RESUMO
Predicting drug-disease associations (DDAs) through computational methods has become a prevalent trend in drug development because of their high efficiency and low cost. Existing methods usually focus on constructing heterogeneous networks by collecting multiple data resources to improve prediction ability. However, potential association possibilities of numerous unconfirmed drug-related or disease-related pairs are not sufficiently considered. In this article, we propose a novel computational model to predict new DDAs. First, a heterogeneous network is constructed, including four types of nodes (drugs, targets, cell lines, diseases) and three types of edges (associations, association scores, similarities). Second, an updating and merging-based similarity network fusion method, termed UM-SF, is presented to fuse various similarity networks with diverse weights. Finally, an intermediate layer-mediated multi-view feature projection representation method, termed IM-FP, is proposed to calculate the predicted DDA scores. This method uses multiple association scores to construct multi-view drug features, then projects them into disease space through the intermediate layer, where an intermediate layer similarity constraint is designed to learn the projection matrices. Results of comparative experiments reveal the effectiveness of our innovations. Comparisons with other state-of-the-art models by the 10-fold cross-validation experiment indicate our model's advantage on AUROC and AUPR metrics. Moreover, our proposed model successfully predicted 107 novel high-ranked DDAs.
RESUMO
COVID-19 vaccines greatly reduce the risk of infection with SARS-CoV-2. However, some people have adverse reactions after vaccination, and these can sometimes be severe. Gender, age, vaccines, and especially certain diseases histories are related to severe adverse reactions following COVID-19 vaccination. However, there are thousands of diseases and only some are known to be related to these severe adverse reactions. The risk of severe adverse reactions with other diseases remains unknown. Therefore, there is a need for predictive studies to provide improved medical care and minimize risk. Herein, we analyzed the statistical results of existing COVID-19 vaccine adverse reaction data and proposed a COVID-19 vaccine severe adverse reaction risk prediction method, named CVSARRP. The performance of the CVSARRP method was tested using the leave-one-out cross-validation approach. The correlation coefficient between the predicted and real risk is greater than 0.86. The CVSARRP method predicts the risk from adverse reactions to severe adverse reactions after COVID-19 vaccination for 10855 diseases. People with certain diseases, such as central nervous system diseases, heart diseases, urinary system disease, anemia, cancer, and respiratory tract disease, among others, may potentially have increased of severe adverse reactions following vaccination against COVID-19 and experiencing adverse events.
RESUMO
As a potential biomarker to predict the response to immunotherapy, tumor mutation burden (TMB) which can be estimated by the cancer gene panel (CGP) has received considerable attention. However, it is not clear which CGP is better in predicting the efficacy of immunotherapy. To evaluate the twelve CGPs, we compared them on 13 datasets of melanoma and non-small cell lung cancer (NSCLC) from the perspective of gene composition, reliability of measuring TMB and prediction performance of patient treatment benefits. The larger CGPs generally performed better, but their proportions of driver genes and function densities were smaller. The CGPs performed differently on melanoma and NSCLC patients treated with two blockades. Moreover, their ability to classify and predict patients with or without long-term clinical benefits was similar but not good enough, so it is necessary to explore a higher-performance biomarker.
