Qifan Kuang

An eigenvalue transformation technique for predicting drug-target interaction

The prediction of drug-target interactions is a key step in the drug discovery process, which serves to identify new drugs or novel targets for existing drugs. However, experimental methods for predicting drug-target interactions are expensive and time-consuming. Therefore, the in silico prediction of drug-target interactions has recently attracted increasing attention. In this study, we propose an eigenvalue transformation technique and apply this technique to two representative algorithms, the Regularized Least Squares classifier (RLS) and the semi-supervised link prediction classifier (SLP), that have been used to predict drug-target interaction. The results of computational experiments with these techniques show that algorithms including eigenvalue transformation achieved better performance on drug-target interaction prediction than did the original algorithms. These findings show that eigenvalue transformation is an efficient technique for improving the performance of methods for predicting drug-target interactions. We further show that, in theory, eigenvalue transformation can be viewed as a feature transformation on the kernel matrix. Accordingly, although we only apply this technique to two algorithms in the current study, eigenvalue transformation also has the potential to be applied to other algorithms based on kernels.

Prediction of adverse drug reactions by a network based external link prediction method

Detecting adverse drug reaction (ADR) is a big challenge to drug development and post-marketing applications. Owing to the low costs and high performance, computational methods are used to predict unknown adverse reactions of drugs. In the present study, a network based method is developed, in which a bipartite network is introduced to represent associations between ADRs and drugs. The potential ADRs of a drug could be simply inferred by its neighbourhood in the bipartite network. Our method was applied on three datasets compiled from FAERS, SIDER and intersection of these two databases (gold standard data). Encouraging results were achieved, area under curve (AUC) values were 0.93, 0.94 and 0.83, respectively. To further evaluate the performance of our method, comparisons were made with internal link prediction method and logistic regression method on the gold standard data. Our method achieved an AUC value of 0.83, while the AUC values were 0.75 for both internal link prediction method and logistic regression method. The results show that it is feasible to predict unknown drug–ADR associations using only topology features of the drug–ADR network.

A systematic investigation of computation models for predicting Adverse Drug Reactions (ADRs).

Background Early and accurate identification of adverse drug reactions (ADRs) is critically important for drug development and clinical safety. Computer-aided prediction of ADRs has attracted increasing attention in recent years, and many computational models have been proposed. However, because of the lack of systematic analysis and comparison of the different computational models, there remain limitations in designing more effective algorithms and selecting more useful features. There is therefore an urgent need to review and analyze previous computation models to obtain general conclusions that can provide useful guidance to construct more effective computational models to predict ADRs. Principal Findings In the current study, the main work is to compare and analyze the performance of existing computational methods to predict ADRs, by implementing and evaluating additional algorithms that have been earlier used for predicting drug targets. Our results indicated that topological and intrinsic features were complementary to an extent and the Jaccard coefficient had an important and general effect on the prediction of drug-ADR associations. By comparing the structure of each algorithm, final formulas of these algorithms were all converted to linear model in form, based on this finding we propose a new algorithm called the general weighted profile method and it yielded the best overall performance among the algorithms investigated in this paper. Conclusion Several meaningful conclusions and useful findings regarding the prediction of ADRs are provided for selecting optimal features and algorithms.

Predicting putative adverse drug reaction related proteins based on network topological properties

Adverse drug reactions (ADRs) are one of the main issues restraining the development and clinical applications of new drugs. Owing to complicated molecular mechanisms of ADRs, various experimental and computational methods have been employed to detect them. It has been reported that a number of ADRs are induced by a series of actions triggered by drugs or their reactive metabolites that bind to therapeutic targets or other proteins involved in drug metabolism. The identification of these ADR-related proteins (ADRRPs) is an available avenue to explore adverse reactions of drugs. In this study, the human protein–protein interaction (PPI) network was constructed as a powerful tool for studying the molecular mechanisms of ADRs. Based on such a network, five network topological properties were calculated to characterize proteins quantitatively. Then a random forest model for ADRRP prediction was built which was dependent on these properties. The prediction model yielded a satisfactory result with a sensitivity of 87.3%, a specificity of 86.1% and an overall accuracy of 86.8%. Finally, text mining was applied to verify the predictions. Some of the predicted ADRRPs have been proved to be involved in regulating ADRs by experimental studies. The results suggested that the genome-wide human interaction network provides us with an effective channel for discovering ADRRPs.

Computational identifying and characterizing circular RNAs and their associated genes in hepatocellular carcinoma

Hepatocellular carcinoma (HCC) is currently still a major factor leading to death, lacking of reliable biomarkers. Therefore, deep understanding the pathogenesis for HCC is of great importance. The emergence of circular RNA (circRNA) provides a new way to study the pathogenesis of human disease. Here, we employed the prediction tool to identify circRNAs based on RNA-seq data. Then, to investigate the biological function of the circRNA, the candidate circRNAs were associated with the protein-coding genes (PCGs) by GREAT. We found significant candidate circRNAs expression alterations between normal and tumor samples. Additionally, the PCGs associated with these candidate circRNAs were also found have discriminative expression patterns between normal and tumor samples. The enrichment analysis illustrated that these PCGs were predominantly enriched for liver/cardiovascular-related diseases such as atherosclerosis, myocardial ischemia and coronary heart disease, and participated in various metabolic processes. Together, a further network analysis indicated that these PCGs play important roles in the regulatory and the PPI network. Finally, we built a classification model to distinguish normal and tumor samples by using candidate circRNAs and their associated genes, respectively. Both of them obtained satisfactory results (~ 0.99 of AUC for circRNA and PCG). Our findings suggested that the circRNA could be a critical factor in HCC, providing a useful resource to explore the pathogenesis of HCC.

Improving the Understanding of Pathogenesis of Human Papillomavirus 16 via Mapping Protein-Protein Interaction Network

The human papillomavirus 16 (HPV16) has high risk to lead various cancers and afflictions, especially, the cervical cancer. Therefore, investigating the pathogenesis of HPV16 is very important for public health. Protein-protein interaction (PPI) network between HPV16 and human was used as a measure to improve our understanding of its pathogenesis. By adopting sequence and topological features, a support vector machine (SVM) model was built to predict new interactions between HPV16 and human proteins. All interactions were comprehensively investigated and analyzed. The analysis indicated that HPV16 enlarged its scope of influence by interacting with human proteins as much as possible. These interactions alter a broad array of cell cycle progression. Furthermore, not only was HPV16 highly prone to interact with hub proteins and bottleneck proteins, but also it could effectively affect a breadth of signaling pathways. In addition, we found that the HPV16 evolved into high carcinogenicity on the condition that its own reproduction had been ensured. Meanwhile, this work will contribute to providing potential new targets for antiviral therapeutics and help experimental research in the future.

Improving the prediction of chemotherapeutic sensitivity of tumors in breast cancer via optimizing the selection of candidate genes.

Estrogen receptor status and the pathologic response to preoperative chemotherapy are two important indicators of chemotherapeutic sensitivity of tumors in breast cancer, which are used to guide the selection of specific regimens for patients. Microarray-based gene expression profiling, which is successfully applied to the discovery of tumor biomarkers and the prediction of drug response, was suggested to predict the cancer outcomes using the gene signatures differentially expressed between two clinical states. However, many false positive genes unrelated to the phenotypic differences will be involved in the lists of differentially expressed genes (DEGs) when only using the statistical methods for gene selection, e.g. Students t test, and subsequently affect the performance of the predictive models. For the purpose of improving the prediction of clinical outcomes, we optimized the selection of DEGs by using a combined strategy, for which the DEGs were firstly identified by the statistical methods, and then filtered by a similarity profiling approach that used for candidate gene prioritization. In our study, we firstly verified the molecular functions of the DEGs identified by the combined strategy with the gene expression data generated in the microarray experiments of Si-Wu-Tang, which is a popular formula in traditional Chinese medicine. The results showed that, for Si-Wu-Tang experimental data set, the cancer-related signaling pathways were significantly enriched by gene set enrichment analysis when using the DEG lists generated by the combined strategy, confirming the potentially cancer-preventive effect of Si-Wu-Tang. To verify the performance of the predictive models in clinical application, we used the combined strategy to select the DEGs as features from the gene expression data of the clinical samples, which were collected from the breast cancer patients, and constructed models to predict the chemotherapeutic sensitivity of tumors in breast cancer. After refining the DEG lists by a similarity profiling approach, the Matthews correlation coefficients of predicting estrogen receptor status and the pathologic response to preoperative chemotherapy with the DEGs selected by the fold change ranking were 0.770 and 0.428, respectively, and were 0.748 and 0.373 with the DEGs selected by SAM, respectively, which were generally higher than those achieved with unrefined DEG lists and those achieved by the candidate models in the second phase of Microarray Quality Control project (0.732 and 0.301, respectively). Our results demonstrated that the strategy of integrating the statistical methods with the gene prioritization methods based on similarity profiling was a powerful tool for DEG selection, which effectively improved the performance of prediction models in clinical applications and can guide the personalized chemotherapy better.

Age-related changes in functional connectivity between young adulthood and late adulthood

Recently, age-related changes in functional connectivity have gained more attention for investigating functional changes across development. In this study, we examine functional connectivity, as derived from resting state functional magnetic resonance imaging (R-fMRI), in 90 cortical and subcortical regions in two healthy groups in young adulthood (ages 18–28 years) and late adulthood (ages 63–73 years). Comparing the processes for constructing a functional network, we found that the network in young adulthood was more easily fully connected than that in late adulthood, indicating that the central regions, frontal lobe, parietal lobe and limbic lobe possibly occupy more resources in late adulthood. We confirmed that the brain in both young adulthood and late adulthood had a “small-world” organization, and that there was a further loss of small-world characteristics in late adulthood. Additionally, we found that late adulthood exhibited a more social-like organization of the brain functional network than young adulthood. Furthermore, the connectivity density showed a general decrease in most brain areas, but only the temporal lobe and occipital lobe showed a decrease in connectivity strength in late adulthood. Conversely, the parietal lobe showed an increase in connectivity density in late adulthood. Our study provides additional support for elucidating the functional changes of the brain across development, and characterizing these changes will lead to a better understanding of the cognitive decline that occurs with advancing age.

A facile strategy applied to simultaneous qualitative-detection on multiple components of mixture samples: a joint study of infrared spectroscopy and multi-label algorithms on PBX explosives

We report a facile yet effective strategy of utilizing a combination of Fourier transform-infrared spectroscopy (FTIR) and multi-label algorithms, through which multi-components in polymer bonded explosives (PBXs) could be rapidly and simultaneously identified with high accuracy. The explosive components include 1,3,5,7-tetranitro-1,3,5,7-tetraazacyclo-octane (HMX), hexahydro-1,3,5-trinitro-1,3,5-triazine (RDX), 2,4,6-triamino-1,3,5-trinitrobenzene (TATB) and 2,4,6-trinitrotoluene (TNT) involved in single-component, binary-component and ternary-component PBXs. The train set contains 354 FTIR spectra of the explosives while the independent test set contains 84. Two multi-label strategies (viz., data decomposition and algorithm adaptation) were adopted to construct the classification model with an objective of testing their efficiency in the multi-classification application. Principal component analysis (PCA) was applied to reduce the variables. Both the two algorithms exhibit excellent performance with 100% accuracy for the training and the independent test sets. However, for real PBX samples, the performance of the algorithm adaptation strategy is sharply decreased to 40% accuracy. But, it is noteworthy that the data decomposition strategy still achieves the accuracy of 100% for the real samples, exhibiting stronger robustness for the background interference and high promise in practice. The strategy proposed by the work would provide valuable information for advancing analytical methods in the explosive detection system and the other complicated samples.

Functional annotation of sixty-five type-2 diabetes risk SNPs and its application in risk prediction

Genome-wide association studies (GWAS) have identified more than sixty single nucleotide polymorphisms (SNPs) associated with increased risk for type 2 diabetes (T2D). However, the identification of causal risk SNPs for T2D pathogenesis was complicated by the factor that each risk SNP is a surrogate for the hundreds of SNPs, most of which reside in non-coding regions. Here we provide a comprehensive annotation of 65 known T2D related SNPs and inspect putative functional SNPs probably causing protein dysfunction, response element disruptions of known transcription factors related to T2D genes and regulatory response element disruption of four histone marks in pancreas and pancreas islet. In new identified risk SNPs, some of them were reported as T2D related SNPs in recent studies. Further, we found that accumulation of modest effects of single sites markedly enhanced the risk prediction based on 1989 T2D samples and 3000 healthy controls. The AROC value increased from 0.58 to 0.62 by only using genotype score when putative risk SNPs were added. Besides, the net reclassification improvement is 10.03% on the addition of new risk SNPs. Taken together, functional annotation could provide a list of prioritized potential risk SNPs for the further estimation on the T2D susceptibility of individuals.