Discriminative and informative features for biomolecular text mining with ensemble feature selection

Motivation: In the field of biomolecular text mining, black box behavior of machine learning systems currently limits understanding of the true nature of the predictions. However, feature selection (FS) is capable of identifying the most relevant features in any supervised learning setting, providing insight into the specific properties of the classification algorithm. This allows us to build more accurate classifiers while at the same time bridging the gap between the black box behavior and the end-user who has to interpret the results

Module networks revisited: computational assessment and prioritization of model predictions

The solution of high-dimensional inference and prediction problems in computational biology is almost always a compromise between mathematical theory and practical constraints such as limited computational resources. As time progresses, computational power increases but well-established inference methods often remain locked in their initial suboptimal solution. We revisit the approach of Segal et al. (2003) to infer regulatory modules and their condition-specific regulators from gene expression data. In contrast to their direct optimization-based solution we use a more representative centroid-like solution extracted from an ensemble of possible statistical models to explain the data. The ensemble method automatically selects a subset of most informative genes and builds a quantitatively better model for them. Genes which cluster together in the majority of models produce functionally more coherent modules. Regulators which are consistently assigned to a module are more often supported by literature, but a single model always contains many regulator assignments not supported by the ensemble. Reliably detecting condition-specific or combinatorial regulation is particularly hard in a single optimum but can be achieved using ensemble averaging.Comment: 8 pages REVTeX, 6 figure

Oidium neolycopersici: Intra-specific variability inferred from AFLP analysis and relationship with closely related powdery mildew fungi infecting various plant species

Previous works indicated a considerable variation in the pathogenicity, virulence, and host range of Oidium neolycopersici isolates causing tomato powdery mildew epidemics in many parts of the world. In this study, rDNA internal transcribed spacer (ITS) sequences, and amplified fragment length polymorphism (AFLP) patterns were analyzed in 17 O. neolycopersici samples collected in Europe, North America, and Japan, including those which overcame some of the tomato major resistance genes. The ITS sequences were identical in all 10 samples tested and were also identical to ITS sequences of eight previously studied O. neolycopersici specimens. The AFLP analysis revealed a high genetic diversity in O. neolycopersici and indicated that all 17 samples represented different genotypes. This might suggest the existence of either a yet unrevealed sexual reproduction or other genetic mechanisms that maintain a high genetic variability in O. neolycopersici. No clear correlation was found between the virulence and the AFLP patterns of the O. neolycopersici isolates studied. The relationship between O. neolycopersici and powdery mildew anamorphs infecting Aquilegia vulgaris, Chelidonium majus, Passiflora caerulea, and Sedum alboroseum was also investigated. These anamorphs are morphologically indistinguishable from and phylogenetically closely related to O. neolycopersici. The cross-inoculation tests and the analyses of ITS sequences and AFLP patterns jointly indicated that the powdery mildew anamorphs collected from the above mentioned plant species all represent distinct, but closely related species according to the phylogenetic species recognition. All these species were pathogenic only to their original host plant species, except O. neolycopersici which infected S. alboroseum, tobacco, petunia, and Arabidopsis thaliana, in addition to tomato, in cross-inoculation tests. This is the first genome-wide study that investigates the relationships among powdery mildews that are closely related based on ITS sequences and morphology. The results indicate that morphologically indistinguishable powdery mildews that differed in only one to five single nucleotide positions in their ITS region are to be considered as different taxa with distinct host ranges

Validating module network learning algorithms using simulated data

In recent years, several authors have used probabilistic graphical models to learn expression modules and their regulatory programs from gene expression data. Here, we demonstrate the use of the synthetic data generator SynTReN for the purpose of testing and comparing module network learning algorithms. We introduce a software package for learning module networks, called LeMoNe, which incorporates a novel strategy for learning regulatory programs. Novelties include the use of a bottom-up Bayesian hierarchical clustering to construct the regulatory programs, and the use of a conditional entropy measure to assign regulators to the regulation program nodes. Using SynTReN data, we test the performance of LeMoNe in a completely controlled situation and assess the effect of the methodological changes we made with respect to an existing software package, namely Genomica. Additionally, we assess the effect of various parameters, such as the size of the data set and the amount of noise, on the inference performance. Overall, application of Genomica and LeMoNe to simulated data sets gave comparable results. However, LeMoNe offers some advantages, one of them being that the learning process is considerably faster for larger data sets. Additionally, we show that the location of the regulators in the LeMoNe regulation programs and their conditional entropy may be used to prioritize regulators for functional validation, and that the combination of the bottom-up clustering strategy with the conditional entropy-based assignment of regulators improves the handling of missing or hidden regulators.Comment: 13 pages, 6 figures + 2 pages, 2 figures supplementary informatio

Cytoplasmic continuity revisited: closure of septa of the filamentous fungus Schizophyllum commune in response to environmental conditions

Background: Mycelia of higher fungi consist of interconnected hyphae that are compartmentalized by septa. These septa contain large pores that allow streaming of cytoplasm and even organelles. The cytoplasm of such mycelia is therefore considered to be continuous.Methodology/Principal Findings: Here, we show by laser dissection that septa of Schizophyllum commune can be closed depending on the environmental conditions. The most apical septum of growing hyphae was open when this basidiomycete was grown in minimal medium with glucose as a carbon source. In contrast, the second and the third septum were closed in more than 50% and 90% of the cases, respectively. Interestingly, only 24 and 37% of these septa were closed when hyphae were growing in the absence of glucose. Whether a septum was open or closed also depended on physical conditions of the environment or the presence of toxic agents. The first septum closed when hyphae were exposed to high temperature, to hypertonic conditions, or to the antibiotic nourseothricin. In the case of high temperature, septa opened again when the mycelium was placed back to the normal growth temperature.Conclusions/Significance: Taken together, it is concluded that the septal pores of S. commune are dynamic structures that open or close depending on the environmental conditions. Our findings imply that the cytoplasm in the mycelium of a higher fungus is not continuous perse

Aesthetics and literature : a problematic relation?

The paper argues that there is a proper place for literature within aesthetics but that care must be taken in identifying just what the relation is. In characterising aesthetic pleasure associated with literature it is all too easy to fall into reductive accounts, for example, of literature as merely "fine writing". Belleslettrist or formalistic accounts of literature are rejected, as are two other kinds of reduction, to pure meaning properties and to a kind of narrative realism. The idea is developed that literature-both poetry and prose fiction-invites its own distinctive kind of aesthetic appreciation which far from being at odds with critical practice, in fact chimes well with it

Transcriptomic analysis of the poultry red mite, Dermanyssus gallinae, across all stages of the lifecycle

Acknowledgements Thanks go to the Centre for Genomic Research (CGR) at the University of Liverpool performing the TruSeq RNA-seq analysis and to our local layer farmers for their continued support and provision of mite material. Funding The authors gratefully acknowledge funding for this project from BBRSC (grant reference BB/J01513X/1), Zoetis and Akita Co. Ltd. and The British Egg Marketing Board Trust.Peer reviewedPublisher PD

Multi-Target Prediction: A Unifying View on Problems and Methods

Multi-target prediction (MTP) is concerned with the simultaneous prediction of multiple target variables of diverse type. Due to its enormous application potential, it has developed into an active and rapidly expanding research field that combines several subfields of machine learning, including multivariate regression, multi-label classification, multi-task learning, dyadic prediction, zero-shot learning, network inference, and matrix completion. In this paper, we present a unifying view on MTP problems and methods. First, we formally discuss commonalities and differences between existing MTP problems. To this end, we introduce a general framework that covers the above subfields as special cases. As a second contribution, we provide a structured overview of MTP methods. This is accomplished by identifying a number of key properties, which distinguish such methods and determine their suitability for different types of problems. Finally, we also discuss a few challenges for future research