A novel dysregulated pathway-identification analysis based on global influence of within-pathway effects and crosstalk between pathways

Identifying dysregulated pathways from high-throughput experimental data in order to infer underlying biological insights is an important task. Current pathway-identification methods focus on single pathways in isolation; however, consideration of crosstalk between pathways could improve our understanding of alterations in biological states. We propose a novel method of pathway analysis based on global influence (PAGI) to identify dysregulated pathways, by considering both within-pathway effects and crosstalk between pathways. We constructed a global gene–gene network based on the relationships among genes extracted from a pathway database. We then evaluated the extent of differential expression for each gene, and mapped them to the global network. The random walk with restart algorithm was used to calculate the extent of genes affected by global influence. Finally, we used cumulative distribution functions to determine the significance values of the dysregulated pathways. We applied the PAGI method to five cancer microarray datasets, and compared our results with gene set enrichment analysis and five other methods. Based on these analyses, we demonstrated that PAGI can effectively identify dysregulated pathways associated with cancer, with strong reproducibility and robustness. We implemented PAGI using the freely available R-based and Web-based tools (http://bioinfo.hrbmu.edu.cn/PAGI).     

Constructing and characterizing a bioactive small molecule and microRNA association network for Alzheimer's disease

Alzheimer''s disease (AD) is an incurable neurodegenerative disorder. Much effort has been devoted to developing effective therapeutic agents. Recently, targeting microRNAs (miRNAs) with small molecules has become a novel therapy for human diseases. In this study, we present a systematic computational approach to construct a bioactive Small molecule and miRNA association Network in AD (SmiRN-AD), which is based on the gene expression signatures of bioactive small molecule perturbation and AD-related miRNA regulation. We also performed topological and functional analysis of the SmiRN-AD from multiple perspectives. At the significance level of p ≤ 0.01, 496 small molecule–miRNA associations, including 25 AD-related miRNAs and 275 small molecules, were recognized and used to construct the SmiRN-AD. The drugs that were connected with the same miRNA tended to share common drug targets (p = 1.72 × 10⁻⁴) and belong to the same therapeutic category (p = 4.22 × 10⁻⁸). The miRNAs that were linked to the same small molecule regulated more common miRNA targets (p = 6.07 × 10⁻³). Further analysis of the positive connections (quinostatin and miR-148b, amantadine and miR-15a) and the negative connections (melatonin and miR-30e-5p) indicated that our large-scale predictions afforded specific biological insights into AD pathogenesis and therapy. This study proposes a holistic strategy for deciphering the associations between small molecules and miRNAs in AD, which may be helpful for developing a novel effective miRNA-associated therapeutic strategy for AD. A comprehensive database for the SmiRN-AD and the differential expression patterns of the miRNA targets in AD is freely available at http://bioinfo.hrbmu.edu.cn/SmiRN-AD/.     

Open data and open code for big science of science studies

Historically, science of science (Sci2) studies have been performed by single investigators or small teams. As the size and complexity of data sets and analyses scales up, a “Big Science” approach (Price, Little science, big science, 1963) is required that exploits the expertise and resources of interdisciplinary teams spanning academic, government, and industry boundaries. Big Sci2 studies utilize “big data”, i.e., large, complex, diverse, longitudinal, and/or distributed datasets that might be owned by different stakeholders. They apply a systems science approach to uncover hidden patterns, bursts of activity, correlations, and laws. They make available open data and open code in support of replication of results, iterative refinement of approaches and tools, and education. This paper introduces a database-tool infrastructure that was designed to support big Sci2 studies. The open access Scholarly Database (http://sdb.cns.iu.edu) provides easy access to 26 million paper, patent, grant, and clinical trial records. The open source Sci2 tool (http://sci2.cns.iu.edu) supports temporal, geospatial, topical, and network studies. The scalability of the infrastructure is examined. Results show that temporal analyses scale linearly with the number of records and file size, while the geospatial algorithm showed quadratic growth. The number of edges rather than nodes determined performance for network based algorithms.     

Inferring phase diagrams from X-ray data with background signals using graph segmentation

Automated composition-structure-processing phase diagram creation is critical for high-throughput experimental material studies. In particular, diffractogram datasets with large background signals are especially difficult to identify the phase regions. In this work, we proposed a novel graph segmentation algorithm from computer vision to solve the phase diagram prediction problem from X-ray diffraction data with large background signals. We introduced a novel background subtraction algorithm with graph-based clustering/segmentation to build the BGPhase algorithm. Experiments on three datasets with the Al–Cu–Mo material family showed that our phase attribution algorithm can achieve high prediction accuracy ranging from 88.6 to 94.8% or with MCC scores ranging from 0.715 to 0.890. The algorithm can be accessed online at http://mleg.cse.sc.edu/bgphase.     

PepHMM: a hidden Markov model based scoring function for mass spectrometry database search

An accurate scoring function for database search is crucial for peptide identification using tandem mass spectrometry. Although many mathematical models have been proposed to score peptides against tandem mass spectra, our method (called PepHMM, http://msms.cmb.usc.edu) is unique in that it combines information on machine accuracy, mass peak intensity, and correlation among ions into a hidden Markov model (HMM). In addition, we develop a method to calculate statistical significance of the HMM scores. We implement the method and test them on two sets of experimental data generated by two different types of mass spectrometers and compare the results with MASCOT and SEQUEST under the same condition. One experimental results show that PepHMM has a much higher accuracy (with 6.5% error rate) than MASCOT (with 17.4% error rate), and the other experimental results show that PepHMM identifies 43 and 31% more correct spectra than SEQUEST and MASCOT, respectively.     

A two-stage method for O-glycosylation site prediction

Correctly predicting the site of O-glycosylation will greatly benefit the search and design of new specific and efficient GalNAc-transferase inhibitors. In this article, the site of O-glycosylation was studied using the correlation-based feature subset (CfsSubset) selection method combined with a wrapper method. Twenty-three important biochemical features were found based on a jackknife test from original data set containing 4779 features. By using the AdaBoost method with the twenty-three selected features, the prediction model yields an accuracy rate of 88.1% for the jackknife test and 87.5% for an independent set test, with increased accuracy over the original dataset by 8.5% and 10.42%, respectively. It is expected that our feature selection scheme can be referred to as a useful assistant technique for finding effective competitive inhibitors of GalNAc-transferase. An online predictor based on this research is available at http://chemdata.shu.edu.cn/gal_p/.     

The effect of ferrous ions,calcium ions and citric acid on absorption of ciprofloxacin across caco-2 cells: practical and structural approach

Objective: To study the potential influence of selected metal ions on absorption (and hence oral bioavailability of ciprofloxacin (Cipro) in presence and absence of a competing ligand.

Significance: The presence of metal ions together with Cipro results in complexes exhibiting a decreased bioavailability. Attempts were made to better understand the mechanism of decreased Cipro bioavailability in the presence of metals such as calcium and ferrous ions, and a small-sized ligand citric acid (CitA).

Methods: Effect of complex size or other potential factors was studied using diffusion through synthetic membrane, permeation studies across Caco-2 cells and capillary electrophoresis. A molecular dynamics (MD) simulation study was conducted to find the arrangement and the nature of the interactions between Cipro molecules and ferrous ions.

Results: Cipro was shown to form complexes with metals and CitA. The presence of CitA improved permeation of Cipro through the synthetic membrane but this was not as obvious in case of Caco-2 cells. Capillary electrophoresis suggested the existence of large molecular aggregates of Cipro: metal complexes. MD simulations offered clear evidence of large size aggregates in line with the experimental findings. CitA alone significantly improved permeation of Cipro through Caco-2 cells.

Conclusions: The size of the formed complexes, rather than the decrease in the solubility of formed complexes, plays a significant role in permeation (absorption) of Cipro. CitA might ameliorate the effect of co-administered metal ions on the bioavailability of Cipro.     

SOMRuler: a novel interpretable transmembrane helices predictor

Transmembrane helices (TMH) identification is one of the most important steps in membrane protein structure prediction. Existing TMH predictors tend to pursue accurate computational models without carefully considering the interpretability of these models and thus act as a black box. In this paper, a novel TMH predictor called SOMRuler with excellent interpretability while possessing high prediction accuracy is presented. The SOMRuler uses a self-organizing map (SOM) to learn helices distribution knowledge, which is encoded in the codebook vectors of the trained SOM, from the training samples. Human interpretable fuzzy rules are then extracted from the codebook vectors of the trained SOM. By extracting fuzzy rules from the learned knowledge rather than the original training samples, on the one hand, the computational burden of extracting fuzzy rules can be greatly reduced; on the other hand, the reliability of the extracted rules can also be enhanced since noise contained in the original samples can be smoothened by the learning procedure of SOM. The validity of the fuzzy rules extracted by SOMRuler is qualitatively and quantitatively analyzed. Experimental results on the benchmark dataset show that the SOMRuler outperforms most existing popular TMH predictors and is flexible to suite for a wide variety of problems in bioinformatics. The SOMRuler software is implemented by Java and Matlab and is available for academic use at: http://www.csbio.sjtu.edu.cn/bioinf/SOMRuler/.     

Rete-netzwerk-red: analyzing and visualizing scholarly networks using the Network Workbench Tool

The enormous increase in digital scholarly data and computing power combined with recent advances in text mining, linguistics, network science, and scientometrics make it possible to scientifically study the structure and evolution of science on a large scale. This paper discusses the challenges of this ‘BIG science of science’—also called ‘computational scientometrics’ research—in terms of data access, algorithm scalability, repeatability, as well as result communication and interpretation. It then introduces two infrastructures: (1) the Scholarly Database (SDB) (http://sdb.slis.indiana.edu), which provides free online access to 22 million scholarly records—papers, patents, and funding awards which can be cross-searched and downloaded as dumps, and (2) Scientometrics-relevant plug-ins of the open-source Network Workbench (NWB) Tool (http://nwb.slis.indiana.edu). The utility of these infrastructures is then exemplarily demonstrated in three studies: a comparison of the funding portfolios and co-investigator networks of different universities, an examination of paper-citation and co-author networks of major network science researchers, and an analysis of topic bursts in streams of text. The article concludes with a discussion of related work that aims to provide practically useful and theoretically grounded cyberinfrastructure in support of computational scientometrics research, education and practice.     

Constrained multi-objective optimization algorithm with an ensemble of constraint handling methods

Different constraint handling techniques have been used with multi-objective evolutionary algorithms (MOEA) to solve constrained multi-objective optimization problems. It is impossible for a single constraint handling technique to outperform all other constraint handling techniques always on every problem irrespective of the exhaustiveness of the parameter tuning. To overcome this selection problem, an ensemble of constraint handling methods (ECHM) is used to tackle constrained multi-objective optimization problems. The ECHM is integrated with a multi-objective differential evolution (MODE) algorithm. The performance is compared between the ECHM and the same single constraint handling methods using the same MODE (using codes available from http://www3.ntu.edu.sg/home/EPNSugan/index.htm). The results show that ECHM overall outperforms the single constraint handling methods.     

An efficient semi-unsupervised gene selection method via spectral biclustering

Gene selection is an important issue in microarray data processing. In this paper, we propose an efficient method for selecting relevant genes. First, we use spectral biclustering to obtain the best two eigenvectors for class partition. Then gene combinations are selected based on the similarity between the genes and the best eigenvectors. We demonstrate our semi-unsupervised gene selection method using two microarray cancer data sets, i.e., the lymphoma and the liver cancer data sets, where our method is able to identify a single gene or a two-gene combinations which can lead to predictions with very high accuracy.     

Update of line parameters of ozone in the 2550-2900 cm(-1) region

An update of spectroscopic line parameters for the 3.45-3.92 microm ozone bands is reported. The line list includes the parameters of 15 bands of the main isotopic species and of the v1+v2+v3 band of 16O16O18O and 16O18O16O. The results are based on previous high resolution laboratory studies. Comparisons of experimental spectra with an absorptance simulation of ozone based on the reported line list shows that the latter one is accurate enough for strong, medium, and weak transmittance in the 2550-2900 cm(-1) spectral range. The data are available on the Web in the Spectroscopy and Molecular Properties of Ozone (S&MPO, http://smpo.iao.ru and http://ozone.univ-reims.fr) and HITRAN (http://cfa-www.harvard.edu/hitran/) databanks.     

A simple tool for two-dimensional quantification of filler dispersion: A proposal

An effective filler dispersion within a polymeric matrix is known to be a crucial requirement for obtaining the desired performance from a composite. A quantification of this parameter cannot be always easily achieved and several methodologies have been proposed so far, generally based on complex image processing algorithms. However, no standard has been established yet.

In order to contribute to the quantitative evaluation of the filler dispersion in a polymeric matrix, a novel straightforward two-dimensional alternative method based on the analysis of electron microscopy micrographs is here proposed. Several case studies were considered to test the reliability of the approach, providing as resulting output, either a qualitative polar plot representative of the analyzed image and an index to measure the filler dispersion.     

Tensor methods for parameter estimation and bifurcation analysis of stochastic reaction networks

Stochastic modelling of gene regulatory networks provides an indispensable tool for understanding how random events at the molecular level influence cellular functions. A common challenge of stochastic models is to calibrate a large number of model parameters against the experimental data. Another difficulty is to study how the behaviour of a stochastic model depends on its parameters, i.e. whether a change in model parameters can lead to a significant qualitative change in model behaviour (bifurcation). In this paper, tensor-structured parametric analysis (TPA) is developed to address these computational challenges. It is based on recently proposed low-parametric tensor-structured representations of classical matrices and vectors. This approach enables simultaneous computation of the model properties for all parameter values within a parameter space. The TPA is illustrated by studying the parameter estimation, robustness, sensitivity and bifurcation structure in stochastic models of biochemical networks. A Matlab implementation of the TPA is available at http://www.stobifan.org.     

XCMS: processing mass spectrometry data for metabolite profiling using nonlinear peak alignment, matching, and identification

Metabolite profiling in biomarker discovery, enzyme substrate assignment, drug activity/specificity determination, and basic metabolic research requires new data preprocessing approaches to correlate specific metabolites to their biological origin. Here we introduce an LC/MS-based data analysis approach, XCMS, which incorporates novel nonlinear retention time alignment, matched filtration, peak detection, and peak matching. Without using internal standards, the method dynamically identifies hundreds of endogenous metabolites for use as standards, calculating a nonlinear retention time correction profile for each sample. Following retention time correction, the relative metabolite ion intensities are directly compared to identify changes in specific endogenous metabolites, such as potential biomarkers. The software is demonstrated using data sets from a previously reported enzyme knockout study and a large-scale study of plasma samples. XCMS is freely available under an open-source license at http://metlin.scripps.edu/download/.     

根据基因表达谱与基因功能模块研究大鼠抑郁症发病的分子机制

用Willner法构造大鼠抑郁症模型,由基因芯片检测8例模型和8例正常大鼠的基因表达谱,识别差异表达基因,寻找差异表达基因功能模块并构建基因表达相关网络,研究大鼠抑郁症模型的分子病理机制.在基因本体(GO)功能体系中寻找显著富集差异表达基因的疾病相关功能模块,提取疾病相关功能模块中的基因构造表达相关网络.从中选择显著多地与其它基因共表达的基因定义为HUB基因,并进一步研究其与疾病的关系.筛选得到207个差异表达基因及13个差异表达基因功能模块,主要涉及神经肽激素活性、信号传导通路、核糖体生成以及蛋白质转运与降解.在共表达相关网络中进一步识别了4个差异表达的HUB基因.利用基因功能模块和表达相关网络研究疾病机制的结果显示,抑郁症的发病可能涉及单胺递质的释放、信号传导以及神经肽激素调节等通路协同作用.     

Identifying genuine protein–protein interactions within communities of gene co‐expression networks using a deconvolution method

Direct relationships between biological molecules connected in a gene co‐expression network tend to reflect real biological activities such as gene regulation, protein–protein interactions (PPIs), and metabolisation. As correlation‐based networks contain numerous indirect connections, those direct relationships are always ‘hidden’ in them. Compared with the global network, network communities imply more biological significance on predicting protein function, detecting protein complexes and studying network evolution. Therefore, identifying direct relationships in communities is a pervasive and important topic in the biological sciences. Unfortunately, this field has not been well studied. A major thrust of this study is to apply a deconvolution algorithm on communities stemming from different gene co‐expression networks, which are constructed by fixing different thresholds for robustness analysis. Using the fifth Dialogue on Reverse Engineering Assessment and Methods challenge (DREAM5) framework, the authors demonstrate that nearly all new communities extracted from a ‘deconvolution filter’ contain more genuine PPIs than before deconvolution.Inspec keywords: proteins, deconvolution, genetics, bioinformatics, biology computing, molecular biophysicsOther keywords: identifying genuine protein–protein interactions, gene co‐expression network, deconvolution method, direct relationships, biological molecules, biological activities, gene regulation, correlation‐based networks, numerous indirect connections, global network, network communities, biological significance, protein function, protein complexes, studying network evolution, biological sciences, different gene co‐expression networks     

Preparation and evaluation of posaconazole-loaded enteric microparticles in rats

Objectives: Posaconazole (POS) is an antifungal compound which has a low oral bioavailability. The aim of this study was to prepare POS enteric microparticles to enhance its oral bioavailability.

Methods: POS enteric microparticles were prepared with hypromellose acetate succinate (HPMCAS) via the spray drying method. The solvent mixtures of acetone and ethanol used in the preparation of the microparticles were optimized to produce the ideal POS enteric microparticles. Multivariate data analysis using a principal component analysis (PCA) was used to find the relationship among the HPMCAS molecular characteristics, particle properties and drug release kinetics from the spray dried microparticles.

Key findings: The optimal spray solvent mixtures were critical to produce the POS microparticles with the defined polymer entanglement index, drug surface enrichment, particle size and drug loading. The HPMCAS molecular characteristics affected the microscopic connectivity and diffusivity of polymer matrix and eventually influenced the drug release behavior, and enhanced the bioavailability of POS.

Conclusions: These studies suggested that the selection of suitable solvent mixtures of acetone and ethanol used in the spray drying of the microparticles was quite important to produce the entangled polymer structures with preferred polymer molecular properties of polymer coiling, overlap concentration and entanglement index. Additional studies on particle size and surface drug enrichment eventually produced HPMCAS-based enteric microparticles to enhance the oral bioavailability of POS.     

Functional classification of secreted proteins by position specific scoring matrix and auto covariance

Secreted proteins play pivotal biological regulatory roles in eukaryotic cells with different functions, and have the potential for disease biomarkers and protein therapeutics. Furthermore, the comprehension of their functions is not only indispensable for helping genome annotation, but also a supplement to the existing methods. As the rapid increase of protein sequences generated in the post-genomic age, it is urgent to develop a computational method to effectively annotate the function types of numerous newly discovered secreted proteins. In view of this, a support vector machine (SVM)-based predictor is proposed to classify secreted proteins with different functions in this paper, including cytokine, hormone, immune system protein, protease, and protease inhibitor. Here, proteins are represented by position specific scoring matrix (PSSM) and auto covariance (AC), which incorporates the evolution and the sequence-order information of proteins. When distinguishing the five types of secreted proteins, an accuracy of 83.2%, 88.9%, 86.1%, 90.9%, and 90.6% is achieved for cytokine, hormone, immune system protein, protease and protease inhibitor, respectively. Particularly, when performed on an independent test set of 325 proteins, the method also yielded a satisfactory accuracy of 91.2%. It shows that this method can be a complementary tool for identifying different functions of secreted proteins. The code and all datasets used in this article are freely available at http://cic.scu.edu.cn/bioinformatics/fcSecretP.zip.