BackgroundOne of the best and most accurate methods for identifying disease-causing genes is monitoring gene expression values in different samples using microarray technology. One of the shortcomings of microarray data is that they provide a small quantity of samples with respect to the number of genes. This problem reduces the classification accuracy of the methods, so gene selection is essential to improve the predictive accuracy and to identify potential marker genes for a disease. Among numerous existing methods for gene selection, support vector machine-based recursive feature elimination (SVMRFE) has become one of the leading methods, but its performance can be reduced because of the small sample size, noisy data and the fact that the method does not remove redundant genes.MethodsWe propose a novel framework for gene selection which uses the advantageous features of conventional methods and addresses their weaknesses. In fact, we have combined the Fisher method and SVMRFE to utilize the advantages of a filtering method as well as an embedded method. Furthermore, we have added a redundancy reduction stage to address the weakness of the Fisher method and SVMRFE. In addition to gene expression values, the proposed method uses Gene Ontology which is a reliable source of information on genes. The use of Gene Ontology can compensate, in part, for the limitations of microarrays, such as having a small number of samples and erroneous measurement results.ResultsThe proposed method has been applied to colon, Diffuse Large B-Cell Lymphoma (DLBCL) and prostate cancer datasets. The empirical results show that our method has improved classification performance in terms of accuracy, sensitivity and specificity. In addition, the study of the molecular function of selected genes strengthened the hypothesis that these genes are involved in the process of cancer growth.ConclusionsThe proposed method addresses the weakness of conventional methods by adding a redundancy reduction stage and utilizing Gene Ontology information. It predicts marker genes for colon, DLBCL and prostate cancer with a high accuracy. The predictions made in this study can serve as a list of candidates for subsequent wet-lab verification and might help in the search for a cure for cancers.
Apolipoprotein E (apoE) binds the amyloid β peptide (Aβ), one of the major culprits in Alzheimer's disease development. The formation of apoE:Aβ complexes is implicated in both Aβ clearance and fibrillization. However, the binding interface between apoE and Aβ is poorly defined despite substantial previous research efforts, and the exact role of apoE in the pathology of Alzheimer's disease remains largely elusive. Here, we compared the three main isoforms of apoE (E2, E3, and E4) for their interaction with Aβ1-42 in an early stage of aggregation and at near physiological conditions. Using electron microscopy and Western blots, we showed that all three isoforms are able to prevent Aβ fibrillization and form a noncovalent complex, with one molecule of Aβ bound per apoE. Using chemical cross-linking coupled to mass spectrometry, we further examined the interface of interaction between apoE2/3/4 and Aβ. Multiple high-confidence intermolecular apoE2/3/4:Aβ cross-links confirmed that Lys16 is located in the region of Aβ binding to apoE2/3/4. Further, we demonstrated that both N- and C-terminal domains of apoE2/3/4 are interacting with Aβ. The cross-linked sites were mapped onto and evaluated in light of a recent structure of apoE. Our results support binding of the hydrophobic Aβ at the apoE domain-domain interaction interface, which would explain how apoE is able to stabilize Aβ and thereby prevent its subsequent aggregation.
One of the fundamental issues in social networks is the influence maximization problem, where the goal is to identify a small subset of individuals such that they can trigger the largest number of members in the network. In real-world social networks, the propagation of information from a node to another may incur a certain amount of time delay; moreover, the value of information may decrease over time. So not only the coverage size, but also the propagation speed matters. In this paper, we propose the Time-Sensitive Influence Maximization (TSIM) problem, which takes into account the time dependence of the information value. Considering the time delay aspect, we develop two diffusion models, namely the Delayed Independent Cascade model and the Delayed Linear Threshold model. We show that the TSIM problem is NP-hard under these models but the spread function is monotone and submodular. Thus, a greedy approximation algorithm can achieve a 1-1/e approximation ratio. Moreover, we propose two time-sensitive centrality measures and compare their performance to the greedy algorithm. We evaluate our methods on four real-world datasets. Experimental results show that the proposed algorithms outperform existing methods, which ignore the decay of information value over time.
Carboxyl-group specific chemical cross-linking is gaining an increased interest as a structural mass spectrometry/structural proteomics technique that is complementary to the more commonly used amine-specific chemistry using succinimide esters. One of these protocols uses a combination of dihydrazide linkers and the coupling reagent DMTMM [4-(4,6-dimethoxy-1,3,5-triazin-2-yl)-4-methylmorpholinium] chloride, which allows performing the reaction at neutral pH. The reaction yields two types of products, carboxyl–carboxyl cross-links that incorporate the dihydrazide linker and zero-length carboxyl-amine cross-links induced by DMTMM alone. Until now, it has not been systematically investigated how the balance between the two products is affected by experimental conditions. Here, we studied the role of the ratios of the two reagents (using pimelic dihydrazide and DMTMM) and demonstrate that the concentration of the two reagents can be systematically adjusted to favor one reaction product over the other. Using a set of five model proteins, we observed that the number of identified cross-linked peptides could be more than doubled by a combination of three different reaction conditions. We also applied this strategy to the bovine 20S proteasome and the Escherichia coli 70S ribosome, again demonstrating complementarity and increased cross-link coverage.
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