Accurate identification of the miRNA-disease associations (MDAs) helps to understand the etiology and mechanisms of various diseases. However, the experimental methods are costly and time-consuming. Thus, it is urgent to develop computational methods towards the prediction of MDAs. Based on the graph theory, the MDA prediction is regarded as a node classification task in the present study. To solve this task, we propose a novel method MDA-GCNFTG, which predicts MDAs based on Graph Convolutional Networks (GCNs) via graph sampling through the Feature and Topology Graph to improve the training efficiency and accuracy. This method models both the potential connections of feature space and the structural relationships of MDA data. The nodes of the graphs are represented by the disease semantic similarity, miRNA functional similarity and Gaussian interaction profile kernel similarity. Moreover, we considered six tasks simultaneously on the MDA prediction problem at the first time, which ensure that under both balanced and unbalanced sample distribution, MDA-GCNFTG can predict not only new MDAs but also new diseases without known related miRNAs and new miRNAs without known related diseases. The results of 5-fold cross-validation show that the MDA-GCNFTG method has achieved satisfactory performance on all six tasks and is significantly superior to the classic machine learning methods and the state-of-the-art MDA prediction methods. Moreover, the effectiveness of GCNs via the graph sampling strategy and the feature and topology graph in MDA-GCNFTG has also been demonstrated. More importantly, case studies for two diseases and three miRNAs are conducted and achieved satisfactory performance.

The long non-coding RNAs (lncRNAs) are subject of intensive recent studies due to its association with various human diseases. It is desirable to build the artificial intelligence-based models for prediction of diseases or tissues based on the lncRNAs data, which will be useful in disease diagnosis and therapy. The accuracy and robustness of existing models based on the machine learning techniques are subject to further improvement. In this study, we propose a deep learning model, called Multi-Label Classifications with Deep Forest, termed MLCDForest, to address multi-label classification on tissue prediction for a given lncRNA, which can be regarded as an implementation of the deep forest model in multi-label classification. The MLCDForest is a sequential multi-label-grained scanning method, which distinguishes from the standard deep forest model. It is proposed to train in sequential of multi-labels with label correlation considered. A systematic comparison using the lncRNA-disease association datasets demonstrates that our method consistently shows superior performance over the state-of-the-art methods in disease prediction. Considering label correlation in the sequential multi-label-grained scanning, our model provides a powerful tool to make multi-label classification and tissue prediction based on given lncRNAs.

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Background: In previous studies on the application of cyclin-dependent kinase 4/6 (CDK4/6) inhibitors combined with endocrine therapy in advanced breast cancer, the outcomes of overall survival (OS) were inconsistent. This systematic review and meta-analysis aimed to further evaluate the clinical efficacy and safety of CDK4/6 inhibitors combined with endocrine therapy on patients with hormone receptor (HR)positive and human epidermal growth factor receptor 2 (HER2)-negative advanced breast cancer.Methods: Randomized controlled trials (RCTs) comparing CDK4/6 inhibitors plus endocrine therapy and endocrine therapy alone in patients with HR-positive and HER2-negative advanced breast cancer were searched in the databases of PubMed, Embase, Cochrane Library, China National Knowledge Infrastructure (CNKI), WANFANG and China Science and Technology Journal Database (VIP) up to November 2022.Hazard ratios (HRs) and confidence intervals (CI) of OS, progression-free survival (PFS), the time from randomization to the first recorded disease progression while the patient was receiving next-line therapy or death from any cause (PFS2), time to first subsequent chemotherapy after discontinuation (TTC), and objective response rate (ORR), clinical benefit rate (CBR), safety indicators were extracted. Stata 14.0 software was used for meta analysis and the Cochrane risk-of-bias tool 2.0 was used to evaluate the bias risk.

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