Parallel Mutual Information Based Construction of Genome-Scale Networks on the Intel® Xeon Phi™ Coprocessor.

IEEE/ACM Trans Comput Biol Bioinform. 2015 Sep-Oct;12(5):1008-20

Authors: Misra S, Pamnany K, Aluru S

Abstract
Construction of whole-genome networks from large-scale gene expression data is an important problem in systems biology. While several techniques have been developed, most cannot handle network reconstruction at the whole-genome scale, and the few that can, require large clusters. In this paper, we present a solution on the Intel Xeon Phi coprocessor, taking advantage of its multi-level parallelism including many x86-based cores, multiple threads per core, and vector processing units. We also present a solution on the Intel® Xeon® processor. Our solution is based on TINGe, a fast parallel network reconstruction technique that uses mutual information and permutation testing for assessing statistical significance. We demonstrate the first ever inference of a plant whole genome regulatory network on a single chip by constructing a 15,575 gene network of the plant Arabidopsis thaliana from 3,137 microarray experiments in only 22 minutes. In addition, our optimization for parallelizing mutual information computation on the Intel Xeon Phi coprocessor holds out lessons that are applicable to other domains.

PMID: 26451815 [PubMed - indexed for MEDLINE]

Expert-Guided Generative Topographical Modeling with Visual to Parametric Interaction.

PLoS One. 2016;11(2):e0129122

Authors: Han C, House L, Leman SC

Abstract
Introduced by Bishop et al. in 1996, Generative Topographic Mapping (GTM) is a powerful nonlinear latent variable modeling approach for visualizing high-dimensional data. It has shown useful when typical linear methods fail. However, GTM still suffers from drawbacks. Its complex parameterization of data make GTM hard to fit and sensitive to slight changes in the model. For this reason, we extend GTM to a visual analytics framework so that users may guide the parameterization and assess the data from multiple GTM perspectives. Specifically, we develop the theory and methods for Visual to Parametric Interaction (V2PI) with data using GTM visualizations. The result is a dynamic version of GTM that fosters data exploration. We refer to the new version as V2PI-GTM. In this paper, we develop V2PI-GTM in stages and demonstrate its benefits within the context of a text mining case study.

PMID: 26905728 [PubMed - indexed for MEDLINE]

DESM: portal for microbial knowledge exploration systems.

Nucleic Acids Res. 2016 Jan 4;44(D1):D624-33

Authors: Salhi A, Essack M, Radovanovic A, Marchand B, Bougouffa S, Antunes A, Simoes MF, Lafi FF, Motwalli OA, Bokhari A, Malas T, Amoudi SA, Othum G, Allam I, Mineta K, Gao X, Hoehndorf R, C Archer JA, Gojobori T, Bajic VB

Abstract
Microorganisms produce an enormous variety of chemical compounds. It is of general interest for microbiology and biotechnology researchers to have means to explore information about molecular and genetic basis of functioning of different microorganisms and their ability for bioproduction. To enable such exploration, we compiled 45 topic-specific knowledgebases (KBs) accessible through DESM portal (www.cbrc.kaust.edu.sa/desm). The KBs contain information derived through text-mining of PubMed information and complemented by information data-mined from various other resources (e.g. ChEBI, Entrez Gene, GO, KOBAS, KEGG, UniPathways, BioGrid). All PubMed records were indexed using 4,538,278 concepts from 29 dictionaries, with 1 638 986 records utilized in KBs. Concepts used are normalized whenever possible. Most of the KBs focus on a particular type of microbial activity, such as production of biocatalysts or nutraceuticals. Others are focused on specific categories of microorganisms, e.g. streptomyces or cyanobacteria. KBs are all structured in a uniform manner and have a standardized user interface. Information exploration is enabled through various searches. Users can explore statistically most significant concepts or pairs of concepts, generate hypotheses, create interactive networks of associated concepts and export results. We believe DESM will be a useful complement to the existing resources to benefit microbiology and biotechnology research.

PMID: 26546514 [PubMed - indexed for MEDLINE]

24 new pubmed citations were retrieved for your search. Click on the search hyperlink below to display the complete search results:

("orphan disease" OR "rare disease" OR "orphan diseases" OR "rare diseases")

These pubmed results were generated on 2016/07/21

PubMed comprises more than 24 million citations for biomedical literature from MEDLINE, life science journals, and online books. Citations may include links to full-text content from PubMed Central and publisher web sites.

7 new pubmed citations were retrieved for your search. Click on the search hyperlink below to display the complete search results:

"Cystic Fibrosis"

These pubmed results were generated on 2016/07/21

PubMed comprises more than 24 million citations for biomedical literature from MEDLINE, life science journals, and online books. Citations may include links to full-text content from PubMed Central and publisher web sites.

[HORMONALLY-GENETICALLY DEPENDENT THERAPY, USING VITAMIN K IN PATIENTS, SUFFERING THE ULCER HEMORRHAGE].

Klin Khir. 2016 Apr;(4):9-11

Authors: Duzhyi ID, Kharchenko SV

Abstract
Pathophysiological mechanisms of the vitamin K impact, including those in the gut with ulcerative affection, are studied still insufficiently. Investigations of pharmacogenomics of the vitamin K gives a new approach to therapy in patients, suffering gastro-intestinal hemorrhage. Possibilities of titration of the vitamin K3 (menadione) doses, depending on level of estrogenemia and genetic constitution, concerning genes-candidates ESR1 (rs2234693) and VKORC1 (rs9923231), were studied. There were examined 36 patients, who were treated for the ulcer hemorrhage. The blood serum concentration of estradiol was investigated in accordance to method of solid phase enzyme immunoassay, the genotyping procedure was performed in accordance to indices of polymerase chain reaction with analysis of the restrictional fragments length. The initial daily dose of menadione have constituted 20 mg. After a genotype determination made (first-second day after admittance to hospital) in patients with normoestrogenemia in genotypes CC/GG, CC/GA, CT/GG, CT/GA a vitaminotherapy was prolonged in daily dose of 20 mg, and in a conditionally-pathological variant of genotype the dose of vitamin K was enhanced up to 30 mg. Determination of hormones and the patients' genetic constitution makes possible to apply a personified approach for the vitamin K3 application in the ulcerative hemorrhage.

PMID: 27434945 [PubMed - in process]

Systems analysis of cis-regulatory motifs in C4 photosynthesis genes using maize and rice leaf transcriptomic data during a process of de-etiolation.

J Exp Bot. 2016 Jul 19;

Authors: Xu J, Bräutigam A, Li Y, Weber AP, Zhu XG

Abstract
Identification of potential cis-regulatory motifs controlling the development of C4 photosynthesis is a major focus of current research. In this study, we used time-series RNA-seq data collected from etiolated maize and rice leaf tissues sampled during a de-etiolation process to systematically characterize the expression patterns of C4-related genes and to further identify potential cis elements in five different genomic regions (i.e. promoter, 5'UTR, 3'UTR, intron, and coding sequence) of C4 orthologous genes. The results demonstrate that although most of the C4 genes show similar expression patterns, a number of them, including chloroplast dicarboxylate transporter 1, aspartate aminotransferase, and triose phosphate transporter, show shifted expression patterns compared with their C3 counterparts. A number of conserved short DNA motifs between maize C4 genes and their rice orthologous genes were identified not only in the promoter, 5'UTR, 3'UTR, and coding sequences, but also in the introns of core C4 genes. We also identified cis-regulatory motifs that exist in maize C4 genes and also in genes showing similar expression patterns as maize C4 genes but that do not exist in rice C3 orthologs, suggesting a possible recruitment of pre-existing cis-elements from genes unrelated to C4 photosynthesis into C4 photosynthesis genes during C4 evolution.

PMID: 27436282 [PubMed - as supplied by publisher]

Training in metabolomics research. I. Designing the experiment, collecting and extracting samples and generating metabolomics data.

J Mass Spectrom. 2016 Jul;51(7):461-75

Authors: Barnes S, Benton HP, Casazza K, Cooper SJ, Cui X, Du X, Engler J, Kabarowski JH, Li S, Pathmasiri W, Prasain JK, Renfrow MB, Tiwari HK

Abstract
The study of metabolism has had a long history. Metabolomics, a systems biology discipline representing analysis of known and unknown pathways of metabolism, has grown tremendously over the past 20 years. Because of its comprehensive nature, metabolomics requires careful consideration of the question(s) being asked, the scale needed to answer the question(s), collection and storage of the sample specimens, methods for extraction of the metabolites from biological matrices, the analytical method(s) to be employed and the quality control of the analyses, how collected data are correlated, the statistical methods to determine metabolites undergoing significant change, putative identification of metabolites and the use of stable isotopes to aid in verifying metabolite identity and establishing pathway connections and fluxes. The National Institutes of Health Common Fund Metabolomics Program was established in 2012 to stimulate interest in the approaches and technologies of metabolomics. To deliver one of the program's goals, the University of Alabama at Birmingham has hosted an annual 4-day short course in metabolomics for faculty, postdoctoral fellows and graduate students from national and international institutions. This paper is the first part of a summary of the training materials presented in the course to be used as a resource for all those embarking on metabolomics research. The complete set of training materials including slide sets and videos can be viewed at http://www.uab.edu/proteomics/metabolomics/workshop/workshop_june_2015.php. Copyright © 2016 John Wiley & Sons, Ltd.

PMID: 27434804 [PubMed - in process]

Animal Models of Chemical Carcinogenesis: Driving Breakthroughs in Cancer Research for 100 Years.

Cold Spring Harb Protoc. 2015 Oct;2015(10):865-74

Authors: Kemp CJ

Abstract
The identification of carcinogens in the workplace, diet, and environment through chemical carcinogenesis studies in animals has directly contributed to a reduction of cancer burden in the human population. Reduced exposure to these carcinogens through lifestyle changes, government regulation, or change in industry practices has reduced cancer incidence in exposed populations. In addition to providing the first experimental evidence for cancer's relationship to chemical and radiation exposure, animal models of environmentally induced cancer have and will continue to provide important insight into the causes, mechanisms, and conceptual frameworks of cancer. More recently, combining chemical carcinogens with genetically engineered mouse models has emerged as an invaluable approach to study the complex interaction between genotype and environment that contributes to cancer development. In the future, animal models of environmentally induced cancer are likely to provide insight into areas such as the epigenetic basis of cancer, genetic modifiers of cancer susceptibility, the systems biology of cancer, inflammation and cancer, and cancer prevention.

PMID: 26430259 [PubMed - indexed for MEDLINE]

Link Prediction on a Network of Co-occurring MeSH Terms: Towards Literature-based Discovery.

Methods Inf Med. 2016 Jul 20;55(4)

Authors: Kastrin A, Rindflesch TC, Hristovski D

Abstract
OBJECTIVES: Literature-based discovery (LBD) is a text mining methodology for automatically generating research hypotheses from existing knowledge. We mimic the process of LBD as a classification problem on a graph of MeSH terms. We employ unsupervised and supervised link prediction methods for predicting previously unknown connections between biomedical concepts.
METHODS: We evaluate the effectiveness of link prediction through a series of experiments using a MeSH network that contains the history of link formation between biomedical concepts. We performed link prediction using proximity measures, such as common neighbor (CN), Jaccard coefficient (JC), Adamic / Adar index (AA) and preferential attachment (PA). Our approach relies on the assumption that similar nodes are more likely to establish a link in the future.
RESULTS: Applying an unsupervised approach, the AA measure achieved the best performance in terms of area under the ROC curve (AUC = 0.76), followed by CN, JC, and PA. In a supervised approach, we evaluate whether proximity measures can be combined to define a model of link formation across all four predictors. We applied various classifiers, including decision trees, k-nearest neighbors, logistic regression, multilayer perceptron, naïve Bayes, and random forests. Random forest classifier accomplishes the best performance (AUC = 0.87).
CONCLUSIONS: The link prediction approach proved to be effective for LBD processing. Supervised statistical learning approaches clearly outperform an unsupervised approach to link prediction.

PMID: 27435341 [PubMed - as supplied by publisher]

Comprehensive Map of Molecules Implicated in Obesity.

PLoS One. 2016;11(2):e0146759

Authors: Jagannadham J, Jaiswal HK, Agrawal S, Rawal K

Abstract
Obesity is a global epidemic affecting over 1.5 billion people and is one of the risk factors for several diseases such as type 2 diabetes mellitus and hypertension. We have constructed a comprehensive map of the molecules reported to be implicated in obesity. A deep curation strategy was complemented by a novel semi-automated text mining system in order to screen 1,000 full-length research articles and over 90,000 abstracts that are relevant to obesity. We obtain a scale free network of 804 nodes and 971 edges, composed of 510 proteins, 115 genes, 62 complexes, 23 RNA molecules, 83 simple molecules, 3 phenotype and 3 drugs in "bow-tie" architecture. We classify this network into 5 modules and identify new links between the recently discovered fat mass and obesity associated FTO gene with well studied examples such as insulin and leptin. We further built an automated docking pipeline to dock orlistat as well as other drugs against the 24,000 proteins in the human structural proteome to explain the therapeutics and side effects at a network level. Based upon our experiments, we propose that therapeutic effect comes through the binding of one drug with several molecules in target network, and the binding propensity is both statistically significant and different in comparison with any other part of human structural proteome.

PMID: 26886906 [PubMed - indexed for MEDLINE]

Detecting themes of public concern: a text mining analysis of the Centers for Disease Control and Prevention's Ebola live Twitter chat.

Am J Infect Control. 2015 Oct 1;43(10):1109-11

Authors: Lazard AJ, Scheinfeld E, Bernhardt JM, Wilcox GB, Suran M

Abstract
A diagnosis of Ebola on US soil triggered widespread panic. In response, the Centers for Disease Control and Prevention held a live Twitter chat to address public concerns. This study applied a textual analytics method to reveal insights from these tweets that can inform communication strategies. User-generated tweets were collected, sorted, and analyzed to reveal major themes. The public was concerned with symptoms and lifespan of the virus, disease transfer and contraction, safe travel, and protection of one's body.

PMID: 26138998 [PubMed - indexed for MEDLINE]

11 new pubmed citations were retrieved for your search. Click on the search hyperlink below to display the complete search results:

("orphan disease" OR "rare disease" OR "orphan diseases" OR "rare diseases")

These pubmed results were generated on 2016/07/20

PubMed comprises more than 24 million citations for biomedical literature from MEDLINE, life science journals, and online books. Citations may include links to full-text content from PubMed Central and publisher web sites.

15 new pubmed citations were retrieved for your search. Click on the search hyperlink below to display the complete search results:

"Systems Biology"[Title/Abstract] AND ("2005/01/01"[PDAT] : "3000"[PDAT])

These pubmed results were generated on 2016/07/20

PubMed comprises more than 24 million citations for biomedical literature from MEDLINE, life science journals, and online books. Citations may include links to full-text content from PubMed Central and publisher web sites.

Tumor deconstruction as a tool for advanced drug screening and repositioning.

Pharmacol Res. 2016 Jul 15;

Authors: Naik RR, Luo T, Kohandel M, Bapat SA

Abstract
A major focus of contemporary drug screening strategies is the identification of novel anticancer compounds, which often results in underutilization of resources. Current drug evaluation involves in vivo tumor (xenograft) regression as proof-of-principle for cytotoxicity (POC). However, this end-point lacks any assessment of drug resistance of the residual tumor and its capability to establish refractory and/or recurrent disease, which would represent more appropriate indicators of therapeutic failure. We have recently developed a flow cytometry-based approach for the analyses of intra-tumor cellular heterogeneity across stem cell hierarchies, genetic instability and differential cell cycling fractions, which can potentially be predictive of refractory disease and tumor relapse. Iterating this approach after initial POC screening in the drug discovery pipeline would have a great impact in terms of precision of drug evaluation, design of optimal drug combinations and/or drug repositioning. In this perspective, we highlight how through embracing of a comprehensive, informative and analytical assessment of the cellular content of residual tumors, the fidelity and statistical robustness of preclinical drug discovery can be greatly improved.

PMID: 27431330 [PubMed - as supplied by publisher]

Repurposing old drugs to chemoprevention: the case of metformin.

Semin Oncol. 2016 Feb;43(1):123-33

Authors: Heckman-Stoddard BM, Gandini S, Puntoni M, Dunn BK, DeCensi A, Szabo E

Abstract
Multiple epidemiologic studies have documented an association between the anti-diabetic agent metformin and reduced cancer incidence and mortality. However, this effect has not been consistently demonstrated in animal models or more recent epidemiological studies. The purpose of this paper is to examine metformin's chemopreventive potential by reviewing relevant mechanisms of action, preclinical evidence of efficacy, updated epidemiologic evidence after correction for potential biases and confounders, and recently completed and ongoing clinical trials. Although repurposing drugs with well described mechanisms of action and safety profiles is an appealing strategy for cancer prevention, there is no substitute for well executed late phase clinical trials to define efficacy and populations that are most likely to benefit from an intervention.

PMID: 26970131 [PubMed - indexed for MEDLINE]

Regulation of Inflammation by IL-17A and IL-17F Modulates Non-Alcoholic Fatty Liver Disease Pathogenesis.

PLoS One. 2016;11(2):e0149783

Authors: Giles DA, Moreno-Fernandez ME, Stankiewicz TE, Cappelletti M, Huppert SS, Iwakura Y, Dong C, Shanmukhappa SK, Divanovic S

Abstract
Non-alcoholic fatty liver disease (NAFLD) has become the most common chronic liver disease worldwide. While it is well-accepted that inflammation is central to NAFLD pathogenesis, the immune pathway(s) orchestrating disease progression are poorly defined. Notably, IL-17RA signaling, via IL-17A, plays an important role in obesity-driven NAFLD pathogenesis. However, the role of the IL-17F, another IL-17RA ligand, in NAFLD pathogenesis has not been examined. Further, the cell types expressing IL-17RA and producing IL-17RA ligands in the pathogenesis of NAFLD have not been defined. Here, IL-17RA-/-, IL-17A-/-, IL-17F-/- and wild-type (WT) mice were fed either standard chow diet or methionine and choline deficient diet (MCDD)--a diet known to induce steatosis and hepatic inflammation through beta-oxidation dysfunction--and hepatic inflammation and NAFLD progression were subsequently quantified. MCDD feeding augmented hepatic IL-17RA expression and significantly increased hepatic infiltration of macrophages and IL-17A and IL-17F producing CD4+ and CD8+ T cells in WT mice. In contrast, IL-17RA-/-, IL-17A-/-, and IL-17F-/- mice, despite increased steatosis, exhibited significant protection from hepatocellular damage compared to WT controls. Protection from hepatocellular damage correlated with decreased levels of hepatic T-cell and macrophage infiltration and decreased expression of inflammatory mediators associated with NAFLD. In sum, our results indicate that the IL-17 axis also plays a role in a MCDD-induced model of NAFLD pathogenesis. Further, we show for the first time that IL-17F, and not only IL-17A, plays an important role in NAFLD driven inflammation.

PMID: 26895034 [PubMed - indexed for MEDLINE]

Publishing FAIR Data: An Exemplar Methodology Utilizing PHI-Base.

Front Plant Sci. 2016;7:641

Authors: Rodríguez-Iglesias A, Rodríguez-González A, Irvine AG, Sesma A, Urban M, Hammond-Kosack KE, Wilkinson MD

Abstract
Pathogen-Host interaction data is core to our understanding of disease processes and their molecular/genetic bases. Facile access to such core data is particularly important for the plant sciences, where individual genetic and phenotypic observations have the added complexity of being dispersed over a wide diversity of plant species vs. the relatively fewer host species of interest to biomedical researchers. Recently, an international initiative interested in scholarly data publishing proposed that all scientific data should be "FAIR"-Findable, Accessible, Interoperable, and Reusable. In this work, we describe the process of migrating a database of notable relevance to the plant sciences-the Pathogen-Host Interaction Database (PHI-base)-to a form that conforms to each of the FAIR Principles. We discuss the technical and architectural decisions, and the migration pathway, including observations of the difficulty and/or fidelity of each step. We examine how multiple FAIR principles can be addressed simultaneously through careful design decisions, including making data FAIR for both humans and machines with minimal duplication of effort. We note how FAIR data publishing involves more than data reformatting, requiring features beyond those exhibited by most life science Semantic Web or Linked Data resources. We explore the value-added by completing this FAIR data transformation, and then test the result through integrative questions that could not easily be asked over traditional Web-based data resources. Finally, we demonstrate the utility of providing explicit and reliable access to provenance information, which we argue enhances citation rates by encouraging and facilitating transparent scholarly reuse of these valuable data holdings.

PMID: 27433158 [PubMed]

An ensemble method for extracting adverse drug events from social media.

Artif Intell Med. 2016 Jun;70:62-76

Authors: Liu J, Zhao S, Zhang X

Abstract
OBJECTIVE: Because adverse drug events (ADEs) are a serious health problem and a leading cause of death, it is of vital importance to identify them correctly and in a timely manner. With the development of Web 2.0, social media has become a large data source for information on ADEs. The objective of this study is to develop a relation extraction system that uses natural language processing techniques to effectively distinguish between ADEs and non-ADEs in informal text on social media.
METHODS AND MATERIALS: We develop a feature-based approach that utilizes various lexical, syntactic, and semantic features. Information-gain-based feature selection is performed to address high-dimensional features. Then, we evaluate the effectiveness of four well-known kernel-based approaches (i.e., subset tree kernel, tree kernel, shortest dependency path kernel, and all-paths graph kernel) and several ensembles that are generated by adopting different combination methods (i.e., majority voting, weighted averaging, and stacked generalization). All of the approaches are tested using three data sets: two health-related discussion forums and one general social networking site (i.e., Twitter).
RESULTS: When investigating the contribution of each feature subset, the feature-based approach attains the best area under the receiver operating characteristics curve (AUC) values, which are 78.6%, 72.2%, and 79.2% on the three data sets. When individual methods are used, we attain the best AUC values of 82.1%, 73.2%, and 77.0% using the subset tree kernel, shortest dependency path kernel, and feature-based approach on the three data sets, respectively. When using classifier ensembles, we achieve the best AUC values of 84.5%, 77.3%, and 84.5% on the three data sets, outperforming the baselines.
CONCLUSIONS: Our experimental results indicate that ADE extraction from social media can benefit from feature selection. With respect to the effectiveness of different feature subsets, lexical features and semantic features can enhance the ADE extraction capability. Kernel-based approaches, which can stay away from the feature sparsity issue, are qualified to address the ADE extraction problem. Combining different individual classifiers using suitable combination methods can further enhance the ADE extraction effectiveness.

PMID: 27431037 [PubMed - in process]

CYP2C19 Genotype-Dependent Pharmacokinetic Drug Interaction Between Voriconazole and Ritonavir-Boosted Atazanavir in Healthy Subjects.

J Clin Pharmacol. 2016 Jul 19;

Authors: Zhu L, Brüggemann RJ, Uy J, Colbers A, Hruska MW, Chung E, Sims K, Vakkalagadda B, Xu X, van Schaik RH, Burger DM, Bertz RJ

Abstract
Voriconazole, a broad-spectrum triazole antifungal agent, is metabolized by cytochrome P450 (CYP) 2C19 and to a lesser extent by CYP3A. Genetic polymorphism of CYP2C19 not only plays a prominent role in its disposition but may also influence potential drug interactions with CYP450 modulators such as ritonavir. This study assessed 2-way drug interactions of voriconazole added-on to ritonavir-boosted atazanavir in both CYP2C19 extensive metabolizer (EM) and poor metabolizer (PM) healthy subjects. Each subject received voriconazole alone on days 1-3 followed by a 7-day washout. Atazanavir/ritonavir 300/100 mg once daily was given on days 11-30 and voriconazole on days 21-30. Voriconazole doses were 200 mg (400 mg on days 1 and 21) twice daily and 50 mg (100 mg on days 1 and 21) twice daily for CYP2C19 EM and PM subjects, respectively. Upon coadministration, voriconazole AUC and Cmin decreased by 33% (90% CI = 22-42%) and 39% (90% CI = 28-49%), respectively, in CYP2C19 EMs, whereas voriconazole Cmax and AUC increased 4.4- (90% CI = 3.6-5.4) and 5.6-fold (90% CI = 4.5-7.0), respectively, in PMs. Adding voriconazole resulted in a 20-30% decrease in atazanavir Cmin in both EMs and PMs. Ritonavir exposures were generally unchanged in either population. The safety and tolerability profiles of the combination were comparable with atazanavir/ritonavir and voriconazole administered alone. The most frequent adverse events with voriconazole were visual disturbance and headache. Coadministration of voriconazole and atazanavir/ritonavir is not recommended unless the benefit/risk to the patient justifies the use of the combination. This article is protected by copyright. All rights reserved.

PMID: 27432796 [PubMed - as supplied by publisher]