Pulse diagnosis is of great research significance and practical value in clinical diagnosis. However, current research has focused on objectifying the pulse signal and has neglected the influence of the TCM constitution on the pulse signal and disease. The effective utilization of constitution factors is the urgent need for pulse analysis and the incorporation of constitution factors into pulse diagnosis to diagnose hypertension will help to improve the TCM connotation of pulse diagnosis. To solve the problems, we first developed a simple, convenient, and noninvasive multichannel pulse sampler, and constructed a public pulse dataset. Then we propose a pulse preprocessing framework, which integrates four pulse preprocessing operations. This method makes the pulse preprocessing operation more efficient. Then we used statistical analysis to determine the hypertension susceptibility constitution factors. The results showed that Yang-deficiency (YaD), Yin-deficiency (YiD), and Phlegm-dampness (PD) were significantly correlated with whether the subjects suffered from hypertension. The pulse feature samples that included hypertension susceptibility constitution factors had the highest classification accuracy in distinguishing between the two types of healthy samples and hypertensive samples. This shows that the inclusion of constitution factors in the pulse features is important for disease classification.

Synthetic lethality (SL) occurs between two genes when the inactivation of either gene alone has no effect on cell survival but the inactivation of both genes results in cell death. SL-based therapy has become one of the most promising targeted cancer therapies in the last decade as PARP inhibitors achieve great success in the clinic. The key point to exploiting SL-based cancer therapy is the identification of robust SL pairs. Although many wet-lab-based methods have been developed to screen SL pairs, known SL pairs are less than 0.1% of all potential pairs due to large number of human gene combinations. Computational prediction methods complement wet-lab-based methods to effectively reduce the search space of SL pairs. In this paper, we review the recent applications of computational methods and commonly used databases for SL prediction. First, we introduce the concept of SL and its screening methods. Second, various SL-related data resources are summarized. Then, computational methods including statistical-based methods, network-based methods, classical machine learning methods and deep learning methods for SL prediction are summarized. In particular, we elaborate on the negative sampling methods applied in these models. Next, representative tools for SL prediction are introduced. Finally, the challenges and future work for SL prediction are discussed.

Combination therapy has shown an obvious efficacy on complex diseases and can greatly reduce the development of drug resistance. However, even with high-throughput screens, experimental methods are insufficient to explore novel drug combinations. In order to reduce the search space of drug combinations, there is an urgent need to develop more efficient computational methods to predict novel drug combinations. In recent decades, more and more machine learning (ML) algorithms have been applied to improve the predictive performance. The object of this study is to introduce and discuss the recent applications of ML methods and the widely used databases in drug combination prediction. In this study, we first describe the concept and controversy of synergism between drug combinations. Then, we investigate various publicly available data resources and tools for prediction tasks. Next, ML methods including classic ML and deep learning methods applied in drug combination prediction are introduced. Finally, we summarize the challenges to ML methods in prediction tasks and provide a discussion on future work.