Automatic extraction of protein-protein interactions using grammatical relationship graph.

BACKGROUND: Relationships between bio-entities (genes, proteins, diseases, etc.) constitute a significant part of our knowledge. Most of this information is documented as unstructured text in different forms, such as books, articles and on-line pages. Automatic extraction of such information and storing it in structured form could help researchers more easily access such information and also make it possible to incorporate it in advanced integrative analysis. In this study, we developed a novel approach to extract bio-entity relationships information using Nature Language Processing (NLP) and a graph-theoretic algorithm. METHODS: Our method, called GRGT (Grammatical Relationship Graph for Triplets), not only extracts the pairs of terms that have certain relationships, but also extracts the type of relationship (the word describing the relationships). In addition, the directionality of the relationship can also be extracted. Our method is based on the assumption that a triplet exists for a pair of interactions. A triplet is defined as two terms (entities) and an interaction word describing the relationship of the two terms in a sentence. We first use a sentence parsing tool to obtain the sentence structure represented as a dependency graph where words are nodes and edges are typed dependencies. The shortest paths among the pairs of words in the triplet are then extracted, which form the basis for our information extraction method. Flexible pattern matching scheme was then used to match a triplet graph with unknown relationship to those triplet graphs with labels (True or False) in the database. RESULTS: We applied the method on three benchmark datasets to extract the protein-protein-interactions (PPIs), and obtained better precision than the top performing methods in literature. CONCLUSIONS: We have developed a method to extract the protein-protein interactions from biomedical literature. PPIs extracted by our method have higher precision among other methods, suggesting that our method can be used to effectively extract PPIs and deposit them into databases. Beyond extracting PPIs, our method could be easily extended to extracting relationship information between other bio-entities.

RASS: a web server for RNA alignment in the joint sequence-structure space.

Comparison of ribonucleic acid (RNA) molecules is important for revealing their evolutionary relationships, predicting their functions and predicting their structures. Many methods have been developed for comparing RNAs using either sequence or three-dimensional (3D) structure (backbone geometry) information. Sequences and 3D structures contain non-overlapping sets of information that both determine RNA functions. When comparing RNA 3D structures, both types of information need to be taken into account. However, few methods compare RNA structures using both sequence and 3D structure information. Recently, we have developed a new method based on elastic shape analysis (ESA) that compares RNA molecules by combining both sequence and 3D structure information. ESA treats RNA structures as 3D curves with sequence information encoded on additional coordinates so that the alignment can be performed in the joint sequence-structure space. The similarity between two RNA molecules is quantified by a formal distance, geodesic distance. In this study, we implement a web server for the method, called RASS, to make it publicly available to research community. The web server is located at http://cloud.stat.fsu.edu/RASS/.

EQNAS: Evolutionary Quantum Neural Architecture Search for Image Classification.

Quantum neural network (QNN) is a neural network model based on the principles of quantum mechanics. The advantages of faster computing speed, higher memory capacity, smaller network size and elimination of catastrophic amnesia make it a new idea to solve the problem of training massive data that is difficult for classical neural networks. However, the quantum circuit of QNN are artificially designed with high circuit complexity and low precision in classification tasks. In this paper, a neural architecture search method EQNAS is proposed to improve QNN. First, initializing the quantum population after image quantum encoding. The next step is observing the quantum population and evaluating the fitness. The last is updating the quantum population. Quantum rotation gate update, quantum circuit construction and entirety interference crossover are specific operations. The last two steps need to be carried out iteratively until a satisfactory fitness is achieved. After a lot of experiments on the searched quantum neural networks, the feasibility and effectiveness of the algorithm proposed in this paper are proved, and the searched QNN is obviously better than the original algorithm. The classification accuracy on the mnist dataset and the warship dataset not only increased by 5.31% and 4.52%, respectively, but also reduced the parameters by 21.88% and 31.25% respectively. Code will be available at https://gitee.com/Pcyslist/models/tree/master/research/cv/EQNAS, and https://github.com/Pcyslist/EQNAS.

Central Venous Disease Increases the Risk of Microbial Colonization in Hemodialysis Catheters.

Objectives: Tunneled-cuffed catheters (TCCs) are widely used in maintenance hemodialysis patients. However, microbial colonization in catheters increases the likelihood of developing various complications, such as catheter-related infection (CRI), catheter failure, hospitalization, and death. Identification of the risk factors related to microorganism colonization may help us reduce the incidence of these adverse events. Therefore, a retrospective analysis of patients who underwent TCC removal was conducted. Methods: From a pool of 389 adult patients, 145 were selected for inclusion in the study. None of the patients met the diagnostic criteria for CRI within 30 days before recruitment. The right internal jugular vein was the unique route evaluated. The catheter removal procedure was guided by digital subtraction angiography. Catheter tips were collected for culture. Biochemical and clinical parameters were collected at the time of catheter removal. Results: The average age of this cohort was 55.46 ± 17.25 years. A total of 45/145 (31.03%) patients were verified to have a positive catheter culture. The proportions of gram-positive bacteria, gram-negative bacteria, and fungi were 57.8, 28.9, and 13.3%, respectively. History of CRI [odds ratio (OR) = 2.44, 95% confidence interval (CI) 1.09 to 5.49], fibrin sheath (OR = 2.93, 95% CI 1.39-6.19), white blood cell (WBC) count ≥5.9 × 109/l (OR = 2.31, 95% CI 1.12-4.77), moderate (OR = 4.87, 95% CI 1.61-14.78) or severe central venous stenosis (CVS) (OR = 4.74, 95% CI 1.16-19.38), and central venous thrombosis (CVT) (OR = 3.41, 95% CI 1.51-7.69) were associated with a significantly increased incidence of microbial colonization in a univariate analysis. Central venous disease (CVD) elevated the risk of microbial colonization, with an OR of 3.37 (1.47-7.71, P = 0.004). A multivariate analysis showed that both CVS and CVT were strongly associated with catheter microbial colonization, with ORs of 3.06 (1.20-7.78, P = 0.019) and 4.13 (1.21-14.05, P = 0.023), respectively. As the extent of stenosis increased, the relative risk of catheter microbial colonization also increased. In patients with moderate and severe stenosis, a sustained and significant increase in OR from 5.13 to 5.77 was observed. Conclusions: An elevated WBC count and CVD can put hemodialysis patients with TCCs at a higher risk of microbial colonization, even if these patients do not have the relevant symptoms of infection. Avoiding indwelling catheters is still the primary method for preventing CRI.