Titlebook: Bioinformatics Research and Applications; 17th International S Yanjie Wei,Min Li,Zhipeng Cai Conference proceedings 2021 Springer Nature Sw

wall-stress

率直

MKL-LP: Predicting Disease-Associated Microbes with Multiple-Similarity Kernel Learning-Based Label utational models to explore unbeknown microbe-disease associations, rather than using the traditionally experimental method which is usually expensive and costs time, is a hot research trend. In this paper, a new method, MKL-LP, which utilizes Multiple Kernel Learning (MKL) and Label Propagation (LP

lambaste

Immune-Microbiota Crosstalk Underlying Inflammatory Bowel DiseaseInflammatory bowel disease (IBD) is a result of the dysbiotic microbial composition together with aberrant mucosal immune responses, while the underlying mechanism is far from clear. In this report, we creatively proposed that when correlating with the host metabolism, functional microbial communiti

Painstaking

機(jī)械

conscience

袖章

Identification of Protein Markers Predictive of Drug-Specific Survival Outcome in Cancerse also critical to personalized medicine. In this study, we identified drug-specific biomarkers by integrating protein expression data, drug treatment data and survival outcome of 7076 patients from The Cancer Genome Atlas (TCGA). We first defined cancer-drug groups, where each cancer-drug group con

葡萄糖

Diabetic Retinopathy Grading Base on Contrastive Learning and Semi-supervised Learningorithms have been proposed, their performance is still limited by the characteristics of DR lesions and grading criteria, and coarse-grained image-level label. In this paper, we propose a novel approach based on contrastive learning and semi-supervised learning to break through these limitations. We

推延

Reinforcement Learning for Diabetes Blood Glucose Control with Meal Informationby diet and insulin dose. The goal of blood glucose management is to continuously control the blood glucose level of a patient in a normal range. Reinforcement learning models show good effectiveness and robustness in dealing with various nonlinear control problems. Because of the importance of diet

Pseudoephedrine

Predicting Microbe-Disease Association via Tripartite Network and Relation Graph Convolutional Netwoprevention. In this study, we propose a predictive model called TNRGCN for microbe-disease associations based on Tripartite Network and Relation Graph Convolutional Network (RGCN). Firstly, we construct a microbe-disease-drug tripartite network through data processing from four databases. Secondly,