PupDB: a database of pupylated proteins

BACKGROUND: Prokaryotic ubiquitin-like protein (Pup), the firstly identified post-translational protein modifier in prokaryotes, is an important signal for the selective degradation of proteins. Recently, large-scale proteomics technology has been applied to identify a large number of pupylated proteins. The development of a database for managing pupylated proteins and pupylation sites is important for further analyses. DESCRIPTION: A database named PupDB is constructed by collecting experimentally identified pupylated proteins and pupylation sites from published studies and integrating the information of pupylated proteins with corresponding structures and functional annotations. PupDB is a web-based database with tools for browses and searches of pupylated proteins and interactive displays of protein structures and pupylation sites. CONCLUSIONS: The structured and searchable database PupDB is expected to provide a useful resource for further analyzing the substrate specificity, identifying pupylated proteins in other organisms and developing computational tools for predicting pupylation sites. PupDB is freely available at http://cwtung.kmu.edu.tw/pupdb

From Real to Complex: Enhancing Radio-based Activity Recognition Using Complex-Valued CSI

Activity recognition is an important component of many pervasive computing applications. Radio-based activity recognition has the advantage that it does not have the privacy concern and the subjects do not have to carry a device on them. Recently, it has been shown channel state information (CSI) can be used for activity recognition in a device-free setting. With the proliferation of wireless devices, it is important to understand how radio frequency interference (RFI) can impact on pervasive computing applications. In this paper, we investigate the impact of RFI on device-free CSI-based location-oriented activity recognition. We present data to show that RFI can have a significant impact on the CSI vectors. In the absence of RFI, different activities give rise to different CSI vectors that can be differentiated visually. However, in the presence of RFI, the CSI vectors become much noisier and activity recognition also becomes harder. Our extensive experiments show that the performance of state-of-the-art classification methods may degrade significantly with RFI. We then propose a number of counter measures to mitigate the impact of RFI and improve the location-oriented activity recognition performance. We are also the first to use complex-valued CSI to improve the performance in the environment with RFI

An Integrated Web-based System for MEDLINE Analysis: A Case Study of Chronic Kidney Disease

In the era of big data, medical researchers attempt to utilize some analysis techniques like machine learning and text mining on their large-scale corpora to save valuable labor work and time. Consequently, many data analysis platforms are built to support medical professionals such as Pubtator, GeneWays, BioContext, etc. These platforms are helpful to medical entities recognition and relation extraction, but there is not an integrated platform to support researchers’ various needs, and medical projects are isolated from each other, which is hard to be shared and reused. As a result, we present an integrated system containing ‘name entity recognition’, ‘document categorization’ and ‘association extraction’. Besides, we add the concept of ‘socialization’ making projects reusable for further analyses. A case study of chronic kidney disease was adopted to indicate the effectiveness of the proposed system

Distributed Training Large-Scale Deep Architectures

Scale of data and scale of computation infrastructures together enable the current deep learning renaissance. However, training large-scale deep architectures demands both algorithmic improvement and careful system configuration. In this paper, we focus on employing the system approach to speed up large-scale training. Via lessons learned from our routine benchmarking effort, we first identify bottlenecks and overheads that hinter data parallelism. We then devise guidelines that help practitioners to configure an effective system and fine-tune parameters to achieve desired speedup. Specifically, we develop a procedure for setting minibatch size and choosing computation algorithms. We also derive lemmas for determining the quantity of key components such as the number of GPUs and parameter servers. Experiments and examples show that these guidelines help effectively speed up large-scale deep learning training

POPISK: T-cell reactivity prediction using support vector machines and string kernels

BACKGROUND: Accurate prediction of peptide immunogenicity and characterization of relation between peptide sequences and peptide immunogenicity will be greatly helpful for vaccine designs and understanding of the immune system. In contrast to the prediction of antigen processing and presentation pathway, the prediction of subsequent T-cell reactivity is a much harder topic. Previous studies of identifying T-cell receptor (TCR) recognition positions were based on small-scale analyses using only a few peptides and concluded different recognition positions such as positions 4, 6 and 8 of peptides with length 9. Large-scale analyses are necessary to better characterize the effect of peptide sequence variations on T-cell reactivity and design predictors of a peptide's T-cell reactivity (and thus immunogenicity). The identification and characterization of important positions influencing T-cell reactivity will provide insights into the underlying mechanism of immunogenicity. RESULTS: This work establishes a large dataset by collecting immunogenicity data from three major immunology databases. In order to consider the effect of MHC restriction, peptides are classified by their associated MHC alleles. Subsequently, a computational method (named POPISK) using support vector machine with a weighted degree string kernel is proposed to predict T-cell reactivity and identify important recognition positions. POPISK yields a mean 10-fold cross-validation accuracy of 68% in predicting T-cell reactivity of HLA-A2-binding peptides. POPISK is capable of predicting immunogenicity with scores that can also correctly predict the change in T-cell reactivity related to point mutations in epitopes reported in previous studies using crystal structures. Thorough analyses of the prediction results identify the important positions 4, 6, 8 and 9, and yield insights into the molecular basis for TCR recognition. Finally, we relate this finding to physicochemical properties and structural features of the MHC-peptide-TCR interaction. CONCLUSIONS: A computational method POPISK is proposed to predict immunogenicity with scores which are useful for predicting immunogenicity changes made by single-residue modifications. The web server of POPISK is freely available at http://iclab.life.nctu.edu.tw/POPISK

Application of Rat In Situ Single-pass Intestinal Perfusion in the Evaluation of Presystemic Extraction of Indinavir Under Different Perfusion Rates

Background/PurposeFirst-pass effect has been an important concern for oral pharmaceuticals. An in vivo system was developed for measuring different concentrations of pharmaceuticals in the portal vein and hepatic vein (via the inferior vena cava) for delineating presystemic metabolism under different perfusion rates by using indinavir as an exemplary agent.MethodsAn in situ single-pass intestinal perfusion technique was modified from previous studies to concomitantly obtain portal and hepatic venous bloods. Portal and hepatic venous samples were simultaneously taken from rats at appropriate time points using the perfusion model of 1 mg/mL indinavir at flow rates of 0.05, 0.1, 0.5 and 1.0 mL/min. The indinavir concentrations were assayed by binary-gradient high-pressure liquid chromatography with UV detection.ResultsThe mean indinavir concentrations in portal vein concentration−time profiles at different perfusion times under various flow rates were all higher than those obtained for hepatic veins. At flow rates of 0.5 and 1.0 mL/min, in particular, the area under the curve (AUC) and maximal concentration (C max) of indinavir absorption were significantly different between portal veins and hepatic veins (p < 0.05), indicating considerable hepatic involvement in the presystemic extraction of indinavir. The system also has potential for use when estimating the hepatic extraction ratio (E H) and hepatic clearance (Cl H).ConclusionThis in vivo approach could provide another useful tool for improving our basic understanding of the absorption kinetics and hepatic metabolism of pharmaceuticals under development and facilitating the clinical application of such