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Densification events in time-evolving networks refer to instants in which the network density, that is, the number of edges, is substantially larger than in the remaining. These events can occur at a global level, involving the majority of the nodes in the network, or at a local level involving only a subset of nodes.While global densification events affect the overall structure of the network, the same does not hold in local densification events, which may remain undetectable by the existing detection methods. In order to address this issue, we propose WINdowed TENsor decomposition for Densification Event Detection (WINTENDED) for the detection and characterization of both global and local densification events. Our method combines a sliding window decomposition with statistical tools to capture the local dynamics of the network and automatically find the irregular behaviours. According to our experimental evaluation, WINTENDED is able to spot global densification events at least as accurately as its competitors, while also being able to find local densification events, on the contrary to its competitors. [ABSTRACT FROM AUTHOR]

Widespread use of herbicides results in the global increase in weed resistance. The rotational use of herbicides according to their modes of action (MoAs) and discovery of novel phytotoxic molecules are the two strategies used against the weed resistance. Herein, Random Forest modeling was used to build predictive models and establish comprehensive characterization of structure–activity relationships underlying herbicide classifications according to their MoAs and weed selectivity. By combining the predictive models with herbicide-likeness rules defined by selected molecular features (numbers of H-bond acceptors and donors, logP, topological and relative polar surface area, and net charge), the virtual stepwise screening platform is proposed for characterization of small weight molecules for their phytotoxic properties. The screening cascade was applied on the data set of phytotoxic natural products. The obtained results may be valuable for refinement of herbicide rotational program as well as for discovery of novel herbicides primarily among natural products as a source for molecules of novel structures and novel modes of action and translocation profiles as compared with the synthetic compounds. [ABSTRACT FROM AUTHOR]

Genes with similar roles in the cell cluster on chromosomes, thus benefiting from coordinated regulation. This allows gene function to be inferred by transferring annotations from genomic neighbors, following the guilt-by-association principle. We performed a systematic search for co-occurrence of >1000 gene functions in genomic neighborhoods across 1669 prokaryotic, 49 fungal and 80 metazoan genomes, revealing prevalent patterns that cannot be explained by clustering of functionally similar genes. It is a very common occurrence that pairs of dissimilar gene functions – corresponding to semantically distant Gene Ontology terms – are significantly co-located on chromosomes. These neighborhood associations are often as conserved across genomes as the known associations between similar functions, suggesting selective benefits from clustering of certain diverse functions, which may conceivably play complementary roles in the cell. We propose a simple encoding of chromosomal gene order, the neighborhood function profiles (NFP), which draws on diverse gene clustering patterns to predict gene function and phenotype. NFPs yield a 26–46% increase in predictive power over state-of-the-art approaches that propagate function across neighborhoods, thus providing hundreds of novel, high-confidence gene function inferences per genome. Furthermore, we demonstrate that copy number-neutral structural variation that shapes gene function distribution across chromosomes can predict phenotype of individuals from their genome sequence. [ABSTRACT FROM AUTHOR]

One important problem occurring in redescription mining is a very large number of produced redescriptions. This makes analyses time consuming and generally difficult. We present the targeted and contextual redescription set exploration, realized through the tool InterSet. The main purpose of the tool is to derive additional knowledge from the redescription set which allows exploring parts of redescription set of interest and examining redescriptions individually or in the broader context, with the aim of increasing overall understandability. InterSet allows relating, grouping redescriptions, observing distributions of various redescription properties and selecting the appropriate subsets for further, detailed study. This allows gaining knowledge about the underlying data, help in forming, understanding, supporting research hypothesis or assists in understanding one or more redescriptions of interest. The tool provides three different, fully connected interaction modes based on: (1) similarity of entity occurrence in redescription support sets, (2) attribute co-occurrence in redescriptions and (3) redescription quality measures. Additionally, it allows exploration of relations between different redescriptions by creating a graph visualization that includes the top k-shortest paths containing selected redescriptions. On the individual redescription level, it allows studying value distributions of described entities, for a given set of attributes. [ABSTRACT FROM AUTHOR]

In this work, we present a redescription mining algorithm that uses Random Forest of Predictive Clustering Trees (RFPCTs) for generating and iteratively improving a set of redescriptions. The approach uses information about element membership in different queries, generated from a single constructed PCT, to explore redescription space, while queries obtained from the Random Forest of PCTs increase candidate diversity. The approach is able to produce highly accurate, statistically significant redescriptions described by Boolean, nominal or numerical attributes. As opposed to current tree-based approaches that use multi-class or binary classification, we explore the benefits of using multi-label classification and multi-target regression to create redescriptions. Major benefit of the approach, compared to other state of the art solutions, is that it does not require specifying minimal threshold on redescription accuracy to obtain highly accurate, optimized set of redescriptions. The process of Random Forest based augmentation and different modes of redescription set creation are evaluated on three datasets with different properties. We use the same datasets to compare the performance of our algorithm to state of the art redescription mining approaches. [ABSTRACT FROM AUTHOR]

Motivated by recent financial crises, significant research efforts have been put into studying contagion effects and herding behaviour in financial markets. Much less has been said regarding the influence of financial news on financial markets. We propose a novel measure of collective behaviour based on financial news on the Web, the News Cohesiveness Index (NCI), and we demonstrate that the index can be used as a financial market volatility indicator. We evaluate the NCI using financial documents from large Web news sources on a daily basis from October 2011 to July 2013 and analyse the interplay between financial markets and finance-related news. We hypothesise that strong cohesion in financial news reflects movements in the financial markets. Our results indicate that cohesiveness in financial news is highly correlated with and driven by volatility in financial markets. [ABSTRACT FROM AUTHOR]