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I describe two traditions of philosophical accounts of evidence: one characterizes the notion in terms of signs of success, the other characterizes the notion in terms of conditions of success. The best examples of the former rely on the probability calculus, and have the virtues of generality and theoretical simplicity. The best examples of the latter describe the features of evidence which scientists appeal to in practice, which include general features of methods, such as quality and relevance, and general features of evidence, such as patterns in data, concordance with other evidence, and believability of the evidence. Two infamous episodes from biomedical research help to illustrate these features. Philosophical characterization of these latter features-conditions of success-has the virtue of potential relevance to, and descriptive accuracy of, practices of experimental scientists. [ABSTRACT FROM AUTHOR]

High-throughput phenotyping projects in model organisms have the potential to improve our understanding of gene functions and their role in living organisms. We have developed a computational, knowledge-based approach to automatically infer gene functions from phenotypic manifestations and applied this approach to yeast (Saccharomyces cerevisiae), nematode worm (Caenorhabditis elegans), zebrafish (Danio rerio), fruitfly (Drosophila melanogaster) and mouse (Mus musculus) phenotypes. Our approach is based on the assumption that, if a mutation in a gene leads to a phenotypic abnormality in a process , then must have been involved in , either directly or indirectly. We systematically analyze recorded phenotypes in animal models using the formal definitions created for phenotype ontologies. We evaluate the validity of the inferred functions manually and by demonstrating a significant improvement in predicting genetic interactions and protein-protein interactions based on functional similarity. Our knowledge-based approach is generally applicable to phenotypes recorded in model organism databases, including phenotypes from large-scale, high throughput community projects whose primary mode of dissemination is direct publication on-line rather than in the literature. [ABSTRACT FROM AUTHOR]

Motivation: Large public repositories of gene expression measurements offer the opportunity to position a new experiment into the context of earlier studies. While previous methods rely on experimental annotation or global similarity of expression profiles across genes or gene sets, we compare experiments by measuring similarity based on an unsupervised, data-driven regulatory model around pre-specified genes of interest. Our experiment retrieval approach is novel in two conceptual respects: (i) targetable focus and interpretability: the analysis is targeted at regulatory relationships of genes that are relevant to the analyst or come from prior knowledge; (ii) regulatory model-based similarity measure: related experiments are retrieved based on the strength of inferred regulatory links between genes.Results: We learn a model for the regulation of specific genes from a data repository and exploit it to construct a similarity metric for an information retrieval task. We use the Fisher kernel, a rigorous similarity measure that typically has been applied to use generative models in discriminative classifiers. Results on human and plant microarray collections indicate that our method is able to substantially improve the retrieval of related experiments against standard methods. Furthermore, it allows the user to interpret biological conditions in terms of changes in link activity patterns. Our study of the osmotic stress network for Arabidopsis thaliana shows that the method successfully identifies relevant relationships around given key genes.Availability: The code (R) is available at http://research.ics.tkk.fi/mi/software.shtml.Contact: elisabeth.georgii@aalto.fi; jarkko.salojarvi@helsinki.fi; samuel.kaski@hiit.fiSupplementary Information: Supplementary data are available at Bioinformatics online. [ABSTRACT FROM AUTHOR]

Motivation: Recent developments in experimental methods facilitate increasingly larger signal transduction datasets. Two main approaches can be taken to derive a mathematical model from these data: training a network (obtained, e.g., from literature) to the data, or inferring the network from the data alone. Purely data-driven methods scale up poorly and have limited interpretability, whereas literature-constrained methods cannot deal with incomplete networks.Results: We present an efficient approach, implemented in the R package CNORfeeder, to integrate literature-constrained and data-driven methods to infer signalling networks from perturbation experiments. Our method extends a given network with links derived from the data via various inference methods, and uses information on physical interactions of proteins to guide and validate the integration of links. We apply CNORfeeder to a network of growth and inflammatory signalling. We obtain a model with superior data fit in the human liver cancer HepG2 and propose potential missing pathways.Availability: CNORfeeder is in the process of being submitted to Bioconductor and in the meantime available at www.cellnopt.org.Contact: saezrodriguez@ebi.ac.ukSupplementary information: Supplementary data are available at Bioinformatics online. [ABSTRACT FROM AUTHOR]

Gene expression index estimation is an essential step in analyzing multiple probe microarray data. Various modeling methods have been proposed in this area. Amidst all, a popular method proposed in Li and Wong (2001) is based on a multiplicative model, which is similar to the additive model discussed in Irizarry et al. (2003a) at the logarithm scale. Along this line, Hu et al. (2006) proposed data transformation to improve expression index estimation based on an ad hoc entropy criteria and naive grid search approach. In this work, we re-examined this problem using a new profile likelihood-based transformation estimation approach that is more statistically elegant and computationally efficient. We demonstrate the applicability of the proposed method using a benchmark Affymetrix U95A spiked-in experiment. Moreover, We introduced a new multivariate expression index and used the empirical study to shows its promise in terms of improving model fitting and power of detecting differential expression over the commonly used univariate expression index. As the other important content of the work, we discussed two generally encountered practical issues in application of gene expression index: normalization and summary statistic used for detecting differential expression. Our empirical study shows somewhat different findings from the MAQC project (MAQC, 2006). [ABSTRACT FROM AUTHOR]

Abstract: Researchers in developmental biology are increasingly recognizing the value of theoretical models in studies of morphogenesis. However, creating and testing realistic quantitative models for morphogenetic processes can be an extremely challenging task. The focus of this paper is on models for the mechanics of morphogenesis. Models for these problems often must include large changes in geometry, leading to highly nonlinear problems with the possibility of multiple solutions that must be sorted out using experimental data. Here, we illustrate our approach to these problems using the specific example of head fold formation in the early chick embryo. The interplay between experimental and theoretical results is emphasized throughout, as the model is gradually refined. Some of the limitations inherent in theoretical/computational modeling of biological systems are also discussed. [Copyright &y& Elsevier]

Motivation: Rapid advances in biomedical sciences and genetics have increased the pressure on drug development companies to promptly translate new knowledge into treatments for disease. Impelled by the demand and facilitated by technological progress, the number of compounds evaluated during the initial high-throughput screening (HTS) step of drug discovery process has steadily increased. As a highly automated large-scale process, HTS is prone to systematic error caused by various technological and environmental factors. A number of error correction methods have been designed to reduce the effect of systematic error in experimental HTS (Brideau et al., 2003; Carralot et al., 2012; Kevorkov and Makarenkov, 2005; Makarenkov et al., 2007; Malo et al., 2010). Despite their power to correct systematic error when it is present, the applicability of those methods in practice is limited by the fact that they can potentially introduce a bias when applied to unbiased data. We describe two new methods for eliminating systematic error from HTS data based on a prior knowledge of the error location. This information can be obtained using a specific version of the t-test or of the χ2 goodness-of-fit test as discussed in Dragiev et al. (2011). We will show that both new methods constitute an important improvement over the standard practice of not correcting for systematic error at all as well as over the B-score correction procedure (Brideau et al., 2003) which is widely used in the modern HTS. We will also suggest a more general data preprocessing framework where the new methods can be applied in combination with the Well Correction procedure (Makarenkov et al., 2007). Such a framework will allow for removing systematic biases affecting all plates of a given screen as well as those relative to some of its individual plates.Contact: makarenkov.vladimir@uqam.caSupplementary information: Supplementary data are available at Bioinformatics online. [ABSTRACT FROM PUBLISHER]

Abstract: The developmental response of insects to temperature is important in understanding the ecology of insect life histories. Temperature-dependent phenology models permit examination of the impacts of temperature on the geographical distributions, population dynamics and management of insects. The measurement of insect developmental, survival and reproductive responses to temperature poses practical challenges because of their modality, variability among individuals and high mortality near the lower and upper threshold temperatures. We address this challenge with an integrated approach to the design of experiments and analysis of data based on maximum likelihood. This approach expands, simplifies and unifies the analysis of laboratory data parameterizing the thermal responses of insects in particular and poikilotherms in general. This approach allows the use of censored observations (records of surviving individuals that have not completed development after a certain time) and accommodates observations from temperature transfer treatments in which individuals pass only a portion of their development at an extreme (near-threshold) temperature and are then placed in optimal conditions to complete their development with a higher rate of survival. Results obtained from this approach are directly applicable to individual-based modeling of insect development, survival and reproduction with respect to temperature. This approach makes possible the development of process-based phenology models that are based on optimal use of available information, and will aid in the development of powerful tools for analyzing eruptive insect population behavior and response to changing climatic conditions. [Copyright &y& Elsevier]

Abstract: A number of recent studies have shown that loop-design is more efficient than reference control design. Data analysis for loop-design microarray experiments is commonly undertaken using linear models and statistical tests. These techniques require specialized knowledge in statistical programming. However, limited loop-design web-based tools are available. We have developed the THEME (Tsing Hua Engine of Microarray Experiment) that exploits all necessary data analysis tools for loop-design microarray studies. THEME allows users to construct linear models and to apply multiple user-defined statistical tests of hypotheses for detection of DEG (differentially expressed genes). Users can modify entries of design matrix for experimental design as well as that of contrast matrix for statistical tests of hypotheses. The output of multiple user-defined statistical tests of hypotheses, DEG lists, can be cross-validated. The web platform provides data assessment and visualization tools that significantly assist users when evaluating the performance of microarray experimental procedures. THEME is also a MIAME (Minimal Information About a Microarray Experiment) compliant system, which enables users to export formatted files for GEO (Gene Expression Omnibus) submission. THEME offers comprehensive web services to biologists for data analysis of loop-design microarray experiments. This web-based resource is especially useful for core facility service as well as collaboration projects when researchers are not at the same site. Data analysis procedures, starting from uploading raw data files to retrieving DEG lists, can be flexibly operated with natural workflows. These features make THEME a reliable and powerful on-line system for data analysis of loop-design microarrays. The THEME server is available at http://metadb.bmes.nthu.edu.tw/theme/. [Copyright &y& Elsevier]

Abstract: Nanoindentation has become a common technique for measuring the hardness and elastic–plastic properties of materials, including coatings and thin films. In recent years, different nanoindenter instruments have been commercialised and used for this purpose. Each instrument is equipped with its own analysis software for the derivation of the hardness and reduced Young''s modulus from the raw data. These data are mostly analysed through the Oliver and Pharr method. In all cases, the calibration of compliance and area function is mandatory. The present work illustrates and describes a calibration procedure and an approach to raw data analysis carried out for six different nanoindentation instruments through several round-robin experiments. Three different indenters were used, Berkovich, cube corner, spherical, and three standardised reference samples were chosen, hard fused quartz, soft polycarbonate, and sapphire. It was clearly shown that the use of these common procedures consistently limited the hardness and reduced the Young''s modulus data spread compared to the same measurements performed using instrument-specific procedures. The following recommendations for nanoindentation calibration must be followed: (a) use only sharp indenters, (b) set an upper cut-off value for the penetration depth below which measurements must be considered unreliable, (c) perform nanoindentation measurements with limited thermal drift, (d) ensure that the load–displacement curves are as smooth as possible, (e) perform stiffness measurements specific to each instrument/indenter couple, (f) use Fq and Sa as calibration reference samples for stiffness and area function determination, (g) use a function, rather than a single value, for the stiffness and (h) adopt a unique protocol and software for raw data analysis in order to limit the data spread related to the instruments (i.e. the level of drift or noise, defects of a given probe) and to make the H and E r data intercomparable. [Copyright &y& Elsevier]

The article analyzes the use of mathematical models to interpret various biological observations and experiments. It mentions the importance of reconstructing the internal knowledge and functional regulation of organisms in the execution of biologically applicable model-supported data analysis. It relates several observations such as the desert ants' path integration and neuronal physiology of goldfish which indicate a wide-scope of potential of model-supported data analysis in life sciences.

Background and Purpose: Transient receptor potential ankyrin 1 (TRPA1) channels are expressed by primary afferent neurones and activated by irritant chemicals including allyl isothiocyanate (AITC). Here we investigated whether intracolonic AITC causes afferent input to the spinal cord and whether this response is modified by mild colitis, morphine or a TRPA1 channel blocker.Experimental Approach: One hour after intracolonic administration of AITC to female mice, afferent signalling was visualized by expression of c-Fos in laminae I-II(o) of the spinal dorsal horn at sacral segment S1. Mild colitis was induced by dextran sulphate sodium (DSS) added to drinking water for 1 week.Key Results: Relative to vehicle, AITC (2%) increased expression of c-Fos in the spinal cord. Following induction of mild colitis by DSS (2%), spinal c-Fos responses to AITC, but not vehicle, were augmented by 41%. Colonic inflammation was present (increased myeloperoxidase content and disease activity score), whereas colonic histology, locomotion, feeding and drinking remained unchanged. Morphine (10 mg.kg(-1)) or the TRPA1 channel blocker HC-030031 (300 mg.kg(-1)) inhibited the spinal c-Fos response to AITC, in control and DSS-pretreated animals, whereas the response to intracolonic capsaicin (5%) was blocked by morphine but not HC-030031.Conclusions and Implications: Activation of colonic TRPA1 channels is signalled to the spinal cord. Mild colitis enhanced this afferent input that, as it is sensitive to morphine, is most likely of a chemonociceptive nature. As several irritant chemicals can be present in chyme, TRPA1 channels may mediate several gastrointestinal pain conditions. [ABSTRACT FROM AUTHOR]

Abstract: Single molecule methods are becoming routine biophysical techniques for studying biological macromolecules. In mechanical unfolding of proteins, an externally applied force is used to induce the unfolding of individual protein molecules. Such experiments have revealed novel information that has significantly enhanced our understanding of the function and folding mechanisms of several types of proteins. To obtain information on the unfolding kinetics and the free energy landscape of the protein molecule from mechanical unfolding data, a Monte Carlo simulation based on a simple two-state kinetic model is often used. In this paper, we provide a detailed description of the procedure to perform such simulations and discuss the approximations and assumptions involved. We show that the appearance of the force versus extension curves from mechanical unfolding of proteins is affected by a variety of experimental parameters, such as the length of the protein polymer and the force constant of the cantilever. We also analyze the errors associated with different methods of data pooling and present a quantitative measure of how well the simulation results fit experimental data. These findings will be helpful in experimental design, artifact identification, and data analysis for single molecule studies of various proteins using the mechanical unfolding method. [Copyright &y& Elsevier]

Abstract: The European Modular Cultivation System (EMCS) on the ISS allows long-term biological experiments, e.g. on plants. Video cameras provide near real-time 2D images from these experiments. A method to obtain 3D coordinates and stereoscopic images from these 2D images has been developed and is described in this paper. The procedure was developed to enhance the data output of the MULTIGEN-1 experiment in 2007. One of the main objectives of the experiment was to study growth movements of the Arabidopsis plants and the effect of gravity on these. 3D data were important during parts of the experiment and the paper presents the method developed to acquire 3D data, the accuracy of the data, limitations to the technique and ways to improve the accuracy. Sequences of 3D data obtained from the MULTIGEN-1 experiment are used to illustrate the potential of this newfound capability of the EMCS. In the experiment setup, a positional depth accuracy of about ±0.4mm for relative object distances and an absolute depth accuracy of about ±1.4mm for time dependent phenomena was reached. The ability to both view biological specimens in 3D as well as obtaining quantitative 3D data added greatly to the scientific output of the MULTIGEN-1 experiment. The uses of the technique to other researchers and their experiments are discussed. [Copyright &y& Elsevier]

Continuous observations, such as reaction and run times, and binary observations, such as correct/incorrect responses, are recorded routinely in behavioral learning experiments. Although both types of performance measures are often recorded simultaneously, the two have not been used in combination to evaluate learning. We present a state-space model of learning in which the observation process has simultaneously recorded continuous and binary measures of performance. We use these performance measures simultaneously to estimate the model parameters and the unobserved cognitive state process by maximum likelihood using an approximate expectation maximization (EM) algorithm. We introduce the concept of a reaction-time curve and reformulate our previous definitions of the learning curve, the ideal observer curve, the learning trial and between-trial comparisons of performance in terms of the new model. We illustrate the properties of the new model in an analysis of a simulated learning experiment. In the simulated data analysis, simultaneous use of the two measures of performance provided more credible and accurate estimates of the learning than either measure analyzed separately. We also analyze two actual learning experiments in which the performance of rats and of monkeys was tracked across trials by simultaneously recorded reaction and run times and the correct and incorrect responses. In the analysis of the actual experiments, our algorithm gave a straightforward, efficient way to characterize learning by combining continuous and binary measures of performance. This analysis paradigm has implications for characterizing learning and for the more general problem of combining different data types to characterize the properties of a neural system. [ABSTRACT FROM AUTHOR]

The article presents a study that verifies that the rove beetle or Dolatia coriaria, Coleoptera: Staphylinidae, predates the western corn rootworm (WCR), Diabrotica virgifera virgifera LeConte, Coleoptera: Chrysomelidae. It states that several adult D. coriaria were collected from containers used for WCR. The study conducted two experiments, in which one involved neonate WCR larvae and the other involved 2nd instar WCR larvae. The experimental data were analyzed using the PROC GENMOD of SAS Institute Inc. The results showed that D. coriaria predation on WCR. D. coriaria is an important factor in reducing WCR colonies population.

The article presents a study regarding the importance and function of the RpoH to the Francisella tularensis heat-shock response. Details are presented regarding the results concerning the F. tularensis transcriptomic response to heat stress and the role of σ32 in heat-stress response. It infers that the RpoH protein in F. tularensis determines the genes responsible for the heat-shock response in a mammalian host.

Abstract: This study aimed at verifying the effects of phonophoresis associated with Arnica montana on the acute phase of an inflammatory muscle lesion. Forty Wistar male rats (300±50g), of which the Tibialis Anterior muscle was surgically lesioned, were divided into four groups (n =10 each): control group received no treatment; the ultrasound group (US) was treated in pulsed mode with 1-MHz frequency, 0.5W/cm2 intensity (spatial and temporal average – SATA), duty cycle of 1:2 (2ms on, 4ms off, 50%), time of application 3min per session, one session per day, for 3 days; the phonophoresis or ultrasound plus arnica (US+A) group was treated with arnica with the same US parameters plus arnica gel; and the arnica group (A) was submitted to massage with arnica gel, also for 3min, once a day, for 3 days. Treatment started 24h after the surgical lesion. On the 4th day after lesion creation, animals were sacrificed and sections of the lesioned, inflamed muscle were removed for quantitative (mononuclear and polymorphonuclear cell count) and qualitative histological analysis. Collected data from the 4 groups were statistically analyzed and the significance level set at p <0.05. Results show higher mononuclear cell density in all three treated groups with no significant difference between them, but values were significantly different (p <0.0001) when compared to control group’s. As to polymorphonuclear cell density, significant differences were found between control group (p =0.0134) and US, US+A and A groups; the arnica group presented lesser density of polymorphonuclear cells when compared (p =0.0134) to the other groups. No significant difference was found between US and US+A groups. While the massage with arnica gel proved to be an effective anti-inflammatory on acute muscle lesion in topic use, these results point to ineffectiveness of Arnica montana phonophoresis, US having seemingly checked or minimized its anti-inflammatory effect. [Copyright &y& Elsevier]

Using microarray experiments, we can model causal relationships of genes measured through mRNA expression levels. To this end, it is desirable to compare experiments of the system under complete interventions of some genes, such as by knock out of some genes, with experiments of the system under no interventions. However, it is expensive and difficult to conduct wet lab experiments of complete interventions of genes in a biological system. Thus, it will be helpful if we can discover promising causal relationships among genes with no interventions or incomplete interventions, such as by applying a treatment that has unknown effects to modeled genes, in order to identify promising genes to perturb in the system that can later be verified in wet laboratories. In this paper we use causal Bayesian networks to implement a causal discovery algorithm—the equivalence local implicit latent variable scoring method (EquLIM)—that identifies promising causal relationships even with a small dataset generated from no or incomplete interventions. We then apply EquLIM to analyze the five-gene-network data and compare EquLIM's predictions with true causal pairwise relationships between the genes. [ABSTRACT FROM AUTHOR]

Motivation: Public genomic databases, which are often used to guide genetic studies of human disease, are now being applied to genomic medicine through in silico integrative genomics. These databases, however, often lack tools for systematically determining the experimental origins of the data.Results: We introduce a new data provenance model that we have implemented in a public web application, BioQ, for assessing the reliability of the data by systematically tracing its experimental origins to the original subjects and biologics. BioQ allows investigators to both visualize data provenance as well as explore individual elements of experimental process flow using precise tools for detailed data exploration and documentation. It includes a number of human genetic variation databases such as the HapMap and 1000 Genomes projects.Availability and implementation: BioQ is freely available to the public at http://bioq.saclab.netContact: ssaccone@wustl.eduSupplementary information: Supplementary data are available at Bioinformatics online. [ABSTRACT FROM AUTHOR]

Studies using visual illusions to demonstrate a dissociation within the visual system can provide relevant and decisive data only if certain methodological points are taken into account. Although, our previous work (Stöttinger et al. in Exp Brain Res 202:88-97, ) followed these points, the task made use of only 2-D stimuli which may raise doubts concerning the nature of grasping in that experiment. We therefore replicated the study using a 3-D version of the empty space illusion. Consistent with the earlier study, that used 2-D stimuli, we found that grip aperture followed actual target size independent of illusory effects, while perceived length, as indicated by finger-thumb span, clearly was subject to the illusion. Therefore, the prior results cannot be due to the use of 2-D stimuli. Together, these two studies provide clear evidence for the perception versus action hypothesis. [ABSTRACT FROM AUTHOR]

Motivation: RNA sequencing is becoming a standard for expression profiling experiments and many tools have been developed in the past few years to analyze RNA-Seq data. Numerous ‘Bioconductor’ packages are available for next-generation sequencing data loading in R, e.g. ShortRead and Rsamtools as well as to perform differential gene expression analyses, e.g. DESeq and edgeR. However, the processing tasks lying in between these require the precise interplay of many Bioconductor packages, e.g. Biostrings, IRanges or external solutions are to be sought.Results: We developed ‘easyRNASeq’, an R package that simplifies the processing of RNA sequencing data, hiding the complex interplay of the required packages behind a single functionality.Availability: The package is implemented in R (as of version 2.15) and is available from Bioconductor (as of version 2.10) at the URL: http://bioconductor.org/packages/release/bioc/html/easyRNASeq.html, where installation and usage instructions can be found.Contact: delhomme@embl.de [ABSTRACT FROM AUTHOR]

Summary: Structural kinetic modeling (SKM) enables the analysis of dynamical properties of metabolic networks solely based on topological information and experimental data. Current SKM-based experiments are hampered by the time-intensive process of assigning model parameters and choosing appropriate sampling intervals for Monte-Carlo experiments. We introduce a toolbox for the automatic and efficient construction and evaluation of structural kinetic models (SK models). Quantitative and qualitative analyses of network stability properties are performed in an automated manner. We illustrate the model building and analysis process in detailed example scripts that provide toolbox implementations of previously published literature models.Availability: The source code is freely available for download at http://bioinformatics.uni-potsdam.de/projects/skm.Contact: girbig@mpimp-golm.mpg.de [ABSTRACT FROM AUTHOR]

Motivation: The genomic era in molecular biology has brought on a rapidly widening gap between the amount of sequence data and first-hand experimental characterization of proteins. Fortunately, the theory of evolution provides a simple solution: functional and structural information can be transferred between homologous proteins. Sequence similarity searching followed by k-nearest neighbor classification is the most widely used tool to predict the function or structure of anonymous gene products that come out of genome sequencing projects.Results: We present a novel word filter, suffix array neighborhood search (SANS), to identify protein sequence similarities in the range of 50–100% identity with sensitivity comparable to BLAST and 10 times the speed of USEARCH. In contrast to these previous approaches, the complexity of the search is proportional only to the length of the query sequence and independent of database size, enabling fast searching and functional annotation into the future despite rapidly expanding databases.Availability and implementation: The software is freely available to non-commercial users from our website http://ekhidna.biocenter.helsinki.fi/downloads/sans.Contact: liisa.holm@helsinki.fi. [ABSTRACT FROM AUTHOR]

Motivation: Gene expression assays allow for genome scale analyses of molecular biological mechanisms. State-of-the-art data analysis provides lists of involved genes, either by calculating significance levels of mRNA abundance or by Bayesian assessments of gene activity. A common problem of such approaches is the difficulty of interpreting the biological implication of the resulting gene lists. This lead to an increased interest in methods for inferring high-level biological information. A common approach for representing high level information is by inferring gene ontology (GO) terms which may be attributed to the expression data experiment.Results: This article proposes a probabilistic model for GO term inference. Modelling assumes that gene annotations to GO terms are available and gene involvement in an experiment is represented by a posterior probabilities over gene-specific indicator variables. Such probability measures result from many Bayesian approaches for expression data analysis. The proposed model combines these indicator probabilities in a probabilistic fashion and provides a probabilistic GO term assignment as a result. Experiments on synthetic and microarray data suggest that advantages of the proposed probabilistic GO term inference over statistical test-based approaches are in particular evident for sparsely annotated GO terms and in situations of large uncertainty about gene activity. Provided that appropriate annotations exist, the proposed approach is easily applied to inferring other high level assignments like pathways.Availability: Source code under GPL license is available from the author.Contact: peter.sykacek@boku.ac.at [ABSTRACT FROM AUTHOR]