A Novel Approach for Protein-Named Entity Recognition and Protein-Protein Interaction Extraction

Many researchers focus on developing protein-named entity recognition (Protein-NER) or PPI extraction systems. However, the studies about these two topics cannot be merged well; then existing PPI extraction systems’ Protein-NER still needs to improve. In this paper, we developed the protein-protein interaction extraction system named PPIMiner based on Support Vector Machine (SVM) and parsing tree. PPIMiner consists of three main models: natural language processing (NLP) model, Protein-NER model, and PPI discovery model. The Protein-NER model, which is named ProNER, identifies the protein names based on two methods: dictionary-based method and machine learning-based method. ProNER is capable of identifying more proteins than dictionary-based Protein-NER model in other existing systems. The final discovered PPIs extracted via PPI discovery model are represented in detail because we showed the protein interaction types and the occurrence frequency through two different methods. In the experiments, the result shows that the performances achieved by our ProNER and PPI discovery model are better than other existing tools. PPIMiner applied this protein-named entity recognition approach and parsing tree based PPI extraction method to improve the performance of PPI extraction. We also provide an easy-to-use interface to access PPIs database and an online system for PPIs extraction and Protein-NER

Identification of protein functions using a machine-learning approach based on sequence-derived properties

<p>Abstract</p> <p>Background</p> <p>Predicting the function of an unknown protein is an essential goal in bioinformatics. Sequence similarity-based approaches are widely used for function prediction; however, they are often inadequate in the absence of similar sequences or when the sequence similarity among known protein sequences is statistically weak. This study aimed to develop an accurate prediction method for identifying protein function, irrespective of sequence and structural similarities.</p> <p>Results</p> <p>A highly accurate prediction method capable of identifying protein function, based solely on protein sequence properties, is described. This method analyses and identifies specific features of the protein sequence that are highly correlated with certain protein functions and determines the combination of protein sequence features that best characterises protein function. Thirty-three features that represent subtle differences in local regions and full regions of the protein sequences were introduced. On the basis of 484 features extracted solely from the protein sequence, models were built to predict the functions of 11 different proteins from a broad range of cellular components, molecular functions, and biological processes. The accuracy of protein function prediction using random forests with feature selection ranged from 94.23% to 100%. The local sequence information was found to have a broad range of applicability in predicting protein function.</p> <p>Conclusion</p> <p>We present an accurate prediction method using a machine-learning approach based solely on protein sequence properties. The primary contribution of this paper is to propose new <it>PNPRD </it>features representing global and/or local differences in sequences, based on positively and/or negatively charged residues, to assist in predicting protein function. In addition, we identified a compact and useful feature subset for predicting the function of various proteins. Our results indicate that sequence-based classifiers can provide good results among a broad range of proteins, that the proposed features are useful in predicting several functions, and that the combination of our and traditional features may support the creation of a discriminative feature set for specific protein functions.</p

Abalone visceral extract inhibit tumor growth and metastasis by modulating Cox-2 levels and CD8+ T cell activity

<p>Abstract</p> <p>Background</p> <p>Abalone has long been used as a valuable food source in East Asian countries. Although the nutritional importance of abalone has been reported through <it>in vitro </it>and <it>in vivo </it>studies, there is little evidence about the potential anti-tumor effects of abalone visceral extract. The aim of the present study is to examine anti-tumor efficacy of abalone visceral extract and to elucidate its working mechanism.</p> <p>Methods</p> <p>In the present study, we used breast cancer model using BALB/c mouse-derived 4T1 mammary carcinoma and investigated the effect of abalone visceral extract on tumor development. Inhibitory effect against tumor metastasis was assessed by histopathology of lungs. Cox-2 productions by primary and secondary tumor were measured by real-time RT-PCR and immunoblotting (IB). Proliferation assay based on [³H]-thymidine incorporation and measurement of cytokines and effector molecules by RT-PCR were used to confirm tumor suppression efficacy of abalone visceral extract by modulating cytolytic CD8+ T cells. The cytotoxicity of CD8⁺T cell was compared by JAM test.</p> <p>Results</p> <p>Oral administration of abalone visceral extract reduced tumor growth (tumor volume and weight) and showed reduced metastasis as confirmed by decreased level of splenomegaly (spleen size and weight) and histological analysis of the lung metastasis (gross analysis and histological staining). Reduced expression of Cox-2 (mRNA and protein) from primary tumor and metastasized lung was also detected. In addition, treatment of abalone visceral extract increased anti-tumor activities of CD8⁺T cells by increasing the proliferation capacity and their cytolytic activity.</p> <p>Conclusions</p> <p>Our results suggest that abalone visceral extract has anti-tumor effects by suppressing tumor growth and lung metastasis through decreasing Cox-2 expression level as well as promoting proliferation and cytolytic function of CD8⁺T cells.</p

Ankle-Foot Orthosis Made by 3D Printing Technique and Automated Design Software

We described 3D printing technique and automated design software and clinical results after the application of this AFO to a patient with a foot drop. After acquiring a 3D modelling file of a patient’s lower leg with peroneal neuropathy by a 3D scanner, we loaded this file on the automated orthosis software and created the “STL” file. The designed AFO was printed using a fused filament fabrication type 3D printer, and a mechanical stress test was performed. The patient alternated between the 3D-printed and conventional AFOs for 2 months. There was no crack or damage, and the shape and stiffness of the AFO did not change after the durability test. The gait speed increased after wearing the conventional AFO (56.5 cm/sec) and 3D-printed AFO (56.5 cm/sec) compared to that without an AFO (42.2 cm/sec). The patient was more satisfied with the 3D-printed AFO than the conventional AFO in terms of the weight and ease of use. The 3D-printed AFO exhibited similar functionality as the conventional AFO and considerably satisfied the patient in terms of the weight and ease of use. We suggest the possibility of the individualized AFO with 3D printing techniques and automated design software

An Environmental Monitoring System for Managing Spatiotemporal Sensor Data over Sensor Networks

In a wireless sensor network, sensors collect data about natural phenomena and transmit them to a server in real-time. Many studies have been conducted focusing on the processing of continuous queries in an approximate form. However, this approach is difficult to apply to environmental applications which require the correct data to be stored. In this paper, we propose a weather monitoring system for handling and storing the sensor data stream in real-time in order to support continuous spatial and/or temporal queries. In our system, we exploit two time-based insertion methods to store the sensor data stream and reduce the number of managed tuples, without losing any of the raw data which are useful for queries, by using the sensors' temporal attributes. In addition, we offer a method for reducing the cost of the join operations used in processing spatiotemporal queries by filtering out a list of irrelevant sensors from query range before making a join operation. In the results of the performance evaluation, the number of tuples obtained from the data stream is reduced by about 30% in comparison to a naïve approach, thereby decreasing the query execution time