Journal of Computer Science & Systems Biology

Personalized medicine is an emerging approach that aims to provide customized healthcare solutions based on a patient's unique genetic makeup and health history. However, the success of personalized medicine relies heavily on the availability of accurate and comprehensive medical data. The sharing of such data is often hindered by concerns over privacy, security, and interoperability. Block chain technology has emerged as a potential solution to these challenges by enabling secure and efficient sharing of medical data among authorized parties. This paper explores the potential of block chain technology for the sharing of medical data in personalized medicine. It examines the advantages of block chain technology, the challenges of implementing it in healthcare, and the potential use cases of block chain technology in personalized medicine.

Drug discovery is a time-consuming and expensive process that relies on identifying compounds that interact with target proteins. In recent years, the use of network analysis and machine learning techniques has shown great promise in predicting drug-target interactions. In this paper, we present a data-driven framework for predicting drug-target interactions using network analysis and machine learning techniques. Our framework involves the construction of a drug-target interaction network and the use of various network analysis techniques to identify topological features that are indicative of drug-target interactions. We also use machine learning techniques to train a predictive model that can accurately predict drugtarget interactions. Our framework was evaluated on several benchmark datasets and demonstrated superior performance compared to existing state-of-the-art methods. We believe that our framework has the potential to significantly accelerate the drug discovery process.