bluuebunny/crossref_metadata_2025_split huggingface dataset:
A global meta-analysis of microarray expression data to predict unknown gene functions and estimate the literature-data divide
Authors: Wren Jonathan D. 
Published: May 2009

Abstract
Motivation: Approximately 9334 (37%) of Human genes have no publications documenting their function and, for those that are published, the number of publications per gene is highly skewed. Furthermore, for reasons not clear, the entry of new gene names into the literature has slowed in recent years. If we are to better understand human/mammalian biology and complete the catalog of human gene function, it is important to finish predicting putative functions for these genes based upon existing experimental evidence.
Results: A global meta-analysis (GMA) of all publicly available GEO two-channel human microarray datasets (3551 experiments total) was conducted to identify genes with recurrent, reproducible patterns of co-regulation across different conditions. Patterns of co-expression were divided into parallel (i.e. genes are up and down-regulated together) and anti-parallel. Several ranking methods to predict a gene's function based on its top 20 co-expressed gene pairs were compared. In the best method, 34% of predicted Gene Ontology (GO) categories matched exactly with the known GO categories for ∼5000 genes analyzed versus only 3% for random gene sets. Only 2.4% of co-expressed gene pairs were found as co-occurring gene pairs in MEDLINE.
Conclusions: Via a GO enrichment analysis, genes co-expressed in parallel with the query gene were frequently associated with the same GO categories, whereas anti-parallel genes were not. Combining parallel and anti-parallel genes for analysis resulted in fewer significant GO categories, suggesting they are best analyzed separately. Expression databases contain much unexpected genetic knowledge that has not yet been reported in the literature. A total of 1642 Human genes with unknown function were differentially expressed in at least 30 experiments.
Availability: Data matrix available upon request.
Contact: jdwren@gmail.com
Supplementary information: Supplementary data are available at Bioinformatics online.
Highly accelerated feature detection in proteomics data sets using modern graphics processing units
Authors: Hussong Rene , Gregorius Barbara , Tholey Andreas , Hildebrandt Andreas 
Published: May 2009

Abstract
Motivation: Mass spectrometry (MS) is one of the most important techniques for high-throughput analysis in proteomics research. Due to the large number of different proteins and their post-translationally modified variants, the amount of data generated by a single wet-lab MS experiment can easily exceed several gigabytes. Hence, the time necessary to analyze and interpret the measured data is often significantly larger than the time spent on sample preparation and the wet-lab experiment itself. Since the automated analysis of this data is hampered by noise and baseline artifacts, more sophisticated computational techniques are required to handle the recorded mass spectra. Obviously, there is a clear tradeoff between performance and quality of the analysis, which is currently one of the most challenging problems in computational proteomics.
Results: Using modern graphics processing units (GPUs), we implemented a feature finding algorithm based on a hand-tailored adaptive wavelet transform that drastically reduces the computation time. A further speedup can be achieved exploiting the multi-core architecture of current computing devices, which leads to up to an approximately 200-fold speedup in our computational experiments. In addition, we will demonstrate that several approximations necessary on the CPU to keep run times bearable, become obsolete on the GPU, yielding not only faster, but also improved results.
Availability: An open source implementation of the CUDA-based algorithm is available via the software framework OpenMS (http://www.openms.de).
Contact: rene@bioinf.uni-sb.de; anhi@bioinf.uni-sb.de
Supplementary information: Supplementary data are available at Bioinformatics online.
Structure discovery in PPI networks using pattern-based network decomposition
Authors: Bachman Philip , Liu Ying 
Published: May 2009

Abstract
Motivation: The large, complex networks of interactions between proteins provide a lens through which one can examine the structure and function of biological systems. Previous analyses of these continually growing networks have primarily followed either of two approaches: large-scale statistical analysis of holistic network properties, or small-scale analysis of local topological features. Meanwhile, investigation of meso-scale network structure (above that of individual functional modules, while maintaining the significance of individual proteins) has been hindered by the computational complexity of structural search in networks. Examining protein–protein interaction (PPI) networks at the meso-scale may provide insights into the presence and form of relationships between individual protein complexes and functional modules.
Results: In this article, we present an efficient algorithm for performing sub-graph isomorphism queries on a network and show its computational advantage over previous methods. We also present a novel application of this form of topological search which permits analysis of a network's structure at a scale between that of individual functional modules and that of network-wide properties. This analysis provides support for the presence of hierarchical modularity in the PPI network of Saccharomyces cerevisiae.
Contact: ying.liu@utdallas.edu
Hierarchical hidden Markov model with application to joint analysis of ChIP-chip and ChIP-seq data
Authors: Choi Hyungwon , Nesvizhskii Alexey I. , Ghosh Debashis , Qin Zhaohui S. 
Published: May 2009

Abstract
Motivation: Chromatin immunoprecipitation (ChIP) experiments followed by array hybridization, or ChIP-chip, is a powerful approach for identifying transcription factor binding sites (TFBS) and has been widely used. Recently, massively parallel sequencing coupled with ChIP experiments (ChIP-seq) has been increasingly used as an alternative to ChIP-chip, offering cost-effective genome-wide coverage and resolution up to a single base pair. For many well-studied TFs, both ChIP-seq and ChIP-chip experiments have been applied and their data are publicly available. Previous analyses have revealed substantial technology-specific binding signals despite strong correlation between the two sets of results. Therefore, it is of interest to see whether the two data sources can be combined to enhance the detection of TFBS.
Results: In this work, hierarchical hidden Markov model (HHMM) is proposed for combining data from ChIP-seq and ChIP-chip. In HHMM, inference results from individual HMMs in ChIP-seq and ChIP-chip experiments are summarized by a higher level HMM. Simulation studies show the advantage of HHMM when data from both technologies co-exist. Analysis of two well-studied TFs, NRSF and CCCTC-binding factor (CTCF), also suggests that HHMM yields improved TFBS identification in comparison to analyses using individual data sources or a simple merger of the two.
Availability: Source code for the software ChIPmeta is freely available for download at http://www.umich.edu/∼hwchoi/HHMMsoftware.zip, implemented in C and supported on linux.
Contact: ghoshd@psu.edu; qin@umich.edu
Supplementary information: Supplementary data are available at Bioinformatics online.
Data structures and compression algorithms for genomic sequence data
Authors: Brandon Marty C. , Wallace Douglas C. , Baldi Pierre 
Published: May 2009

Abstract
Motivation: The continuing exponential accumulation of full genome data, including full diploid human genomes, creates new challenges not only for understanding genomic structure, function and evolution, but also for the storage, navigation and privacy of genomic data. Here, we develop data structures and algorithms for the efficient storage of genomic and other sequence data that may also facilitate querying and protecting the data.
Results: The general idea is to encode only the differences between a genome sequence and a reference sequence, using absolute or relative coordinates for the location of the differences. These locations and the corresponding differential variants can be encoded into binary strings using various entropy coding methods, from fixed codes such as Golomb and Elias codes, to variables codes, such as Huffman codes. We demonstrate the approach and various tradeoffs using highly variables human mitochondrial genome sequences as a testbed. With only a partial level of optimization, 3615 genome sequences occupying 56 MB in GenBank are compressed down to only 167 KB, achieving a 345-fold compression rate, using the revised Cambridge Reference Sequence as the reference sequence. Using the consensus sequence as the reference sequence, the data can be stored using only 133 KB, corresponding to a 433-fold level of compression, roughly a 23% improvement. Extensions to nuclear genomes and high-throughput sequencing data are discussed.
Availability: Data are publicly available from GenBank, the HapMap web site, and the MITOMAP database. Supplementary materials with additional results, statistics, and software implementations are available from http://mammag.web.uci.edu/bin/view/Mitowiki/ProjectDNACompression.
Contact: pfbaldi@ics.uci.edu
Gradient lasso for Cox proportional hazards model
Authors: Sohn Insuk , Kim Jinseog , Jung Sin-Ho , Park Changyi 
Published: May 2009

AbstractMotivation: There has been an increasing interest in expressing a survival phenotype (e.g. time to cancer recurrence or death) or its distribution in terms of a subset of the expression data of a subset of genes. Due to high dimensionality of gene expression data, however, there is a serious problem of collinearity in fitting a prediction model, e.g. Cox's proportional hazards model. To avoid the collinearity problem, several methods based on penalized Cox proportional hazards models have been proposed. However, those methods suffer from severe computational problems, such as slow or even failed convergence, because of high-dimensional matrix inversions required for model fitting. We propose to implement the penalized Cox regression with a lasso penalty via the gradient lasso algorithm that yields faster convergence to the global optimum than do other algorithms. Moreover the gradient lasso algorithm is guaranteed to converge to the optimum under mild regularity conditions. Hence, our gradient lasso algorithm can be a useful tool in developing a prediction model based on high-dimensional covariates including gene expression data.Results: Results from simulation studies showed that the prediction model by gradient lasso recovers the prognostic genes. Also results from diffuse large B-cell lymphoma datasets and Norway/Stanford breast cancer dataset indicate that our method is very competitive compared with popular existing methods by Park and Hastie and Goeman in its computational time, prediction and selectivity.Availability: R package glcoxph is available at http://datamining.dongguk.ac.kr/R/glcoxph.Contact: park463@uos.ac.kr
Relating periodicity of nucleosome organization and gene regulation
Authors: Wan Jun , Lin Jimmy , Zack Donald J. , Qian Jiang 
Published: May 2009

AbstractMotivation: The relationship between nucleosome positioning and gene regulation is fundamental yet complex. Previous studies on genomic nucleosome positions have revealed a correlation between nucleosome occupancy on promoters and gene expression levels. Many of these studies focused on individual nucleosomes, especially those proximal to transcription start sites. To study the collective effect of multiple nucleosomes on the gene expression, we developed a mathematical approach based on autocorrelation to relate genomic nucleosome organization to gene regulation.Results: We found that nucleosome organization in gene promoters can be well described by autocorrelation transformation. Some promoters show obvious periods in their nucleosome organization, while others have no clear periodicity. The genes with periodic nucleosome organization in promoters tend to be lower expressed than the genes without periodic nucleosome organization. These suggest that regular organization of nucleosomes plays a critical role in gene regulation. To quantitatively associate nucleosome organization and gene expression, we predicted gene expression solely based on nucleosome status and found that nucleosome status accounts for ∼25% of the observed gene expression variability. Furthermore, we explored the underlying forces that maintain the periodicity in nucleosome organization, namely intrinsic (i.e. DNA sequence) and extrinsic forces (i.e. chromatin remodeling factors). We found that the extrinsic factors play a critical role in maintaining the periodic nucleosome organization.Contact: jiang.qian@jhmi.eduSupplementary information: Supplementary data are available at Bioinformatics online.
Integrative analysis of transcriptomic and proteomic data of Desulfovibrio vulgaris: a non-linear model to predict abundance of undetected proteins
Authors: Torres-García Wandaliz , Zhang Weiwen , Runger George C. , Johnson Roger H. , Meldrum Deirdre R. 
Published: May 2009

Abstract
Motivation: Gene expression profiling technologies can generally produce mRNA abundance data for all genes in a genome. A dearth of proteomic data persists because identification range and sensitivity of proteomic measurements lag behind those of transcriptomic measurements. Using partial proteomic data, it is likely that integrative transcriptomic and proteomic analysis may introduce significant bias. Developing methodologies to accurately estimate missing proteomic data will allow better integration of transcriptomic and proteomic datasets and provide deeper insight into metabolic mechanisms underlying complex biological systems.
Results: In this study, we present a non-linear data-driven model to predict abundance for undetected proteins using two independent datasets of cognate transcriptomic and proteomic data collected from Desulfovibrio vulgaris. We use stochastic gradient boosted trees (GBT) to uncover possible non-linear relationships between transcriptomic and proteomic data, and to predict protein abundance for the proteins not experimentally detected based on relevant predictors such as mRNA abundance, cellular role, molecular weight, sequence length, protein length, guanine-cytosine (GC) content and triple codon counts. Initially, we constructed a GBT model using all possible variables to assess their relative importance and characterize the behavior of the predictive model. A strong plateau effect in the regions of high mRNA values and sparse data occurred in this model. Hence, we removed genes in those areas based on thresholds estimated from the partial dependency plots where this behavior was captured. At this stage, only the strongest predictors of protein abundance were retained to reduce the complexity of the GBT model. After removing genes in the plateau region, mRNA abundance, main cellular functional categories and few triple codon counts emerged as the top-ranked predictors of protein abundance. We then created a new tuned GBT model using the five most significant predictors. The construction of our non-linear model consists of a set of serial regression trees models with implicit strength in variable selection. The model provides variable relative importance measures using as a criterion mean square error. The results showed that coefficients of determination for our nonlinear models ranged from 0.393 to 0.582 in both datasets, providing better results than linear regression used in the past. We evaluated the validity of this non-linear model using biological information of operons, regulons and pathways, and the results demonstrated that the coefficients of variation of estimated protein abundance values within operons, regulons or pathways are indeed smaller than those for random groups of proteins.
Contact: weiwen.zhang@asu.edu; george.runger@asu.edu
Supplementary Information: Supplementary data are available at Bioinformatics online.
A two-stage biomass model for Irish Sea herring allowing for additional variance in the recruitment index caused by mixing of stocks
Authors: Roel Beatriz A. , De Oliveira José A. A. , Beggs Steven 
Published: May 2009

Abstract
Roel, B. A., De Oliveira, J. A. A., and Beggs, S. 2009. A two-stage biomass model for Irish Sea herring allowing for additional variance in the recruitment index caused by mixing of stocks. – ICES Journal of Marine Science, 66: 1808–1813. An assessment of Irish Sea (ICES Division VIIa, North) herring using a two-stage biomass model was undertaken given lack of internal consistency in the survey age-disaggregated index. The model constrains recruitment variability and reduces the number of catchability parameters to address concerns of overparameterization. An extra parameter was incorporated to estimate the component of the variance resulting from the mixing of Celtic Sea and Irish Sea juvenile herring in the survey area. This additional variance, although imprecise, represented a large part of the total variance associated with the recruit survey data. More information on the variability of Irish Sea herring recruitment is required to estimate additional variance in the context of the two-stage biomass model presented. Limitations in the age-composition data and potential interannual variation in the selection pattern of the fishery favour an assessment method, such as the two-stage biomass method, which is based on a simplified age structure and does not require separability assumptions.
Parental Worries and Beliefs About Abdominal Pain
Authors: van Tilburg Miranda AL , Chitkara Denesh K , Palsson Olafur S , Levy Rona L , Whitehead William E 
Published: May 2009

ABSTRACTBackground:Solicitous parental responses to stomachaches may perpetuate chronic abdominal pain in children. Discussing these issues in clinical practice is difficult because parents feel misunderstood and blamed for their child's pain. Focusing on parental worries and beliefs that motivate solicitous responses may be better accepted.Objectives:Our aim was to determine parental fears, worries, and beliefs about their child's chronic abdominal pain that influence parental responses to child's pain.Materials and Methods:In 2 studies, a large online sample and a smaller community sample consisting of parents with children who have abdominal pain, we developed and evaluated a self‐report questionnaire to assess parental Worries and Beliefs about Abdominal Pain (WAP).Results:Principal component analysis identified 4 subscales: “pain is real,” “desire for care,” “worry about coping,” and “exacerbating factors.” The WAP is easily understood and possesses adequate initial reliability (Cronbach α = 0.7–0.9). It shows good initial validity (ie, families who consulted a physician for their child's pain scored higher on the WAP than families who did not consult a physician and the WAP correlates with parental reactions to the child's pain).Conclusions:Discussing parents' fears and worries about their children's chronic abdominal pain may facilitate discussions of social learning of gastrointestinal illness behavior.
Trends in Nonalcoholic Fatty Liver Disease–related Hospitalizations in US Children, Adolescents, and Young Adults
Authors: Koebnick Corinna , Getahun Darios , Reynolds Kristi , Coleman Karen J , Porter Amy H , Lawrence Jean M , Punyanitya Mark , Quinn Virginia P , Jacobsen Steven J 
Published: May 2009

ABSTRACTObjective:To investigate temporal trends of nonalcoholic fatty liver disease (NAFLD) and obesity among hospitalized US children, adolescents, and young adults over the past 2 decades and to examine potential sex disparities in NAFLD hospitalizations.Methods:Hospitalization discharges with NAFLD or obesity were identified among children and young adults (6–25 years, weighted n = 91,687,413) from the 1986 to 2006 National Hospital Discharge Survey data. Age‐ and sex‐specific rates and trends in hospitalizations with NAFLD and obesity were estimated. Rates were standardized to age distribution of the 2000 US Census population. Sex disparities were examined for the most recent period 2004 to 2006 (weighted n = 12,969,532).Results:Between 1986 to 1988 and 2004 to 2006, hospitalizations with NAFLD diagnosis increased from 0.9 to 4.3/100,000 population (P < 0.001). During the same time, hospitalizations with a diagnosis of obesity increased from 35.5 to 114.7/100,000 population (P < 0.001). During 2004 to 2006, hospitalization rates with a diagnosis of NAFLD were higher among females than among males (5.9 vs 2.7/100,000 population, P < 0.001), as were hospitalizations with a diagnosis of obesity (140.8 vs 61.5/100,000 population, P < 0.001). Obesity and diabetes were reported in 43.3% and 31.9%, respectively, of discharges with NAFLD.Conclusion:The prevalence of NAFLD among young hospitalized patients increased in the past 2 decades, paralleling obesity‐related hospitalizations. This could be a consequence of the obesity epidemic or of increased screening for liver disease.
Evolving Concepts of the Pathogenesis of Irritable Bowel Syndrome: To Treat the Brain or the Gut?
Authors: Camilleri Michael 
Published: May 2009

ABSTRACTRecent in‐depth studies of irritable bowel syndrome (IBS) that assessed multiple physiological endpoints in large patient cohorts confirm evidence of abnormal motility, sensation, and psychosocial disturbances. However, the proportion with hypersensitivity has dropped from the time of the original claim that it is a “biological marker” of IBS. Discomfort thresholds in male and female IBS patients normalize over time, whereas IBS symptoms persist, and increased colonic sensitivity in IBS is strongly influenced by a psychological tendency to report pain and urge rather than increased neurosensory sensitivity. The objectives of this article are to review the classical pathophysiology of IBS, the putative roles of infection, inflammation, and bacterial flora; consider mimics of IBS; assess the evidence that IBS is a serotonergic disease; evaluate recent advances in membrane biology and neuroscience related to IBS; consider genetic association with IBS and its endophenotype; and discuss whether to treat the gut or the brain.
New Frontiers in the Treatment of Hirschsprung Disease
Authors: Thapar Nikhil 
Published: May 2009

ABSTRACTBackground:Hirschsprung disease (HSCR) is the most common identifiable developmental disorder of the enteric nervous system (ENS), characterized by a failure of its formation in a variable segment of distal bowel. Currently available surgical therapies for HSCR, although lifesaving, are associated with an unsatisfactory long‐term prognosis for many, highlighting the need for newer curative therapies. On the basis of our understanding of ENS development, 1 potential therapeutic tool relates to the cells that gave rise to the ENS during embryogenesis. Data from initial animal studies suggested that such “ENS stem cells” persist within postnatal gut, from which they can be harvested and used to replenish absent nerves within experimental models of gut aganglionosis. More recently, it has been shown that ENS stem cells can also be isolated from human postnatal gut. The early human studies, however, used full‐thickness gut tissue obtained at surgery to generate ENS stem cells, which in clinical terms poses practical problems. In an effort to directly address such obstacles our latest research has shown that ENS stem cells can also be harvested from gut mucosal biopsies obtained at routine endoscopy. Such gut mucosal ENS stem cells are capable of appropriately and fully colonizing aganglionic gut tissue, including from HSCR, to generate components of a mature and functional ENS.Conclusions:Current evidence suggests that transplantation of ENS stem cells, sourced from easily accessible postnatal gut, could be a viable alternative treatment for HSCR and ultimately for a number of other congenital or acquired ENS disorders.