The fields of medicine science and health informatics have made great progress recently and have led to in-depth analytics that is demanded by generation, collection and accumulation of massive data. Meanwhile, we are entering a new period where novel technologies are starting to analyze and explore knowledge from tremendous amount of data, bringing limitless potential for information growth. One fact that cannot be ignored is that the techniques of machine learning and deep learning applications play a more significant role in the success of bioinformatics exploration from biological data point of view, and a linkage is emphasized and established to bridge these two data analytics techniques and bioinformatics in both industry and academia. This survey concentrates on the review of recent researches using data mining and deep learning approaches for analyzing the specific domain knowledge of bioinformatics. The authors give a brief but pithy summarization of numerous data mining algorithms used for preprocessing, classification and clustering as well as various optimized neural network architectures in deep learning methods, and their advantages and disadvantages in the practical applications are also discussed and compared in terms of their industrial usage. It is believed that in this review paper, valuable insights are provided for those who are dedicated to start using data analytics methods in bioinformatics.

Many studies utilize the signal strength of short-range radio systems (such as WiFi, ultrasound and infrared) to build a radio map for indoor localization, by deploying a large number of beacon nodes within a building. The drawback of such an infrastructure-based approach is that the deployment and calibration of the system are costly and labor-intensive. Some prior studies proposed the use of Pedestrian Dead Reckoning (PDR) for indoor localization, which does not require the deployment of beacon nodes. In a PDR system, a small number of sensors are put on the pedestrian. These sensors (such as a G-sensor and gyroscope) are used to estimate the distance and direction that a user travels. The effectiveness of a PDR system lies in its success in accurately estimating the user's moving distance and direction. In this work, we propose a novel waist-mounted based PDR that can measure the user's step lengths with a high accuracy. We utilize vertical acceleration of the body to calculate the user's change in height during walking. Based on the Pythagorean Theorem, we can then estimate each step length using this data. Furthermore, we design a map matching algorithm to calibrate the direction errors from the gyro using building floor plans. The results of our experiment show that we can achieve about 98.26% accuracy in estimating the user's walking distance, with an overall location error of about 0.48 m.

Background: Diagnosing brain disorders, such as Parkinson's disease (PD) or Alzheimer's disease, is often difficult, especially in the early stages. Moreover, it has been estimated that nearly 40% of people with PD may not be diagnosed. Traditionally, the diagnosis of neurological disorders, such as PD, often required a doctor to observe the patient over time to recognize signs of rigidity in movement. Materials and Methods: The pedestrian dead reckoning (PDR) system is a self-contained technique that has been widely used for indoor localization. In this work we propose a PDR-based method to continuously monitor and record the patient's gait characteristics using a smartphone. Seventeen patients were studied over a period of 1 year. During the year it became apparent that 1 of the patients was actually developing PD. To the best of our knowledge, our work is the first attempt to use sensors in a smartphone to help identify patients in their early stages of neurological disease. Results: On average, the accuracy of our step length estimation was about 98%. Using a binary classification method-namely, support vector machine-we carried out a case study and showed that it was feasible to identify changes in the walking patterns of a PD patient with an accuracy of 94%. Conclusions: Using 1 year of gait trace data obtained from the users' phones, our work provides a first step to experimentally show the possibility of applying smartphone sensor data to provide early warnings to potential PD patients to encourage them to seek medical assistance and thus help doctors diagnose this disease earlier.

Numerous supply-chain combines with internet of things (IoT) applications have been proposed, and many methods and algorithms enhance the convenience of supply chains. However, new businesses still find it challenging to enter a supply chain, because unauthorised IoT devices of different companies illegally access resources. As security is paramount in a supply chain, IoT management has become very difficult. Public resources allocation and waste management also pose a problem. To solve the above problems, we proposed a new IoT management framework that embraces blockchain technology to help companies to form a supply chain effectively. This framework consists of an access control system, a backup peer mechanism and an internal data isolation and transmission approach. The access control system has a registrar module and an inspection module. The registrar module is mainly responsible for information registration with a registration policy, which has to be followed by all the companies in the supply chain. Besides, it provides a revocation and updating function. The inspection module focuses on judging misbehaviour and monitors the actions of the subjects; when any misoperation occurs, the system will correspondingly penalise violators. So that all related actions and information are verified and stored into blockchain, the IoT access control and safety of IoT admission are enhanced. Furthermore, in a blockchain system, if one single peer in the network breaks down, then the whole system may stop, because consensus cannot be reached. The data of the broken peer may be lost if it does not commit yet. The backup peer mechanism allows the primary peer and the backup peer to connect to an inspecting server for acquiring real-time data. The internal data isolation and transmission modules transmit and stores private data without creating a new subchannel. The proposed method is taken full account of the stability of the network and the fault tolerance to guarantee the robust of the system. To obtain unbiases results, experiments are conducted in two different blockchain environment. The results show our proposed method are promising IoT blockchain system for the supply chain.