogeneous Distillation.

Deep convoloutional networks have achieved remarkable performance in a wide range of vision-based tasks in modern internet of things (IoT). Due to privacy issue and transmission cost, mannually annotated data for training the deep learning models are usually stored in different sites with fog and edge devices of various computing capacity. It has been proved that knowledge distillation technique can effectively compress well trained neural networks into light-weight models suitable to particular devices. However, different fog and edge devices may perform different sub-tasks, and simplely performing model compression on powerful cloud servers failed to make use of the private data sotred at different sites. To overcome these obstacles, we propose an novel knowledge distillation method for object recognition in real-world IoT sencarios. Our method enables flexible bidirectional online training of heterogeneous models distributed datasets with a new ``brain storming'' mechanism and optimizable temperature parameters. In our comparison experiments, this heterogeneous brain storming method were compared to multiple state-of-the-art single-model compression methods, as well as the newest heterogeneous and homogeneous multi-teacher knowledge distillation methods. Our methods outperformed the state of the arts in both conventional and heterogeneous tasks. Further analysis of the ablation expxeriment results shows that introducing the trainable temperature parameters into the conventional knowledge distillation loss can effectively ease the learning process of student networks in different methods. To the best of our knowledge, this is the IoT-oriented method that allows asynchronous bidirectional heterogeneous knowledge distillation in deep networks