Domain-Based Predictive Models for Protein-Protein Interaction Prediction

Protein interactions are of biological interest because they orchestrate a number of cellular processes such as metabolic pathways and immunological recognition. Recently, methods for predicting protein interactions using domain information are proposed and preliminary results have demonstrated their feasibility. In this paper, we develop two domain-based statistical models (neural networks and decision trees) for protein interaction predictions. Unlike most of the existing methods which consider only domain pairs (one domain from one protein) and assume that domain-domain interactions are independent of each other, the proposed methods are capable of exploring all possible interactions between domains and make predictions based on all the domains. Compared to maximum-likelihood estimation methods, our experimental results show that the proposed schemes can predict protein-protein interactions with higher specificity and sensitivity, while requiring less computation time. Furthermore, the decision tree-based model can be used to infer the interactions not only between two domains, but among multiple domains as well

Domain-Based Predictive Models for Protein-Protein Interaction Prediction

Protein interactions are of biological interest because they orchestrate a number of cellular processes such as metabolic pathways and immunological recognition. Recently, methods for predicting protein interactions using domain information are proposed and preliminary results have demonstrated their feasibility. In this paper, we develop two domain-based statistical models (neural networks and decision trees) for protein interaction predictions. Unlike most of the existing methods which consider only domain pairs (one domain from one protein) and assume that domain-domain interactions are independent of each other, the proposed methods are capable of exploring all possible interactions between domains and make predictions based on all the domains. Compared to maximum-likelihood estimation methods, our experimental results show that the proposed schemes can predict protein-protein interactions with higher specificity and sensitivity, while requiring less computation time. Furthermore, the decision tree-based model can be used to infer the interactions not only between two domains, but among multiple domains as well

Knowledge-guided inference of domain–domain interactions from incomplete protein–protein interaction networks

Motivation: Protein-protein interactions (PPIs), though extremely valuable towards a better understanding of protein functions and cellular processes, do not provide any direct information about the regions/domains within the proteins that mediate the interaction. Most often, it is only a fraction of a protein that directly interacts with its biological partners. Thus, understanding interaction at the domain level is a critical step towards (i) thorough understanding of PPI networks; (ii) precise identification of binding sites; (iii) acquisition of insights into the causes of deleterious mutations at interaction sites; and (iv) most importantly, development of drugs to inhibit pathological protein interactions. In addition, knowledge derived from known domain–domain interactions (DDIs) can be used to understand binding interfaces, which in turn can help discover unknown PPIs

Assessing reliability of protein-protein interactions by integrative analysis of data in model organisms

Background: Protein-protein interactions play vital roles in nearly all cellular processes and are involved in the construction of biological pathways such as metabolic and signal transduction pathways. Although large-scale experiments have enabled the discovery of thousands of previously unknown linkages among proteins in many organisms, the high-throughput interaction data is often associated with high error rates. Since protein interaction networks have been utilized in numerous biological inferences, the inclusive experimental errors inevitably affect the quality of such prediction. Thus, it is essential to assess the quality of the protein interaction data. Results: In this paper, a novel Bayesian network-based integrative framework is proposed to assess the reliability of protein-protein interactions. We develop a cross-species in silico model that assigns likelihood scores to individual protein pairs based on the information entirely extracted from model organisms. Our proposed approach integrates multiple microarray datasets and novel features derived from gene ontology. Furthermore, the confidence scores for cross-species protein mappings are explicitly incorporated into our model. Applying our model to predict protein interactions in the human genome, we are able to achieve 80% in sensitivity and 70% in specificity. Finally, we assess the overall quality of the experimentally determined yeast protein-protein interaction dataset. We observe that the more high-throughput experiments confirming an interaction, the higher the likelihood score, which confirms the effectiveness of our approach. Conclusion: This study demonstrates that model organisms certainly provide important information for protein-protein interaction inference and assessment. The proposed method is able to assess not only the overall quality of an interaction dataset, but also the quality of individual protein-protein interactions. We expect the method to continually improve as more high quality interaction data from more model organisms becomes available and is readily scalable to a genome-wide application

Assessing reliability of protein-protein interactions by integrative analysis of data in model organisms

BACKGROUND: Protein-protein interactions play vital roles in nearly all cellular processes and are involved in the construction of biological pathways such as metabolic and signal transduction pathways. Although large-scale experiments have enabled the discovery of thousands of previously unknown linkages among proteins in many organisms, the high-throughput interaction data is often associated with high error rates. Since protein interaction networks have been utilized in numerous biological inferences, the inclusive experimental errors inevitably affect the quality of such prediction. Thus, it is essential to assess the quality of the protein interaction data. RESULTS: In this paper, a novel Bayesian network-based integrative framework is proposed to assess the reliability of protein-protein interactions. We develop a cross-species in silico model that assigns likelihood scores to individual protein pairs based on the information entirely extracted from model organisms. Our proposed approach integrates multiple microarray datasets and novel features derived from gene ontology. Furthermore, the confidence scores for cross-species protein mappings are explicitly incorporated into our model. Applying our model to predict protein interactions in the human genome, we are able to achieve 80% in sensitivity and 70% in specificity. Finally, we assess the overall quality of the experimentally determined yeast protein-protein interaction dataset. We observe that the more high-throughput experiments confirming an interaction, the higher the likelihood score, which confirms the effectiveness of our approach. CONCLUSION: This study demonstrates that model organisms certainly provide important information for protein-protein interaction inference and assessment. The proposed method is able to assess not only the overall quality of an interaction dataset, but also the quality of individual protein-protein interactions. We expect the method to continually improve as more high quality interaction data from more model organisms becomes available and is readily scalable to a genome-wide application

Effect of intubation in patients with functional epiphora after endoscopic dacryocystorhinostomy

AIM: To investigate the effect of bicanalicular silicone tube intubation (BSTI) in the treatment of functional epiphora after endoscopic dacryocystorhinostomy (En-DCR). METHODS: Clinical data of 84 patients (95 eyes) with functional epiphora after En-DCR were retrospectively analyzed. Functional epiphora was confirmed as persistent or recurrent epiphora by fluorescein dye disappearance test (FDDT), lacrimal irrigation test, as well as endoscopic examination. Secondary BSTIs were recommended for patients with functional epiphora. These tubes were removed 1mo after surgery. Functional success and associated complications were assessed after 2y of follow-up. RESULTS: Seven patients (9 eyes) refused intervention, 5 patients (6 eyes) did not complete postoperative follow-up, and 1 patient (1 eye) developed tube prolapse within 1mo after surgery. Seventy-one patients (79 eyes) were included at last. Functional success ratios at six months, one year, as well as two years post-operation were 94.9% (75/79), 92.4% (73/79), and 91.1% (72/79), respectively. Three eyes presented with punctal slitting (2 eyes without epiphora), 1 eye with proximal canaliculus slitting, 1 eye with canaliculus stenosis and 4 eyes with still present functional epiphora without detectable abnormal at the last follow-up. CONCLUSION: Secondary intubation is an effective procedure with low recurrence probability for functional epiphora after En-DCR. Punctal and canaliculus injury are the main tube-associated complications after secondary intubation

Parallel Acceleration and Improvement of Gravitational Field Optimization Algorithm

The Gravitational Field Algorithm, a modern optimization algorithm, mainly simulates celestial mechanics and is derived from the Solar Nebular Disk Model (SNDM). It simulates the process of planetary formation to search for the optimal solution. Although this optimization algorithm has more advantages than other optimization algorithms in multi-peak optimization problems, it still has the shortcoming of long computation time when dealing with large-scale datasets or solving complex problems. Therefore, it is necessary to improve the efficiency of the Gravitational Field Algorithm (GFA). In this paper, an optimization method based on multi-population parallel is proposed to accelerate the Gravitational Field Algorithm. With the help of the parallel mechanism in MATLAB, the algorithm execution speed will be improved by using the parallel computing mode of multi-core CPU. In addition, this paper also improves the absorption operation strategy. By comparing the experimental results of eight classical unconstrained optimization problems, it is shown that the computational efficiency of this method is improved compared with the original Gravitational Field Algorithm, and the algorithm accuracy has also been slightly improved

1-Benzoyl-3-(5-quinol­yl)thio­urea

The title compound, C17H13N3OS, was obtained by the reaction of benzoyl chloride, ammonium thio­cyanate and 5-amino­quinoline in the presence of polyethyl­eneglycol-400 (PEG-400) as a phase-transfer catalyst. The compound crystallized as discrete mol­ecules linked by N—H⋯N and C—H⋯N hydrogen bonds involving all the potential donors, generating sheets parallel to (100). An intramolecular N—H⋯O bond is also present

Low temperature of radiofrequency ablation at the target sites can facilitate rapid progression of residual hepatic VX2 carcinoma

<p>Abstract</p> <p>Background</p> <p>Rapid progression of residual tumor after radiofrequency ablation (RFA) of hepatocellular carcinoma has been observed increasingly. However, its underlying mechanisms remain to be clarified. The present study was designed to determine whether low temperature of RFA at the target sites facilitates rapid progression of residual hepatic VX2 carcinoma and to clarify the possible underlying mechanisms.</p> <p>Methods</p> <p>The residual VX2 hepatoma model in rabbits was established by using RFA at 55, 70 and 85°C. Rabbits that were implanted with VX2 hepatoma but did not receive RFA acted as a control group. The relationship between rapid progression of residual hepatic VX2 carcinoma and low temperature of RFA at the target sites was carefully evaluated. A number of potential contributing molecular factors, such as proliferating cell nuclear antigen (PCNA), matrix metalloproteinase 9 (MMP-9), vascular endothelial growth factor (VEGF), hepatocyte growth factor (HGF) and Interleukin-6 (IL-6) were measured.</p> <p>Results</p> <p>The focal tumor volume and lung metastases of RFA-treated rabbits increased significantly compared with the control group (<it>P </it>< 0.05), and the greatest changes were seen in the 55°C group (<it>P </it>< 0.05). Expression of PCNA, MMP-9, VEGF, HGF and IL-6 in tumor tissues increased significantly in the RFA-treated groups compared with the control group, and of the increases were greatest in the 55°C group (<it>P </it>< 0.05). These results were consistent with gross pathological observation. Tumor re-inoculation experiments confirmed that low temperature of RFA at the target sites facilitated rapid progression of residual hepatic VX2 carcinoma.</p> <p>Conclusions</p> <p>Insufficient RFA that is caused by low temperature at the target sites could be an important cause of rapid progression of residual hepatic VX2 carcinoma. Residual hepatic VX2 carcinoma could facilitate its rapid progression through inducing overexpression of several molecular factors, such as PCNA, MMP-9, VEGF, HGF and IL-6.</p