bluuebunny/crossref_metadata_2025_split huggingface dataset:
Pipasic: similarity and expression correction for strain-level identification and quantification in metaproteomics
Authors: Penzlin Anke , Lindner Martin S. , Doellinger Joerg , Dabrowski Piotr Wojtek , Nitsche Andreas , Renard Bernhard Y. 
Published: June 2014

Abstract
Motivation: Metaproteomic analysis allows studying the interplay of organisms or functional groups and has become increasingly popular also for diagnostic purposes. However, difficulties arise owing to the high sequence similarity between related organisms. Further, the state of conservation of proteins between species can be correlated with their expression level, which can lead to significant bias in results and interpretation. These challenges are similar but not identical to the challenges arising in the analysis of metagenomic samples and require specific solutions.
Results: We introduce Pipasic (peptide intensity-weighted proteome abundance similarity correction) as a tool that corrects identification and spectral counting-based quantification results using peptide similarity estimation and expression level weighting within a non-negative lasso framework. Pipasic has distinct advantages over approaches only regarding unique peptides or aggregating results to the lowest common ancestor, as demonstrated on examples of viral diagnostics and an acid mine drainage dataset.
Availability and implementation: Pipasic source code is freely available from https://sourceforge.net/projects/pipasic/.
Contact: RenardB@rki.de
Supplementary information: Supplementary data are available at Bioinformatics online
Inductive matrix completion for predicting gene–disease associations
Authors: Natarajan Nagarajan , Dhillon Inderjit S. 
Published: June 2014

Abstract
Motivation: Most existing methods for predicting causal disease genes rely on specific type of evidence, and are therefore limited in terms of applicability. More often than not, the type of evidence available for diseases varies—for example, we may know linked genes, keywords associated with the disease obtained by mining text, or co-occurrence of disease symptoms in patients. Similarly, the type of evidence available for genes varies—for example, specific microarray probes convey information only for certain sets of genes. In this article, we apply a novel matrix-completion method called Inductive Matrix Completion to the problem of predicting gene-disease associations; it combines multiple types of evidence (features) for diseases and genes to learn latent factors that explain the observed gene–disease associations. We construct features from different biological sources such as microarray expression data and disease-related textual data. A crucial advantage of the method is that it is inductive; it can be applied to diseases not seen at training time, unlike traditional matrix-completion approaches and network-based inference methods that are transductive.
Results: Comparison with state-of-the-art methods on diseases from the Online Mendelian Inheritance in Man (OMIM) database shows that the proposed approach is substantially better—it has close to one-in-four chance of recovering a true association in the top 100 predictions, compared to the recently proposed Catapult method (second best) that has &lt;15% chance. We demonstrate that the inductive method is particularly effective for a query disease with no previously known gene associations, and for predicting novel genes, i.e. genes that are previously not linked to diseases. Thus the method is capable of predicting novel genes even for well-characterized diseases. We also validate the novelty of predictions by evaluating the method on recently reported OMIM associations and on associations recently reported in the literature.
Availability: Source code and datasets can be downloaded from http://bigdata.ices.utexas.edu/project/gene-disease.
Contact: naga86@cs.utexas.edu
Robust clinical outcome prediction based on Bayesian analysis of transcriptional profiles and prior causal networks
Authors: Zarringhalam Kourosh , Enayetallah Ahmed , Reddy Padmalatha , Ziemek Daniel 
Published: June 2014

Abstract
Motivation: Understanding and predicting an individual’s response in a clinical trial is the key to better treatments and cost-effective medicine. Over the coming years, more and more large-scale omics datasets will become available to characterize patients with complex and heterogeneous diseases at a molecular level. Unfortunately, genetic, phenotypical and environmental variation is much higher in a human trial population than currently modeled or measured in most animal studies. In our experience, this high variability can lead to failure of trained predictors in independent studies and undermines the credibility and utility of promising high-dimensional datasets.
Methods: We propose a method that utilizes patient-level genome-wide expression data in conjunction with causal networks based on prior knowledge. Our approach determines a differential expression profile for each patient and uses a Bayesian approach to infer corresponding upstream regulators. These regulators and their corresponding posterior probabilities of activity are used in a regularized regression framework to predict response.
Results: We validated our approach using two clinically relevant phenotypes, namely acute rejection in kidney transplantation and response to Infliximab in ulcerative colitis. To demonstrate pitfalls in translating trained predictors across independent trials, we analyze performance characteristics of our approach as well as alternative feature sets in the regression on two independent datasets for each phenotype. We show that the proposed approach is able to successfully incorporate causal prior knowledge to give robust performance estimates.
Contact: daniel.ziemek@pfizer.com
Supplementary information: Supplementary data are available at Bioinformatics online.
GRASP: analysis of genotype–phenotype results from 1390 genome-wide association studies and corresponding open access database
Authors: Leslie Richard , O’Donnell Christopher J. , Johnson Andrew D. 
Published: June 2014

Abstract
Summary: We created a deeply extracted and annotated database of genome-wide association studies (GWAS) results. GRASP v1.0 contains &gt;6.2 million SNP-phenotype association from among 1390 GWAS studies. We re-annotated GWAS results with 16 annotation sources including some rarely compared to GWAS results (e.g. RNAediting sites, lincRNAs, PTMs).
Motivation: To create a high-quality resource to facilitate further use and interpretation of human GWAS results in order to address important scientific questions.
Results: GWAS have grown exponentially, with increases in sample sizes and markers tested, and continuing bias toward European ancestry samples. GRASP contains &gt;100 000 phenotypes, roughly: eQTLs (71.5%), metabolite QTLs (21.2%), methylation QTLs (4.4%) and diseases, biomarkers and other traits (2.8%). cis-eQTLs, meQTLs, mQTLs and MHC region SNPs are highly enriched among significant results. After removing these categories, GRASP still contains a greater proportion of studies and results than comparable GWAS catalogs. Cardiovascular disease and related risk factors pre-dominate remaining GWAS results, followed by immunological, neurological and cancer traits. Significant results in GWAS display a highly gene-centric tendency. Sex chromosome X (OR = 0.18[0.16-0.20]) and Y (OR = 0.003[0.001-0.01]) genes are depleted for GWAS results. Gene length is correlated with GWAS results at nominal significance (P ≤ 0.05) levels. We show this gene-length correlation decays at increasingly more stringent P-value thresholds. Potential pleotropic genes and SNPs enriched for multi-phenotype association in GWAS are identified. However, we note possible population stratification at some of these loci. Finally, via re-annotation we identify compelling functional hypotheses at GWAS loci, in some cases unrealized in studies to date.
Conclusion: Pooling summary-level GWAS results and re-annotating with bioinformatics predictions and molecular features provides a good platform for new insights.
Availability: The GRASP database is available at http://apps.nhlbi.nih.gov/grasp.
Contact: johnsonad2@nhlbi.nih.gov
Cross-study validation for the assessment of prediction algorithms
Authors: Bernau Christoph , Riester Markus , Boulesteix Anne-Laure , Parmigiani Giovanni , Huttenhower Curtis , Waldron Levi , Trippa Lorenzo 
Published: June 2014

AbstractMotivation: Numerous competing algorithms for prediction in high-dimensional settings have been developed in the statistical and machine-learning literature. Learning algorithms and the prediction models they generate are typically evaluated on the basis of cross-validation error estimates in a few exemplary datasets. However, in most applications, the ultimate goal of prediction modeling is to provide accurate predictions for independent samples obtained in different settings. Cross-validation within exemplary datasets may not adequately reflect performance in the broader application context.Methods: We develop and implement a systematic approach to ‘cross-study validation’, to replace or supplement conventional cross-validation when evaluating high-dimensional prediction models in independent datasets. We illustrate it via simulations and in a collection of eight estrogen-receptor positive breast cancer microarray gene-expression datasets, where the objective is predicting distant metastasis-free survival (DMFS). We computed the C-index for all pairwise combinations of training and validation datasets. We evaluate several alternatives for summarizing the pairwise validation statistics, and compare these to conventional cross-validation.Results: Our data-driven simulations and our application to survival prediction with eight breast cancer microarray datasets, suggest that standard cross-validation produces inflated discrimination accuracy for all algorithms considered, when compared to cross-study validation. Furthermore, the ranking of learning algorithms differs, suggesting that algorithms performing best in cross-validation may be suboptimal when evaluated through independent validation.Availability: The survHD: Survival in High Dimensions package (http://www.bitbucket.org/lwaldron/survhd) will be made available through Bioconductor.Contact: levi.waldron@hunter.cuny.eduSupplementary information: Supplementary data are available at Bioinformatics online.
Functional association networks as priors for gene regulatory network inference
Authors: Studham Matthew E. , Tjärnberg Andreas , Nordling Torbjörn E.M. , Nelander Sven , Sonnhammer Erik L. L. 
Published: June 2014

Abstract
Motivation: Gene regulatory network (GRN) inference reveals the influences genes have on one another in cellular regulatory systems. If the experimental data are inadequate for reliable inference of the network, informative priors have been shown to improve the accuracy of inferences.
Results: This study explores the potential of undirected, confidence-weighted networks, such as those in functional association databases, as a prior source for GRN inference. Such networks often erroneously indicate symmetric interaction between genes and may contain mostly correlation-based interaction information. Despite these drawbacks, our testing on synthetic datasets indicates that even noisy priors reflect some causal information that can improve GRN inference accuracy. Our analysis on yeast data indicates that using the functional association databases FunCoup and STRING as priors can give a small improvement in GRN inference accuracy with biological data.
Contact: matthew.studham@scilifelab.se
Supplementary information: Supplementary data are available at Bioinformatics online.
Pareto-optimal phylogenetic tree reconciliation
Authors: Libeskind-Hadas Ran , Wu Yi-Chieh , Bansal Mukul S. , Kellis Manolis 
Published: June 2014

Abstract
Motivation: Phylogenetic tree reconciliation is a widely used method for reconstructing the evolutionary histories of gene families and species, hosts and parasites and other dependent pairs of entities. Reconciliation is typically performed using maximum parsimony, in which each evolutionary event type is assigned a cost and the objective is to find a reconciliation of minimum total cost. It is generally understood that reconciliations are sensitive to event costs, but little is understood about the relationship between event costs and solutions. Moreover, choosing appropriate event costs is a notoriously difficult problem.
Results: We address this problem by giving an efficient algorithm for computing Pareto-optimal sets of reconciliations, thus providing the first systematic method for understanding the relationship between event costs and reconciliations. This, in turn, results in new techniques for computing event support values and, for cophylogenetic analyses, performing robust statistical tests. We provide new software tools and demonstrate their use on a number of datasets from evolutionary genomic and cophylogenetic studies.
Availability and implementation: Our Python tools are freely available at www.cs.hmc.edu/∼hadas/xscape.
Contact: mukul@engr.uconn.edu
Supplementary information: Supplementary data are available at Bioinformatics online.
Probabilistic method for detecting copy number variation in a fetal genome using maternal plasma sequencing
Authors: Rampášek Ladislav , Arbabi Aryan , Brudno Michael 
Published: June 2014

Abstract
Motivation: The past several years have seen the development of methodologies to identify genomic variation within a fetus through the non-invasive sequencing of maternal blood plasma. These methods are based on the observation that maternal plasma contains a fraction of DNA (typically 5–15%) originating from the fetus, and such methodologies have already been used for the detection of whole-chromosome events (aneuploidies), and to a more limited extent for smaller (typically several megabases long) copy number variants (CNVs).
Results: Here we present a probabilistic method for non-invasive analysis of de novo CNVs in fetal genome based on maternal plasma sequencing. Our novel method combines three types of information within a unified Hidden Markov Model: the imbalance of allelic ratios at SNP positions, the use of parental genotypes to phase nearby SNPs and depth of coverage to better differentiate between various types of CNVs and improve precision. Our simulation results, based on in silico introduction of novel CNVs into plasma samples with 13% fetal DNA concentration, demonstrate a sensitivity of 90% for CNVs &gt;400 kb (with 13 calls in an unaffected genome), and 40% for 50–400 kb CNVs (with 108 calls in an unaffected genome).
Availability and implementation: Implementation of our model and data simulation method is available at http://github.com/compbio-UofT/fCNV.
Contact: brudno@cs.toronto.edu
Multiple interests across European coastal waters: the importance of a common language
Authors: Ramos Jorge , Soma Katrine , Bergh Øivind , Schulze Torsten , Gimpel Antje , Stelzenmüller Vanessa , Mäkinen Timo , Fabi Gianna , Grati Fabio , Gault Jeremy 
Published: June 2014

AbstractDifferent marine and coastal activities have diverse economic, environmental, and socio-cultural objectives, which can lead to conflict when these multidimensional activities coincide spatially or temporally. This is sometimes driven by a lack of understanding or other users' needs and consequentially adequate planning and the utilization of a common language is essential. By using a transparent approach based on multi-criteria analysis, we characterize and establish priorities for future development/conservation for all users in the coastal area using six representative European Case Studies with different levels of complexity. Results varied according to location, but significantly it was found that stakeholders tended to favour ecological and social over economic objectives. This paper outlines the methodology employed, the results derived, and the potential for this approach to reduce conflict in coastal and marine waters.
Critical role for p53-serine 15 phosphorylation in stimulating transactivation at p53-responsive promoters
Authors: Loughery Jayne , Cox Miranda , Smith Linda M. , Meek David W. 
Published: June 2014

Abstract
The p53 tumour suppressor is induced by various stress stimuli and coordinates an adaptive gene expression programme leading to growth arrest or cell death. Some stimuli, such as DNA damage, lead to rapid and substantial multisite phosphorylation of p53, nucleated initially through phosphorylation of serine 15. Other stimuli, such as hyper-proliferation, do not stimulate p53-phosphorylation, raising questions regarding the physiological role for phosphorylation. Here, we show that a basal level of Ser15 phosphorylation occurs in both unstimulated cells and cells stimulated pharmacologically to induce p53. p53 in which Ser15 is substituted by alanine (S15A) fails to mediate p53-dependent transcription or growth arrest but can be rescued by substitution with aspartate (S15D: a phospho-mimic). Chromatin immunoprecipitation (ChIP) analyses show that, while wt- and S15A-p53 are detectable on the CDKN1A (p21) promoter (as a representative p53-responsive promoter), S15A-p53 does not stimulate histone acetylation (a measure of chromatin relaxation), nor is its recruitment stimulated, in response to a DNA damage or pharmacological stimulus. These data demonstrate that Ser15 phosphorylation is required for p53 function in the physiological context of p53-responsive promoters and suggest a key and possibly universal role even for low levels of this modification in promoting p53-transcription function.
Reducing Unnecessary Preoperative Blood Orders and Costs by Implementing an Updated Institution-specific Maximum Surgical Blood Order Schedule and a Remote Electronic Blood Release System
Authors: Frank Steven M. , Oleyar Michael J. , Ness Paul M. , Tobian Aaron A. R. 
Published: June 2014

Abstract

Background:
Using blood utilization data acquired from the anesthesia information management system, an updated institution-specific maximum surgical blood order schedule was introduced. The authors evaluated whether the maximum surgical blood order schedule, along with a remote electronic blood release system, reduced unnecessary preoperative blood orders and costs.

Methods:
At a large academic medical center, data for preoperative blood orders were analyzed for 63,916 surgical patients over a 34-month period. The new maximum surgical blood order schedule and the electronic blood release system (Hemosafe®; Haemonetics Corp., Braintree, MA) were introduced mid-way through this time period. The authors assessed whether these interventions led to reductions in unnecessary preoperative orders and associated costs.

Results:
Among patients having surgical procedures deemed not to require a type and screen or crossmatch (n = 33,216), the percent of procedures with preoperative blood orders decreased by 38% (from 40.4% [7,167 of 17,740 patients] to 25.0% [3,869 of 15,476 patients], P < 0.001). Among all hospitalized inpatients, the crossmatch-to-transfusion ratio decreased by 27% (from 2.11 to 1.54; P < 0.001) over the same time period. The proportion of patients who required emergency release uncrossmatched blood increased from 2.2 to 3.1 per 1,000 patients (P = 0.03); however, most of these patients were having emergency surgery. Based on the realized reductions in blood orders, annual costs were reduced by $137,223 ($6.08 per patient) for surgical patients, and by $298,966 ($6.20/patient) for all hospitalized patients.

Conclusion:
Implementing institution-specific, updated maximum surgical blood order schedule–directed preoperative blood ordering guidelines along with an electronic blood release system results in a substantial reduction in unnecessary orders and costs, with a clinically insignificant increase in requirement for emergency release blood transfusions.
The management of the domestic refrigeration: microbiological status and temperature
Authors: Vegara Alberto , Di Ciccio Pierluigi , Rita Festino Anna , Costanzo Claudia , Pennisi Luca , Ianieri Adriana 
Published: June 2014

Purpose
– The purpose of this paper is to provide information on the consumer management of refrigerated food, establish the level and the incidence of bacterial contamination and operating temperatures in domestic refrigerators from north and centre Italy.

Design/methodology/approach
– A questionnaire was designed involving 24 questions which covered three topics: the socio-demographic data, the domestic management of refrigerated food and general information about the refrigerator. The questionnaire responses were subjected to descriptive statistical analysis and further processed with the multiple correspondence analysis (MCA) and cluster analysis (CA). Based on MCA and CA, 84 refrigerators were selected to assess the temperature and the microbiological status (total viable counts (TVC); Enterobacteriaceae total counts (ETC); Salmonella spp. and Listeria spp.).

Findings
– Totally, 660 interviews were carried out. The majority of respondents were female (81.4 per cent) and were married (71.4 per cent). Almost an half of them were between 31 and 50 years old (48.2 per cent) and had a secondary school degree (47 per cent). Regarding domestic management of refrigerated food, the majority of respondents (87.2 per cent) were aware of the correct temperature range (1-5°C) for retail refrigerator units, but only 18 per cent of them check the temperature. Listeria monocytogenes and Salmonella spp. were not recovered; Listeria innocua was recovered (2.4 per cent). Regarding the TVC values, the 21.5 per cent of the tested refrigerators were classified as insufficient (from 100 to 104 cfu/cm2) or inadequate (>104 cfu/cm2). Consumer education should be focused in order to reduce foodborne disease. Only safety-conscious consumers can become active partners within the food safety chain.

Originality/value
– Result obtained from the present survey revealed that consumers are not familiar with their role in the food safety chain and that they allow numerous opportunities for microbiological contamination of food. The study clearly indicates the need for greater consumer education regarding proper domestic refrigerator management. Indeed, appropriate behaviours could make the refrigerator less likely to act as a significant niche for persistence and dissemination of food pathogens.
From killing cows to culturing meat
Authors: Buscemi Francesco 
Published: June 2014

Purpose– The purpose of this paper is to investigate how in Britain, France and Italy the idea of the living animal is being detached from the action of eating meat. It is an ongoing historical process, which has recently been fuelled by the new issue of cultured meat.Design/methodology/approach– Starting from Goody's developmentalist stages (Production, Distribution, Preparation and Consumption), first this work analyses historically how these stages have undergone the process of the disappearance of the animal origins of meat (animal origins of meat are parts like the head and legs that remind us that once meat was an animal). Second, this paper applies cultured meat to Goody's stage of Production, linking the new product to the historical, above described, process.Findings– The analysis shows that, in the past, Goody's stages of Consumption, Distribution and Preparation witnessed the disappearance of the animal origins of meat, while Production was not affected by the phenomenon. Today, with testing on cultured meat, even the stage of Production has gone through this process. Now, all of Goody's stages are involved in the process. Usually considered a shocking novelty, cultured meat is instead a stage of a historical process.Originality/value– No one has previously analysed the disappearance of the animal origins of meat relating it to Goody's stages, and a current issue like cultured meat has never been considered a further step of this process.