Predicting protein-protein interactions in unbalanced data using the primary structure of proteins

<p>Abstract</p> <p>Background</p> <p>Elucidating protein-protein interactions (PPIs) is essential to constructing protein interaction networks and facilitating our understanding of the general principles of biological systems. Previous studies have revealed that interacting protein pairs can be predicted by their primary structure. Most of these approaches have achieved satisfactory performance on datasets comprising equal number of interacting and non-interacting protein pairs. However, this ratio is highly unbalanced in nature, and these techniques have not been comprehensively evaluated with respect to the effect of the large number of non-interacting pairs in realistic datasets. Moreover, since highly unbalanced distributions usually lead to large datasets, more efficient predictors are desired when handling such challenging tasks.</p> <p>Results</p> <p>This study presents a method for PPI prediction based only on sequence information, which contributes in three aspects. First, we propose a probability-based mechanism for transforming protein sequences into feature vectors. Second, the proposed predictor is designed with an efficient classification algorithm, where the efficiency is essential for handling highly unbalanced datasets. Third, the proposed PPI predictor is assessed with several unbalanced datasets with different positive-to-negative ratios (from 1:1 to 1:15). This analysis provides solid evidence that the degree of dataset imbalance is important to PPI predictors.</p> <p>Conclusions</p> <p>Dealing with data imbalance is a key issue in PPI prediction since there are far fewer interacting protein pairs than non-interacting ones. This article provides a comprehensive study on this issue and develops a practical tool that achieves both good prediction performance and efficiency using only protein sequence information.</p

Understanding Mobile Apps Continuance Usage Behavior and Habit: An Expectance-Confirmation Theory

With the growing development of information technology and the wireless telecommunication network nowadays, mobile devices have been expanding rapidly and have been emerging as important tools for consumers. Using m-services and applications (apps) on mobile devices becomes custom in people’s daily lives. This study proposes a theoretical model to explore the continued usage behavior for smartphone. The objective of this study is to explore how perceived usefulness, perceived enjoyment, and confirmation influencing satisfaction and habit of consumers, and in turn influencing continued usage behavior, as well as the moderating effect of three characteristics of m-commerce. The proposed model will empirically be tested using survey method and collecting data from smartphone users in longitudinal setting. The structural equation modeling technique will be used to evaluate the causal model and confirmatory factor analysis will be performed to examine the reliability and validity of the measurement model. The findings of this study are expected to illustrate how factors influence individuals to use m-services and mobile apps and become a habit, as well as how these habits influence continued smartphone usage

The flow back tracing and DDoS defense mechanism of the TWAREN defender cloud

The TWAREN Defender Cloud is a distributed filter platform on thenetwork backbone to help defending our connecting institutions against maliciousnetwork attacks. By combining the security reports from participating schools, thissystem can block the incoming threats from the entry points, thus it helps protectingall connecting institutions in the most economic and effective way. This paper aimedat explaining the analyzer design, its mechanism to back trace DDoS attack flows totheir entry points and the defense mechanism it provides to block the threats

A new analysis tool for individual-level allele frequency for genomic studies

<p>Abstract</p> <p>Background</p> <p>Allele frequency is one of the most important population indices and has been broadly applied to genetic/genomic studies. Estimation of allele frequency using genotypes is convenient but may lose data information and be sensitive to genotyping errors.</p> <p>Results</p> <p>This study utilizes a unified intensity-measuring approach to estimating individual-level allele frequencies for 1,104 and 1,270 samples genotyped with the single-nucleotide-polymorphism arrays of the Affymetrix Human Mapping 100K and 500K Sets, respectively. Allele frequencies of all samples are estimated and adjusted by coefficients of preferential amplification/hybridization (CPA), and large ethnicity-specific and cross-ethnicity databases of CPA and allele frequency are established. The results show that using the CPA significantly improves the accuracy of allele frequency estimates; moreover, this paramount factor is insensitive to the time of data acquisition, effect of laboratory site, type of gene chip, and phenotypic status. Based on accurate allele frequency estimates, analytic methods based on individual-level allele frequencies are developed and successfully applied to discover genomic patterns of allele frequencies, detect chromosomal abnormalities, classify sample groups, identify outlier samples, and estimate the purity of tumor samples. The methods are packaged into a new analysis tool, ALOHA (<b>A</b>llele-frequency/<b>L</b>oss-<b>o</b>f-<b>h</b>eterozygosity/<b>A</b>llele-imbalance).</p> <p>Conclusions</p> <p>This is the first time that these important genetic/genomic applications have been simultaneously conducted by the analyses of individual-level allele frequencies estimated by a unified intensity-measuring approach. We expect that additional practical applications for allele frequency analysis will be found. The developed databases and tools provide useful resources for human genome analysis via high-throughput single-nucleotide-polymorphism arrays. The ALOHA software was written in R and R GUI and can be downloaded at <url>http://www.stat.sinica.edu.tw/hsinchou/genetics/aloha/ALOHA.htm</url>.</p

The potential impact of primary headache disorders on stroke risk

Distribution of PHDs. (DOC 55 kb

GolgiP: prediction of Golgi-resident proteins in plants

Summary: We present a novel Golgi-prediction server, GolgiP, for computational prediction of both membrane- and non-membrane-associated Golgi-resident proteins in plants. We have employed a support vector machine-based classification method for the prediction of such Golgi proteins, based on three types of information, dipeptide composition, transmembrane domain(s) (TMDs) and functional domain(s) of a protein, where the functional domain information is generated through searching against the Conserved Domains Database, and the TMD information includes the number of TMDs, the length of TMD and the number of TMDs at the N-terminus of a protein. Using GolgiP, we have made genome-scale predictions of Golgi-resident proteins in 18 plant genomes, and have made the preliminary analysis of the predicted data

Abnormalities of Hippocampal Subfield and Amygdalar Nuclei Volumes and Clinical Correlates in Behavioral Variant Frontotemporal Dementia with Obsessive–Compulsive Behavior—A Pilot Study

(1) Background: The hippocampus (HP) and amygdala are essential structures in obsessive–compulsive behavior (OCB); however, the specific role of the HP in patients with behavioral variant frontotemporal dementia (bvFTD) and OCB remains unclear. (2) Objective: We investigated the alterations of hippocampal and amygdalar volumes in patients with bvFTD and OCB and assessed the correlations of clinical severity with hippocampal subfield and amygdalar nuclei volumes in bvFTD patients with OCB. (3) Materials and methods: Eight bvFTD patients with OCB were recruited and compared with eight age- and sex-matched healthy controls (HCs). Hippocampal subfield and amygdalar nuclei volumes were analyzed automatically using a 3T magnetic resonance image and FreeSurfer v7.1.1. All participants completed the Yale–Brown Obsessive–Compulsive Scale (Y-BOCS), Neuropsychiatric Inventory (NPI), and Frontal Behavioral Inventory (FBI). (4) Results: We observed remarkable reductions in bilateral total hippocampal volumes. Compared with the HCs, reductions in the left hippocampal subfield volume over the cornu ammonis (CA)1 body, CA2/3 body, CA4 body, granule cell layer, and molecular layer of the dentate gyrus (GC-ML-DG) body, molecular layer of the HP body, and hippocampal tail were more obvious in patients with bvFTD and OCB. Right subfield volumes over the CA1 body and molecular layer of the HP body were more significantly reduced in bvFTD patients with OCB than in those in HCs. We observed no significant difference in amygdalar nuclei volume between the groups. Among patients with bvFTD and OCB, Y-BOCS score was negatively correlated with left CA2/3 body volume (τb = −0.729, p < 0.001); total NPI score was negatively correlated with left GC-ML-DG body (τb = −0.648, p = 0.001) and total bilateral hippocampal volumes (left, τb = −0.629, p = 0.002; right, τb = −0.455, p = 0.023); and FBI score was negatively correlated with the left molecular layer of the HP body (τb = −0.668, p = 0.001), CA4 body (τb = −0.610, p = 0.002), and hippocampal tail volumes (τb = −0.552, p < 0.006). Mediation analysis confirmed these subfield volumes as direct biomarkers for clinical severity, independent of medial and lateral orbitofrontal volumes. (5) Conclusions: Alterations in hippocampal subfield volumes appear to be crucial in the pathophysiology of OCB development in patients with bvFTD