Construction of Complex Features for Computational Predicting ncRNA-Protein Interaction

Non-coding RNA (ncRNA) plays important roles in many critical regulation processes. Many ncRNAs perform their regulatory functions by the form of RNA-protein complexes. Therefore, identifying the interaction between ncRNA and protein is fundamental to understand functions of ncRNA. Under pressures from expensive cost of experimental techniques, developing an accuracy computational predictive model has become an indispensable way to identify ncRNA-protein interaction. A powerful predicting model of ncRNA-protein interaction needs a good feature set of characterizing the interaction. In this paper, a novel method is put forward to generate complex features for characterizing ncRNA-protein interaction (named CFRP). To obtain a comprehensive description of ncRNA-protein interaction, complex features are generated by non-linear transformations from the traditional k-mer features of ncRNA and protein sequences. To further reduce the dimensions of complex features, a group of discriminative features are selected by random forest. To validate the performances of the proposed method, a series of experiments are carried on several widely-used public datasets. Compared with the traditional k-mer features, the CFRP complex features can boost the performances of ncRNA-protein interaction prediction model. Meanwhile, the CFRP-based prediction model is compared with several state-of-the-art methods, and the results show that the proposed method achieves better performances than the others in term of the evaluation metrics. In conclusion, the complex features generated by CFRP are beneficial for building a powerful predicting model of ncRNA-protein interaction

Large-Scale VCE Consequence Modeling for Industrial Facility Siting, Risk Assessment, Hazard Mitigation Design, and Response Planning

PresentationNew, fully three dimensional, technologies are now making it possible to more quickly and accurately simulate and predict the consequences of accidental releases leading to vapor cloud explosions (VCE) at a wide variety of industrial facilities. The primary objective of these explosion consequence analysis (ECA) technologies is to assess building damage and occupant injury levels for both ‘individual’ and ‘all possible’ release and explosion scenarios. A typical industrial facility can have hundreds of potential release scenarios and hundreds of potential VCE locations leading to an exponential number of possible explosion scenarios. High speed 3D modeling techniques can provide updateable, on-going and real-time capabilities for analyzing individual and all possible release and explosion scenarios. These ECA technologies, combined with release probabilities, make possible quantitative risk assessments (QRA) which lead to better risk evaluations, mitigation strategies, risk management, and emergency response planning. In this paper, the Vapor Cloud Explosion Damage Assessment module in BREEZE ExDAM is used to demonstrate a high speed 3D modeling technique that can quickly 1) generate 3D models of large-scale chemical/petroleum facilities with hundreds of building structures, hundreds of release locations, and hundreds of congestion zones, 2) simulate, display, and document the consequences of individual release scenarios involving a subset of congestion zones within a single plume geometry, and 3) compute, display, and document the maximum consequence levels resulting from explosions at all identified congestion zones

A least square method based model for identifying protein complexes in protein-protein interaction network

Protein complex formed by a group of physical interacting proteins plays a crucial role in cell activities. Great effort has been made to computationally identify protein complexes from protein-protein interaction (PPI) network. However, the accuracy of the prediction is still far from being satisfactory, because the topological structures of protein complexes in the PPI network are too complicated. This paper proposes a novel optimization framework to detect complexes from PPI network, named PLSMC. The method is on the basis of the fact that if two proteins are in a common complex, they are likely to be interacting. PLSMC employs this relation to determine complexes by a penalized least squares method. PLSMC is applied to several public yeast PPI networks, and compared with several state-of-the-art methods. The results indicate that PLSMC outperforms other methods. In particular, complexes predicted by PLSMC can match known complexes with a higher accuracy than other methods. Furthermore, the predicted complexes have high functional homogeneity

Mining disease genes using integrated protein–protein interaction and gene–gene co-regulation information

AbstractIn humans, despite the rapid increase in disease-associated gene discovery, a large proportion of disease-associated genes are still unknown. Many network-based approaches have been used to prioritize disease genes. Many networks, such as the protein–protein interaction (PPI), KEGG, and gene co-expression networks, have been used. Expression quantitative trait loci (eQTLs) have been successfully applied for the determination of genes associated with several diseases. In this study, we constructed an eQTL-based gene–gene co-regulation network (GGCRN) and used it to mine for disease genes. We adopted the random walk with restart (RWR) algorithm to mine for genes associated with Alzheimer disease. Compared to the Human Protein Reference Database (HPRD) PPI network alone, the integrated HPRD PPI and GGCRN networks provided faster convergence and revealed new disease-related genes. Therefore, using the RWR algorithm for integrated PPI and GGCRN is an effective method for disease-associated gene mining

Computational Prediction of Protein Function Based on Weighted Mapping of Domains and GO Terms

In this paper, we propose a novel method, SeekFun, to predict protein function based on weighted mapping of domains and GO terms. Firstly, a weighted mapping of domains and GO terms is constructed according to GO annotations and domain composition of the proteins. The association strength between domain and GO term is weighted by symmetrical conditional probability. Secondly, the mapping is extended along the true paths of the terms based on GO hierarchy. Finally, the terms associated with resident domains are transferred to host protein and real annotations of the host protein are determined by association strengths. Our careful comparisons demonstrate that SeekFun outperforms the concerned methods on most occasions. SeekFun provides a flexible and effective way for protein function prediction. It benefits from the well-constructed mapping of domains and GO terms, as well as the reasonable strategy for inferring annotations of protein from those of its domains

Transmission efficiency of the plague pathogen (Y. pestis) by the flea, Xenopsylla skrjabini, to mice and great gerbils

Abstract Background Plague, a zoonotic disease caused by Yersinia pestis, is characterized by its ability to persist in the plague natural foci. Junggar Basin plague focus was recently identified in China, with Rhombomys opimus (great gerbils) and Xenopsylla skrjabini as the main reservoir and vector for plague. No transmission efficiency data of X. skrjabini for Y. pestis is available till now. Methods In this study, we estimated the median infectious dose (ID50) and the blockage rates of X. skrjabini with Y. pestis, by using artificial feeders. We then evaluated the flea transmission ability of Y. pestis to the mice and great gerbils via artificial bloodmeal feeding. Finally, we investigated the transmission of Y. pestis to mice with fleas fed by infected great gerbils. Results ID50 of Y. pestis to X. skrjabini was estimated as 2.04 × 105 CFU (95% CI, 1.45 × 105 – 3.18 × 105 CFU), around 40 times higher than that of X. cheopis. Although fleas fed by higher bacteremia bloodmeal had higher infection rates for Y. pestis, they lived significantly shorter than their counterparts. X. skrjabini could get fully blocked as early as day 3 post of infection (7.1%, 3/42 fleas), and the overall blockage rate of X. cheopis was estimated as 14.9% (82/550 fleas) during the 14 days of investigation. For the fleas infected by artificial feeders, they seemed to transmit plague more efficiently to great gerbils than mice. Our single flea transmission experiments also revealed that, the transmission capacity of naturally infected fleas (fed by infected great gerbils) was significantly higher than that of artificially infected ones (fed by artificial feeders). Conclusion Our results indicated that ID50 of Y. pestis to X. skrjabini was higher than other fleas like X. cheopis, and its transmission efficiency to mice might be lower than other flea vectors in the artificial feeding modes. We also found different transmission potentials in the artificially infected fleas and the naturally infected ones. Further studies are needed to figure out the role of X. skrjabini in the plague epidemiological cycles in Junggar Basin plague focus

Prediction of microRNAs Associated with Human Diseases Based on Weighted <i>k</i> Most Similar Neighbors

<div><p>Background</p><p>The identification of human disease-related microRNAs (disease miRNAs) is important for further investigating their involvement in the pathogenesis of diseases. More experimentally validated miRNA-disease associations have been accumulated recently. On the basis of these associations, it is essential to predict disease miRNAs for various human diseases. It is useful in providing reliable disease miRNA candidates for subsequent experimental studies.</p><p>Methodology/Principal Findings</p><p>It is known that miRNAs with similar functions are often associated with similar diseases and vice versa. Therefore, the functional similarity of two miRNAs has been successfully estimated by measuring the semantic similarity of their associated diseases. To effectively predict disease miRNAs, we calculated the functional similarity by incorporating the information content of disease terms and phenotype similarity between diseases. Furthermore, the members of miRNA family or cluster are assigned higher weight since they are more probably associated with similar diseases. A new prediction method, HDMP, based on weighted <i>k</i> most similar neighbors is presented for predicting disease miRNAs. Experiments validated that HDMP achieved significantly higher prediction performance than existing methods. In addition, the case studies examining prostatic neoplasms, breast neoplasms, and lung neoplasms, showed that HDMP can uncover potential disease miRNA candidates.</p><p>Conclusions</p><p>The superior performance of HDMP can be attributed to the accurate measurement of miRNA functional similarity, the weight assignment based on miRNA family or cluster, and the effective prediction based on weighted <i>k</i> most similar neighbors. The online prediction and analysis tool is freely available at <a href="http://nclab.hit.edu.cn/hdmpred" target="_blank">http://nclab.hit.edu.cn/hdmpred</a>.</p></div