iGRLCDA: identifying circRNA-disease association based on graph representation learning.

While the technologies of ribonucleic acid-sequence (RNA-seq) and transcript assembly analysis have continued to improve, a novel topology of RNA transcript was uncovered in the last decade and is called circular RNA (circRNA). Recently, researchers have revealed that they compete with messenger RNA (mRNA) and long noncoding for combining with microRNA in gene regulation. Therefore, circRNA was assumed to be associated with complex disease and discovering the relationship between them would contribute to medical research. However, the work of identifying the association between circRNA and disease in vitro takes a long time and usually without direction. During these years, more and more associations were verified by experiments. Hence, we proposed a computational method named identifying circRNA-disease association based on graph representation learning (iGRLCDA) for the prediction of the potential association of circRNA and disease, which utilized a deep learning model of graph convolution network (GCN) and graph factorization (GF). In detail, iGRLCDA first derived the hidden feature of known associations between circRNA and disease using the Gaussian interaction profile (GIP) kernel combined with disease semantic information to form a numeric descriptor. After that, it further used the deep learning model of GCN and GF to extract hidden features from the descriptor. Finally, the random forest classifier is introduced to identify the potential circRNA-disease association. The five-fold cross-validation of iGRLCDA shows strong competitiveness in comparison with other excellent prediction models at the gold standard data and achieved an average area under the receiver operating characteristic curve of 0.9289 and an area under the precision-recall curve of 0.9377. On reviewing the prediction results from the relevant literature, 22 of the top 30 predicted circRNA-disease associations were noted in recent published papers. These exceptional results make us believe that iGRLCDA can provide reliable circRNA-disease associations for medical research and reduce the blindness of wet-lab experiments.

Total Synthesis of Dysifragilones A and B and Dysidavarone C.

The concise synthesis of dysifragilones A and B and dysidavarones has been accomplished for the first time in a divergent way from a common intermediate. The synthetic route features an intramolecular reductive Heck reaction to construct the 6/5/6/6/-tetracycle of dysifragilones A and B and an intramolecular palladium-catalyzed α-arylation of a sterically hindered ketone to forge the tetracyclo[7.7.1.02,7.010,15]heptadecane core structure of dysidavarone C. The late-stage introduction of amino and ethoxy groups is effective.

GCNCDA: A new method for predicting circRNA-disease associations based on Graph Convolutional Network Algorithm.

Numerous evidences indicate that Circular RNAs (circRNAs) are widely involved in the occurrence and development of diseases. Identifying the association between circRNAs and diseases plays a crucial role in exploring the pathogenesis of complex diseases and improving the diagnosis and treatment of diseases. However, due to the complex mechanisms between circRNAs and diseases, it is expensive and time-consuming to discover the new circRNA-disease associations by biological experiment. Therefore, there is increasingly urgent need for utilizing the computational methods to predict novel circRNA-disease associations. In this study, we propose a computational method called GCNCDA based on the deep learning Fast learning with Graph Convolutional Networks (FastGCN) algorithm to predict the potential disease-associated circRNAs. Specifically, the method first forms the unified descriptor by fusing disease semantic similarity information, disease and circRNA Gaussian Interaction Profile (GIP) kernel similarity information based on known circRNA-disease associations. The FastGCN algorithm is then used to objectively extract the high-level features contained in the fusion descriptor. Finally, the new circRNA-disease associations are accurately predicted by the Forest by Penalizing Attributes (Forest PA) classifier. The 5-fold cross-validation experiment of GCNCDA achieved 91.2% accuracy with 92.78% sensitivity at the AUC of 90.90% on circR2Disease benchmark dataset. In comparison with different classifier models, feature extraction models and other state-of-the-art methods, GCNCDA shows strong competitiveness. Furthermore, we conducted case study experiments on diseases including breast cancer, glioma and colorectal cancer. The results showed that 16, 15 and 17 of the top 20 candidate circRNAs with the highest prediction scores were respectively confirmed by relevant literature and databases. These results suggest that GCNCDA can effectively predict potential circRNA-disease associations and provide highly credible candidates for biological experiments.

LMTRDA: Using logistic model tree to predict MiRNA-disease associations by fusing multi-source information of sequences and similarities.

Emerging evidence has shown microRNAs (miRNAs) play an important role in human disease research. Identifying potential association among them is significant for the development of pathology, diagnose and therapy. However, only a tiny portion of all miRNA-disease pairs in the current datasets are experimentally validated. This prompts the development of high-precision computational methods to predict real interaction pairs. In this paper, we propose a new model of Logistic Model Tree for predicting miRNA-Disease Association (LMTRDA) by fusing multi-source information including miRNA sequences, miRNA functional similarity, disease semantic similarity, and known miRNA-disease associations. In particular, we introduce miRNA sequence information and extract its features using natural language processing technique for the first time in the miRNA-disease prediction model. In the cross-validation experiment, LMTRDA obtained 90.51% prediction accuracy with 92.55% sensitivity at the AUC of 90.54% on the HMDD V3.0 dataset. To further evaluate the performance of LMTRDA, we compared it with different classifier and feature descriptor models. In addition, we also validate the predictive ability of LMTRDA in human diseases including Breast Neoplasms, Breast Neoplasms and Lymphoma. As a result, 28, 27 and 26 out of the top 30 miRNAs associated with these diseases were verified by experiments in different kinds of case studies. These experimental results demonstrate that LMTRDA is a reliable model for predicting the association among miRNAs and diseases.

[Case-control study on T-shaped locking internal fixation and external fixation for the treatment of dorsal Barton's fracture].

OBJECTIVE: To compare clinical effect of T-shaped locking internal fixation and external fixation in treating dorsal Barton's fracture,and investigate selective strategy of internal fixation. METHODS: From January 2008 to January 2013, 100 patients with dorsal Barton's fracture were randomly divided into two groups. In treatment group, there were 30 males and 20 females with an average age of (33.8±3.6) years old;30 cases were type B, 20 cases were type C;and treated with T-shaped locking internal fixation. In control group, there were 32 male and 18 females with an average age of (32.9±3.4) years old; 29 cases were type B, 21 cases were type C; and treated with external fixation. Volar tilt, ulnar deviation and radial height at 3 months after operation were detected and compared between two groups. Mechara functional evaluation were used to evaluate postoperative clinical effects. Clinical cure time, postoperative complications,joint mobility and function score were recorded and compared between two groups. RESULTS: In treatment group,volar tilt was (11.9±2.7)°, ulnar deviation was (20.8+ 2.9)°,and radial height was (10.9±1.8) mm; while volar tilt was (9.1±1.6)°, ulnar deviation was (17.1±2.9)°, and radial height was (8.1±1.5) mm in control group. Treatment group was better than control group in volar tilt, ulnar deviation and radial height. Clinical cure time in treatment group was(12.0±2.3) weeks, shorter than control group (18.0±4.1) weeks. The incidence of complications in treatment group was lower than control group. According to Mehara functional evaluation,20 cases got excellent results, 25 good, 3 moderate and 2 poor in treatment group; 16 cases got excellent results, 14 good, 10 moderate and 10 poor in control group. Treatment group was better than control group in clinical effects. CONCLUSION: T-shaped locking internal fixation with postoperative functional exercise for the treatment of dorsal Barton's fracture fits for biomechanics demands,and has advantages of stable fixation,rapid recovery, less complications and good functional recovery, it has better clinical effects.

A characteristic condition of finite nilpotent group.

This paper gives a characteristic condition of finite nilpotent group under the assumption that all minimal subgroups of G are well-suited in G.