Current Issue : July - September Volume : 2018 Issue Number : 3 Articles : 5 Articles
Alzheimer�s disease (AD) is a chronic and progressive neurodegenerative disorder and the pathogenesis ofADis poorly understood.\nG protein-coupled receptors (GPCRs) are involved in numerous key AD pathways and play a key role in the pathology of AD. To\nfully understand the pathogenesis of AD and design novel drug therapeutics, analyzing the connection between AD and GPCRs is\nof great importance. In this paper, we firstly build and analyze the AD-related pathway by consulting the KEGG pathway of AD and\na mass of literature and collect 25 AD-related GPCRs for drug discovery. Then the ILbind and AutoDock Vina tools are integrated\nto find out potential drugs related to AD. According to the analysis of DUD-E dataset, we select five drugs, that is, Acarbose (ACR),\nCarvedilol (CVD), Digoxin (DGX), NADH (NAI), and Telmisartan (TLS), by sorting the ILbind scores (�0.73). Then depending\non their AutoDock Vina scores and pocket position information, the binding patterns of these five drugs are obtained.We analyze\nthe regulation function of GPCRs in the metabolic network of AD based on the drug screen results, which may be helpful for the\nstudy of the off-target effect and the side effect of drugs....
Risk stratification model for lung cancer with gene expression profile is of great interest. Instead of previous models based on\nindividual prognostic genes, we aimed to develop a novel system-level risk stratification model for lung adenocarcinoma based on\ngene coexpression network. Using multiple microarray, gene coexpression network analysis was performed to identify survivalrelated\nnetworks. A deep learning based risk stratification model was constructed with representative genes of these networks. The\nmodel was validated in two test sets. Survival analysis was performed using the output of the model to evaluate whether it could\npredict patients� survival independent of clinicopathological variables. Five networks were significantly associated with patients�\nsurvival. Considering prognostic significance and representativeness, genes of the two survival-related networks were selected for\ninput of the model. The output of the model was significantly associated with patients� survival in two test sets and training set\n(...
Cancerlectins have an inhibitory effect on the growth of cancer cells and are currently being employed as therapeutic agents.\nThe accurate identification of the cancerlectins should provide insight into the molecular mechanisms of cancers. In this study,\na new computational method based on the RF (Random Forest) algorithm is proposed for further improving the performance\nof identifying cancerlectins. Hybrid feature space before feature selection is developed by combining different individual feature\nspaces, CTD (Composition, Transition, and Distribution), PseAAC (Pseudo Amino Acid Composition), PSSM (Position-Specific\nScoring Matrix), and disorder.The SMOTE (Synthetic Minority Oversampling Technique) is applied to solve the imbalanced data\nproblem. To reduce feature redundancy and computation complexity, we propose a two-step feature selection process to select\ninformative features. A 5-fold cross-validation technique is used for the evaluation of various prediction strategies. The proposed\nmethod achieves a sensitivity of 0.779, a specificity of 0.717, an accuracy of 0.748, and anMCC (Matthew�s Correlation Coefficient)\nof 0.497.The prediction results are also compared with other existing methods on the same dataset using 5-fold cross-validation.\nThe comparison results demonstrate the high effectiveness of our method for predicting cancerlectins...
High-accuracy alignment of sequences with disease information contributes to disease treatment and prevention. The results\nof multiple sequence alignment depend on the parameters of the objective function, including gap open penalties (GOP), gap\nextension penalties (GEP), and substitution matrix (SM). Firstly, the theory parameter formulas relating to GOP, GAP, and SMare\ninferred, combining unaligned sequence length, number, and identity. Secondly, we tested the rationality of the theory parameter\nformulas, with experiment on the ClustalW and MAFFT program. In addition,we obtained a group of MAFFT programparameters\naccording to the formulas proposed. The results of all experiments show that the SPS (sum-of-pair score) obtained from theory\nparameters is better than the SPS obtained from the default parameters of ClustalW and MAFFT. In both theory and practice,\nour method to determine the parameters is feasible and efficient. These can provide high-accuracy alignment results for precision\nmedicine....
As a novel class of noncoding RNAs, circular RNAs (circRNAs) have been reported to play a role in various biological processes.\nSome circRNAs may serve as microRNA (miRNA) sponges, regulating transcription or splicing. Herein, we investigated the\nexpression profiles and interactions of miRNAs/isomiRs and circRNAs in male patients with esophageal cancer. We found that\nsome miRNA genes generated two deregulated miRNA products (miR-#-5p and miR-#-3p), and these products were consistently\nabnormally expressed. Some circRNAs were predicted to be miRNA sponges for specific miRNAs. Some of these typically showed\nopposing expression patterns in cancer tissues: one upregulated and the other downregulated. Although fewer miRNAs were\npredicted to interact with circRNAs, the number of predicted interactions would be substantially increased if detailed isomiRs\nwere involved. High sequence similarity across multiple isomiRs suggested that they might interact with circRNAs, similar to the\ninteraction of homologous miRNAs with circRNAs. At the isomiR level, due to the characteristics of the sequences and expression\npatterns involved, the cross-talk between different ncRNAs is complicated despite simplification of the isomiRs involved through\nclustering.We expect that our results may provide methods for further study of the cross-talk among ncRNAs and elucidate their\nbiological roles in human diseases....
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