Objective: To investigate the influencing factors of the implementation of precision medical services based on clinician cognition, and provide a scientific reference for the implementation and advancement of precision medical services. Methods: Using electronic questionnaires, clinicians from 48 hospitals in 12 provinces(autonomous regions and municipalities directly under the Central Government) were surveyed from July to September 2019, with 341 valid questionnaires collected. Descriptive statistics and factor analysis were performed on the survey data. Results: Three dimensions that affect precision medical services were identified from the recovered questionnaires, namely external support factors, hospital drive capabilities, patient information provision and protection. The contribution rate of variance after orthogonal rotation was 35.157%, 22.234% and 16.343% respectively. Conclusions The external supporting factors should be developed, the hospital driving ability should be strengthened, and the patient information reception and privacy information security should be ensured to provide an important basis and guarantee for the implementation of precision medical service.

Proteases are enzymes that cleave and hydrolyse the peptide bonds between two specific amino acid residues of target substrate proteins. Protease-controlled proteolysis plays a key role in the degradation and recycling of proteins, which is essential for various physiological processes. Thus, solving the substrate identification problem will have important implications for the precise understanding of functions and physiological roles of proteases, as well as for therapeutic target identification and pharmaceutical applicability. Consequently, there is a great demand for bioinformatics methods that can predict novel substrate cleavage events with high accuracy by utilizing both sequence and structural information. In this study, we present Procleave, a novel bioinformatics approach for predicting protease-specific substrates and specific cleavage sites by taking into account both their sequence and 3D structural information. Structural features of known cleavage sites were represented by discrete values using a LOWESS data-smoothing optimization method, which turned out to be critical for the performance of Procleave. The optimal approximations of all structural parameter values were encoded in a conditional random field (CRF) computational framework, alongside sequence and chemical group-based features. Here, we demonstrate the outstanding performance of Procleave through extensive benchmarking and independent tests. Procleave is capable of correctly identifying most cleavage sites in the case study. Importantly, when applied to the human structural proteome encompassing 17,628 protein structures, Procleave suggests a number of potential novel target substrates and their corresponding cleavage sites of different proteases. Procleave is implemented as a webserver and is freely accessible at http://procleave.erc.monash.edu/.