92 research outputs found
LOCSVMPSI: a web server for subcellular localization of eukaryotic proteins using SVM and profile of PSI-BLAST
Subcellular location of a protein is one of the key functional characters as proteins must be localized correctly at the subcellular level to have normal biological function. In this paper, a novel method named LOCSVMPSI has been introduced, which is based on the support vector machine (SVM) and the position-specific scoring matrix generated from profiles of PSI-BLAST. With a jackknife test on the RH2427 data set, LOCSVMPSI achieved a high overall prediction accuracy of 90.2%, which is higher than the prediction results by SubLoc and ESLpred on this data set. In addition, prediction performance of LOCSVMPSI was evaluated with 5-fold cross validation test on the PK7579 data set and the prediction results were consistently better than the previous method based on several SVMs using composition of both amino acids and amino acid pairs. Further test on the SWISSPROT new-unique data set showed that LOCSVMPSI also performed better than some widely used prediction methods, such as PSORTII, TargetP and LOCnet. All these results indicate that LOCSVMPSI is a powerful tool for the prediction of eukaryotic protein subcellular localization. An online web server (current version is 1.3) based on this method has been developed and is freely available to both academic and commercial users, which can be accessed by at
Research progress on the relationship between axonal transport dysfunction in neuronal cells and Alzheimer’s disease
Alzheimer’s disease is known as one of the “top ten killers in the world”. Due to lack of effective therapy at present, early pathological changes have captivated widespread attention. Axonal transport dysfunction has been reported as an early pathological feature of many neurodegenerative diseases. However, multiple factors can cause axonal transport dysfunction. In this article, the relationship between axonal transport dysfunction caused by kinesins, microtubules and mitochondria and Alzheimer’s disease was discussed, aiming to provide new ideas for the prevention and treatment of Alzheimer’s disease by in-depth study on axonal transport mechanism of neure
Super-tetragonal Sr4Al2O7: a versatile sacrificial layer for high-integrity freestanding oxide membranes
Releasing the epitaxial oxide heterostructures from substrate constraints
leads to the emergence of various correlated electronic phases and paves the
way for integrations with advanced semiconductor technologies. Identifying a
suitable water-soluble sacrificial layer, compatible with the high-quality
epitaxial growth of oxide heterostructures, is currently the key to the
development of large-scale freestanding oxide membranes. In this study, we
unveil the super-tetragonal Sr4Al2O7 (SAOT) as a promising water-soluble
sacrificial layer. The distinct low-symmetric crystal structure of SAOT enables
a superior capability to sustain epitaxial strain, thus allowing for broad
tunability in lattice constants. The resultant structural coherency and
defect-free interface in perovskite ABO3/SAOT heterostructures effectively
restrain crack formations during the water-assisted release of freestanding
oxide membranes. For a variety of non-ferroelectric oxide membranes, the
crack-free areas can span up to a few millimeters in length scale. These
compelling features, combined with the inherent high-water solubility, make
SAOT a versatile and feasible sacrificial layer for producing high-quality
freestanding oxide membranes, thereby boosting their potential for innovative
oxide electronics and flexible device designs.Comment: 5 figures and SI, it is the second version of this manuscrip
Clinical features and CKD-related quality of life in patients with CKD G3a and CKD G3b in China: results from the Chinese Cohort Study of Chronic Kidney Disease (C-STRIDE)
Aberrant Mucin5B expression in lung adenocarcinomas detected by iTRAQ labeling quantitative proteomics and immunohistochemistry
ksrMKL: a novel method for identification of kinase–substrate relationships using multiple kernel learning
Phosphorylation exerts a crucial role in multiple biological cellular processes which is catalyzed by protein kinases and closely related to many diseases. Identification of kinase–substrate relationships is important for understanding phosphorylation and provides a fundamental basis for further disease-related research and drug design. In this study, we develop a novel computational method to identify kinase–substrate relationships based on multiple kernel learning. The comparative analysis is based on a 10-fold cross-validation process and the dataset collected from the Phospho.ELM database. The results show that ksrMKL is greatly improved in various measures when compared with the single kernel support vector machine. Furthermore, with an independent test dataset extracted from the PhosphoSitePlus database, we compare ksrMKL with two existing kinase–substrate relationship prediction tools, namely iGPS and PKIS. The experimental results show that ksrMKL has better prediction performance than these existing tools
A Bipartite Network-based Method for Prediction of Long Non-coding RNA–protein Interactions
As one large class of non-coding RNAs (ncRNAs), long ncRNAs (lncRNAs) have gained considerable attention in recent years. Mutations and dysfunction of lncRNAs have been implicated in human disorders. Many lncRNAs exert their effects through interactions with the corresponding RNA-binding proteins. Several computational approaches have been developed, but only few are able to perform the prediction of these interactions from a network-based point of view. Here, we introduce a computational method named lncRNA–protein bipartite network inference (LPBNI). LPBNI aims to identify potential lncRNA–interacting proteins, by making full use of the known lncRNA–protein interactions. Leave-one-out cross validation (LOOCV) test shows that LPBNI significantly outperforms other network-based methods, including random walk (RWR) and protein-based collaborative filtering (ProCF). Furthermore, a case study was performed to demonstrate the performance of LPBNI using real data in predicting potential lncRNA–interacting proteins
Discovering mutated driver genes through a robust and sparse co-regularized matrix factorization framework with prior information from mRNA expression patterns and interaction network
Abstract Background Discovery of mutated driver genes is one of the primary objective for studying tumorigenesis. To discover some relatively low frequently mutated driver genes from somatic mutation data, many existing methods incorporate interaction network as prior information. However, the prior information of mRNA expression patterns are not exploited by these existing network-based methods, which is also proven to be highly informative of cancer progressions. Results To incorporate prior information from both interaction network and mRNA expressions, we propose a robust and sparse co-regularized nonnegative matrix factorization to discover driver genes from mutation data. Furthermore, our framework also conducts Frobenius norm regularization to overcome overfitting issue. Sparsity-inducing penalty is employed to obtain sparse scores in gene representations, of which the top scored genes are selected as driver candidates. Evaluation experiments by known benchmarking genes indicate that the performance of our method benefits from the two type of prior information. Our method also outperforms the existing network-based methods, and detect some driver genes that are not predicted by the competing methods. Conclusions In summary, our proposed method can improve the performance of driver gene discovery by effectively incorporating prior information from interaction network and mRNA expression patterns into a robust and sparse co-regularized matrix factorization framework
Prediction of post-translational modification sites using multiple kernel support vector machine
Protein post-translational modification (PTM) is an important mechanism that is involved in the regulation of protein function. Considering the high-cost and labor-intensive of experimental identification, many computational prediction methods are currently available for the prediction of PTM sites by using protein local sequence information in the context of conserved motif. Here we proposed a novel computational method by using the combination of multiple kernel support vector machines (SVM) for predicting PTM sites including phosphorylation, O-linked glycosylation, acetylation, sulfation and nitration. To largely make use of local sequence information and site-modification relationships, we developed a local sequence kernel and Gaussian interaction profile kernel, respectively. Multiple kernels were further combined to train SVM for efficiently leveraging kernel information to boost predictive performance. We compared the proposed method with existing PTM prediction methods. The experimental results revealed that the proposed method performed comparable or better performance than the existing prediction methods, suggesting the feasibility of the developed kernels and the usefulness of the proposed method in PTM sites prediction
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