bluuebunny/crossref_metadata_2025_split huggingface dataset:
A fully Bayesian model to cluster gene-expression profiles
Authors: Vogl C. , Sanchez-Cabo F. , Stocker G. , Hubbard S. , Wolkenhauer O. , Trajanoski Z. 
Published: October 2005

Abstract
Motivation: With cDNA or oligonucleotide chips, gene-expression levels of essentially all genes in a genome can be simultaneously monitored over a time-course or under different experimental conditions. After proper normalization of the data, genes are often classified into co-expressed classes (clusters) to identify subgroups of genes that share common regulatory elements, a common function or a common cellular origin. With most methods, e.g. k-means, the number of clusters needs to be specified in advance; results depend strongly on this choice. Even with likelihood-based methods, estimation of this number is difficult. Furthermore, missing values often cause problems and lead to the loss of data.
Results: We propose a fully probabilistic Bayesian model to cluster gene-expression profiles. The number of classes does not need to be specified in advance; instead it is adjusted dynamically using a Reversible Jump Markov Chain Monte Carlo sampler. Imputation of missing values is integrated into the model. With simulations, we determined the speed of convergence of the sampler as well as the accuracy of the inferred variables. Results were compared with the widely used k-means algorithm. With our method, biologically related co-expressed genes could be identified in a yeast transcriptome dataset, even when some values were missing.
Availability: The code is available at http://genome.tugraz.at/BayesianClustering/
Contact: claus.vogl@vu-wien.ac.at
Supplementary information: The supplementary material is available at http://genome.tugraz.at/BayesianClustering/
Fusing microarray experiments with multivariate regression
Authors: Gilks Walter R. , Tom Brian D. M. , Brazma Alvis 
Published: October 2005

Abstract
Motivation: It is widely acknowledged that microarray data are subject to high noise levels and results are often platform dependent. Therefore, microarray experiments should be replicated several times and in several laboratories before the results can be relied upon. To make the best use of such extensive datasets, methods for microarray data fusion are required. Ideally, the fused data should distil important aspects of the data while suppressing unwanted sources of variation and be amenable to further informal and formal methods of analysis. Also, the variability in the quality of experimentation should be taken into account.
Results: We present such an approach to data fusion, based on multivariate regression. We apply our methodology to data from a previous study on cell-cycle control in Schizosaccharomyces pombe.
Availability: The algorithm implemented in R is freely available from the authors on request.
Contact: wally.gilks@mrc-bsu.cam.ac.uk
A Gamma mixture model better accounts for among site rate heterogeneity
Authors: Mayrose Itay , Friedman Nir , Pupko Tal 
Published: October 2005

Abstract
Motivation: Variation of substitution rates across nucleotide and amino acid sites has long been recognized as a characteristic of molecular sequence evolution. Evolutionary models that account for this rate heterogeneity usually use a gamma density function to model the rate distribution across sites. This density function, however, may not fit real datasets, especially when there is a multimodal distribution of rates. Here, we present a novel evolutionary model based on a mixture of gamma density functions. This model better describes the among-site rate variation characteristic of molecular sequence evolution. The use of this model may improve the accuracy of various phylogenetic methods, such as reconstructing phylogenetic trees, dating divergence events, inferring ancestral sequences and detecting conserved sites in proteins.
Results: Using diverse sets of protein sequences we show that the gamma mixture model better describes the stochastic process underlying protein evolution. We show that the proposed gamma mixture model fits protein datasets significantly better than the single-gamma model in 9 out of 10 datasets tested. We further show that using the gamma mixture model improves the accuracy of model-based prediction of conserved residues in proteins.
Availability: C++ source codes are available from the authors upon request.
Contact: talp@post.tau.ac.il
Computing recombination networks from binary sequences
Authors: Huson Daniel H. , Kloepper Tobias H. 
Published: October 2005

Abstract
Motivation: Phylogenetic networks are becoming an important tool in molecular evolution, as the evolutionary role of reticulate events, such as hybridization, horizontal gene transfer and recombination, is becoming more evident, and as the available data is dramatically increasing in quantity and quality.
Results: This paper addresses the problem of computing a most parsimonious recombination network for an alignment of binary sequences that are assumed to have arisen under the ‘infinite sites’ model of evolution with recombinations. Using the concept of a splits network as the underlying datastructure, this paper shows how a recent method designed for the computation of hybridization networks can be extended to also compute recombination networks. A robust implementation of the approach is provided and is illustrated using a number of real biological datasets.
Availability: Our implementation of this approach is freely available as part of the SplitsTree4 software, downloadable from www.splitstree.org
Contact: huson@informatik.uni-tuebingen.de
A quadratic programming approach for decomposing steady-state metabolic flux distributions onto elementary modes
Authors: Schwartz Jean-Marc , Kanehisa Minoru 
Published: October 2005

Abstract
Motivation: It is known that steady-state flux distributions in metabolic networks can be expressed as non-negative combinations of elementary modes. However, little understanding has been achieved so far in how individual elementary modes contribute to the reconstruction of actual physiological flux distributions.
Results: We introduce an approach for decomposing steady-state flux distributions onto elementary modes based on quadratic programming. The decomposition is performed so as to favour modes that are closest to the actual state of the system, i.e. most relevant for biological interpretation. As an illustration, an application of this approach to a model of yeast glycolysis is presented.
Availability: Software is available upon request from the authors.
Contact: jean@kuicr.kyoto-u.ac.jp
Estimating gene regulatory networks and protein–protein interactions of Saccharomyces cerevisiae from multiple genome-wide data
Authors: Nariai Naoki , Tamada Yoshinori , Imoto Seiya , Miyano Satoru 
Published: October 2005

Abstract
Motivation: Biological processes in cells are properly performed by gene regulations, signal transductions and interactions between proteins. To understand such molecular networks, we propose a statistical method to estimate gene regulatory networks and protein–protein interaction networks simultaneously from DNA microarray data, protein–protein interaction data and other genome-wide data.
Results: We unify Bayesian networks and Markov networks for estimating gene regulatory networks and protein–protein interaction networks according to the reliability of each biological information source. Through the simultaneous construction of gene regulatory networks and protein–protein interaction networks of Saccharomyces cerevisiae cell cycle, we predict the role of several genes whose functions are currently unknown. By using our probabilistic model, we can detect false positives of high-throughput data, such as yeast two-hybrid data. In a genome-wide experiment, we find possible gene regulatory relationships and protein–protein interactions between large protein complexes that underlie complex regulatory mechanisms of biological processes.
Contact: nariai@ims.u-tokyo.ac.jp
A probabilistic model for mining implicit ‘chemical compound–gene’ relations from literature
Authors: Zhu Shanfeng , Okuno Yasushi , Tsujimoto Gozoh , Mamitsuka Hiroshi 
Published: October 2005

Abstract
Motivation: The importance of chemical compounds has been emphasized more in molecular biology, and ‘chemical genomics’ has attracted a great deal of attention in recent years. Thus an important issue in current molecular biology is to identify biological-related chemical compounds (more specifically, drugs) and genes. Co-occurrence of biological entities in the literature is a simple, comprehensive and popular technique to find the association of these entities. Our focus is to mine implicit ‘chemical compound and gene’ relations from the co-occurrence in the literature.
Results: We propose a probabilistic model, called the mixture aspect model (MAM), and an algorithm for estimating its parameters to efficiently handle different types of co-occurrence datasets at once. We examined the performance of our approach not only by a cross-validation using the data generated from the MEDLINE records but also by a test using an independent human-curated dataset of the relationships between chemical compounds and genes in the ChEBI database. We performed experimentation on three different types of co-occurrence datasets (i.e. compound–gene, gene–gene and compound–compound co-occurrences) in both cases. Experimental results have shown that MAM trained by all datasets outperformed any simple model trained by other combinations of datasets with the difference being statistically significant in all cases. In particular, we found that incorporating compound–compound co-occurrences is the most effective in improving the predictive performance. We finally computed the likelihoods of all unknown compound–gene (more specifically, drug–gene) pairs using our approach and selected the top 20 pairs according to the likelihoods. We validated them from biological, medical and pharmaceutical viewpoints.
Contact: mami@kuicr.kyoto-u.ac.jp
Expert knowledge without the expert: integrated analysis of gene expression and literature to derive active functional contexts
Authors: Küffner Robert , Fundel Katrin , Zimmer Ralf 
Published: October 2005

Abstract
Motivation: The interpretation of expression data without appropriate expert knowledge is difficult and usually limited to exploratory data analysis, such as clustering and detecting differentially regulated genes. However, comparing experimental results against manually compiled knowledge resources might limit or bias the perspective on the data. Thus, manual analysis by experts is required to obtain confident predictions about involved processes.
Results: We present an algorithm to simultaneously derive interpretations of expression measurements together with biological hypotheses from biomedical publications. It identifies active functional contexts (‘concepts’), i.e. gene clusters that exhibit both a significant gene expression as well as a coherent literature profile. Manual intervention by an expert in specifying prior knowledge is not required. The approach scales to realistic applications and does not rely on controlled vocabularies or pathway resources.
We validated our algorithm by analyzing a current juvenile arthritis dataset. A number of gene clusters and accompanying literature topics are identified as an interpretation of the data that coincide well with the phenotype and biological processes known to be involved in the disease. We demonstrate that generated clusters are both more sensitive and more specific than Gene Ontology categories detected on the same data. The method allows for in-depth investigation of subsets of genes, the associated literature topics and publications.
Availability: Supplementary data on clusters is available upon request.
Contact: Robert.Kueffner@bio.ifi.lmu.de
Optimizing the fluid management of ventilated patients with suspected hypovolaemia
Authors: Murch Philip 
Published: October 2005

ABSTRACTFluid management is a vital component of patient care within the critical care setting; it has a range of indications and applications, one of the most important being to maintain tissue perfusion and safeguard against single/multiple organ failure. Hypovolaemia is a commonly encountered condition within critical care and has the potential to jeopardize tissue perfusion and accelerate the risk of organ failure. In an attempt to optimize the fluid management of patients within the intensive therapy unit, this article outlines the development and implementation of a fluid prescription for ventilated patients with suspected hypovolaemia.
Low algal carbon content and its effect on the C : P stoichiometry of periphyton
Authors: FROST PAUL C. , HILLEBRAND HELMUT , KAHLERT MARIA 
Published: October 2005

Summary1. We examined the contribution of algal cells to periphytic organic carbon and assessed the effects of variable biomass composition on the carbon : phosphorus (C : P) ratio of periphyton. We compiled more than 5000 published and unpublished observations of periphytic carbon : chlorophyll a (C : Chl) ratios, an index of algal prevalence, from a variety of substrata collected from lake and low‐salinity coastal habitats. In addition, we converted estimates of algal biovolume into algal C to obtain an independent measure of cellular algal carbon in periphyton. This information was used in a model relating periphyton C : P ratio to algal cellular carbon, the algal C : P ratio, and the C : P ratio of non‐algal organic matter in periphyton.2. The mean C : Chl ratio of periphyton (405) was relatively high with values in >25% of the samples exceeding 500. On average, 8.4% of total periphyton C was accounted for by C in algal cells. Only 15% of samples were found to have more than 15% periphyton C in cellular algal carbon. Our model showed a nonlinear relationship between periphytic C : P ratios and the C : P ratio of algal cells in the periphyton when non‐algal organic matter was present. However, even at relatively low cellular algal C (<10% of total C), algal C : P ratios can strongly affect the C : P ratio of periphyton as a whole (i.e. algal cells plus other organic matter).3. The high C : Chl ratios and the low biovolume‐derived algal C of periphyton samples in our data set indicate that algal cells are typically a minor component of organic carbon in periphyton, However, this minor contribution would not preclude algal cellular stoichiometry from notably influencing periphyton C : P ratios.
Taxonomic and regional patterns in benthic macroinvertebrate elemental composition in streams
Authors: EVANS‐WHITE MICHELLE A. , STELZER ROBERT S. , LAMBERTI GARY A. 
Published: October 2005

Summary1. Ecological stoichiometry has been used to better understand dynamics in consumer growth and the role of consumer‐recycled nutrients because it focuses on more than one element. Most research has focused on pelagic rather than benthic consumers. Variation in elemental composition among benthic consumer taxa would suggest that taxa differ in their susceptibility to nutrient limitation or in their role in recycling nutrients.2. We collected benthic macroinvertebrates from streams in two regions (Indiana–Michigan and Wisconsin, U.S.A.) to examine taxonomic and regional variation in benthic macroinvertebrate body carbon (C), nitrogen (N), and phosphorus (P) concentrations and ratios.3. Elemental composition varied little within taxa common to both regions. In contrast, elemental composition differed greatly among taxa and appeared to be related to phylogeny. The elemental composition of macroinvertebrates clustered into three distinct groups: insects, mollusks, and crustaceans. To a lesser extent, insects and mollusks also differed in elemental composition among genera.4. Functional feeding groups (FFGs) differed in elemental composition, with predators having a higher N content than other groups. Substantial elemental imbalances between C and N were found between most primary consumers and their likely food sources, and the magnitude of the imbalance depended in part on the FFG.5. Our results support an assumption of most ecological stoichiometry models that, within a species, the elemental composition of aquatic invertebrates is relatively constant. Variation in elemental composition among taxa at various higher taxonomic levels suggests that susceptibility of stream invertebrates to nutrient limitation and their role in nutrient cycling will strongly depend on phylogeny.
Biased estimates of fitness consequences of brood size manipulation through correlated effects on natal dispersal
Authors: TINBERGEN JOOST M. 
Published: October 2005

SummaryDispersal of parents and offspring in relation to manipulated brood size were analysed in the great titParus major(L.) to study the potential confusion between dispersal and survival. The study area consisted of eight woodlots interspersed with nonbreeding habitat. The maximum distance between nestboxes was 10 km.The brood size of pairs with similar clutch size and laying date was manipulated in 3 years when chicks were 2 days old (1995, 1997 and 1998). Three nestlings were removed from one and added to another brood while a third was kept as a control. Offspring were measured, weighed and marked and breeding birds were captured and marked to allow dispersal estimates.For the offspring, dispersal was estimated as the distance between the natal nestbox and the nestbox of first breeding (natal dispersal distance) and, for the parents, as the distance between breeding boxes in two subsequent seasons (breeding dispersal distance).Natal dispersal distance was positively affected by brood size manipulation. This effect was more pronounced in males than in females. Breeding dispersal distance was not affected by manipulation.The practical consequence of this finding is that fitness estimates used to measure selection on brood size did depend on the spatial scale of the study area. For the Lauwersmeer population measured selection pressure changed from positive to stabilizing when I restricted the spatial scale of recovery. Other brood size manipulation experiments may suffer from similar biases in their fitness estimates.The biological consequence of this finding is that, if clutch size has a heritable component, local adaptation of clutch size will depend on the spatial heterogeneity of the habitat.
Effect of species interactions on landscape abundance patterns
Authors: BUCKLEY LAUREN B. , ROUGHGARDEN JOAN 
Published: October 2005

SummaryWe develop a process‐based bioenergetic model that incorporates population dynamics to predict the abundance trends for Anolis lizards along elevation gradients in the Lesser Antilles islands. Model outcomes are compared with empirical abundance trends on islands with and without species interactions and with differential topography.The bioenergetic model incorporating body size, thermal physiology and prey abundance qualitatively predicts how lizard abundance declines with elevation for solitary anole species. Empirical elevation trends on northern and southern one‐species islands have similar slopes but different intercepts at sea level. The greater anole abundance on the southern islands is predicted by the model when parameterized with the empirically observed higher insect abundance.Anole abundance patterns on two‐species islands diverge from the bioenergetic model predictions. One species is more abundant at sea level and the other more abundant at higher elevations compared with the trend for solitary anoles. Species interactions modify population responses to elevation gradients.
Quantifying the influence of sociality on population structure in bottlenose dolphins
Authors: LUSSEAU DAVID , WILSON BEN , HAMMOND PHILIP S. , GRELLIER KATE , DURBAN JOHN W. , PARSONS KIM M. , BARTON TIM R. , THOMPSON PAUL M. 
Published: October 2005

SummaryThe social structure of a population plays a key role in many aspects of its ecology and biology. It influences its genetic make‐up, the way diseases spread through it and the way animals exploit their environment. However, the description of social structure in nonprimate animals is receiving little attention because of the difficulty in abstracting social structure from the description of association patterns between individuals.Here we focus on recently developed analytical techniques that facilitate inference about social structure from association patterns. We apply them to the population of bottlenose dolphins residing along the Scottish east coast, to detect the presence of communities within this population and infer its social structure from the temporal variation in association patterns between individuals.Using network analytical techniques, we show that the population is composed of two social units with restricted interactions. These two units seem to be related to known differences in the ranging pattern of individuals. By examining social structuring at different spatial scales, we confirm that the identification of these two units is the result of genuine social affiliation and is not an artefact of their spatial distribution.We also show that the structure of this fission‐fusion society relies principally on short‐term casual acquaintances lasting a few days with a smaller proportion of associations lasting several years. These findings highlight how network analyses can be used to detect and understand the forces driving social organization of bottlenose dolphins and other social species.
Demographic vital rates determine the performance advantage of crop–wild hybrids in lettuce
Authors: HOOFTMAN DANNY A. P. , OOSTERMEIJER J. GERARD B. , JACOBS MIRJAM M. J. , DEN NIJS HANS C. M. 
Published: October 2005

Summary
Hybridization seems possible for many crop species after pollen transfer from crops to wild relatives in the surrounding vegetation. Subsequent introgression of crop‐specific traits into wild relatives could lead to invasive introgressants. This process has become a public concern following the introduction of genetically modified (GM) crops. Until now, few studies have used demographic vital rates to compare the performance of hybrids with their wild relatives.
We created second‐generation (S1 and BC1) hybrids between the non‐transgenic crop Lactuca sativa and its entirely cross‐fertile wild relative Lactuca serriola. Seeds of parents and hybrids were individually sown in field plots at three different locations. Next to germination and survival, we measured a range of single fitness components and morphological traits. We also compared observed phenotypes to phenotypes theoretically expected, according to different inheritance scenarios.
Phenotypes of both hybrid classes resembled L. serriola closely, and more than theoretically expected. However, demographic vital rates, i.e. germination and survival of hybrids were much higher than in L. serriola.
Our results indicate that hybrids between crop and wild Lactuca are phenotypically indistinguishable from the wild relative and thus will largely remain unnoticed when they occur. However, these hybrids could potentially become invasive because of substantial differences in vital rates and seeds returned per seed sown.
Synthesis and applications. A comparative study on single fitness components, such as seed production, alone would not have revealed the performance advantage of crop–wild hybrids in Lactuca. Therefore, studying demographic vital rates of hybrids and back‐crosses to test for long‐term consequences of hybridization should be part of any risk assessment of GM crops. Demographic vital rates are also important for the development of predictive modelling tools that can be employed to test the individual‐ and population‐level consequences of new‐to‐add traits.