Pneumonia, especially corona virus disease 2019 (COVID-19), can lead to serious acute lung injury, acute respiratory distress syndrome, multiple organ failure and even death. Thus it is an urgent task for developing high-efficiency, low-toxicity and targeted drugs according to pathogenesis of coronavirus. In this paper, a novel disease-related compound identification model–based capsule network (CapsNet) is proposed. According to pneumonia-related keywords, the prescriptions and active components related to the pharmacological mechanism of disease are collected and extracted in order to construct training set. The features of each component are extracted as the input layer of capsule network. CapsNet is trained and utilized to identify the pneumonia-related compounds in Qingre Jiedu injection. The experiment results show that CapsNet can identify disease-related compounds more accurately than SVM, RF, gcForest and forgeNet.

Post-translational modification plays a key role in the field of biology. Experimental identification methods are time-consuming and expensive. Therefore, computational methods to deal with such issues overcome these shortcomings and limitations. In this article, we propose a lysine acetylation site identification with polynomial tree method (LAIPT), making use of the polynomial style to demonstrate amino-acid residue relationships in peptide segments. This polynomial style was enriched by the physical and chemical properties of amino-acid residues. Then, these reconstructed features were input into the employed classification model, named the flexible neural tree. Finally, some effect evaluation measurements were employed to test the model’s performance.

Proteins are the material foundation of life, and they are organic macromolecules that make up the basic organic matter of cells. Therefore, proteins can be considered as the main bearers of life activities. Proteins are important components that make up all cells and tissues in an organism. All critical elements of an organism require the participation of proteins, and the most important thing is that they are related to life phenomena. The transportation and localization of proteins within organelles is a complex and delicate process that involves multiple steps and mechanisms. Organelle proteins are an essential element in several biological processions. In this work, we proposed TAPE_selection methods to reduce the useless information of the Tasks Assessing Protein Embed-dings (TAPE) feature in some organelle proteins, which mainly include plant vacuole proteins (PVPs) and peroxidase ones. In order to reduce the useless information, we employed some feature selection Strategies, including the Chi-Squared Test, Minimum Redundancy Maximum Relevance(mRMR), and Neighborhood Components Analysis (NCA). With the selected feature, the Proper Orthogonal Decomposition (POD) and t-distributed Stochastic Neighbor Embedding (t-SNE) were employed to reduce the reconstructed feature scale.