Non deterministic Repairable Fault Trees for computing optimal repair strategy

In this paper, the Non deterministic Repairable Fault Tree (NdRFT) formalism is proposed: it allows to model failure modes of complex systems as well as their repair processes. The originality of this formalism with respect to other Fault Tree extensions is that it allows to face repair strategies optimization problems: in an NdRFT model, the decision on whether to start or not a given repair action is non deterministic, so that all the possibilities are left open. The formalism is rather powerful allowing to specify which failure events are observable, whether local repair or global repair can be applied, and the resources needed to start a repair action. The optimal repair strategy can then be computed by solving an optimization problem on a Markov Decision Process (MDP) derived from the NdRFT. A software framework is proposed in order to perform in automatic way the derivation of an MDP from a NdRFT model, and to deal with the solution of the MDP

An entropy heuristic to optimize decision diagrams for index-driven search in biological graph databases

Graphs are a widely used structure for knowledge representation. Their uses range from biochemical to biomedical applications and are recently involved in multi-omics analyses. A key computational task regarding graphs is the search of specific topologies contained in them. The task is known to be NP-complete, thus indexing techniques are applied for dealing with its complexity. In particular, techniques exploiting paths extracted from graphs have shown good performances in terms of time requirements, but they still suffer because of the relatively large size of the produced index. We applied decision diagrams (DDs) as index data structure showing a good reduction in the indexing size with respect to other approaches. Nevertheless, the size of a DD is dependent on its variable order. Because the search of an optimal order is an NP-complete task, variable order heuristics on DDs are applied by exploiting domain-specific information. Here, we propose a heuristic based on the information content of the labeled paths. Tests on well-studied biological benchmarks, which are an essential part of multi-omics graphs, show that the resultant size correlates with the information measure related to the paths and that the chosen order allows to effectively reduce the index size

An entropy heuristic to optimize decision diagrams for index-driven search in biological graph databases

Graphs are a widely used structure for knowledge representation. Their uses range from biochemical to biomedical applications and are recently involved in multi-omics analyses. A key computational task regarding graphs is the search of specific topologies contained in them. The task is known to be NP-complete, thus indexing techniques are applied for dealing with its complexity. In particular, techniques exploiting paths extracted from graphs have shown good performances in terms of time requirements, but they still suffer because of the relatively large size of the produced index. We applied decision diagrams (DDs) as index data structure showing a good reduction in the indexing size with respect to other approaches. Nevertheless, the size of a DD is dependent on its variable order. Because the search of an optimal order is an NP-complete task, variable order heuristics on DDs are applied by exploiting domain-specific information. Here, we propose a heuristic based on the information content of the labeled paths. Tests on well-studied biological benchmarks, which are an essential part of multi-omics graphs, show that the resultant size correlates with the information measure related to the paths and that the chosen order allows to effectively reduce the index size

rCASC implementation in Laniakea: porting containerization-based-reproducibility to a cloud Galaxy on-demand platform

Integrating rCASC in Laniakea: rCASC, Cluster Analysis of Single Cells [Alessandri et al. BioRxiv], is part of the reproducible-bioinformatics.org project and provides single cell analysis functionalities within the reproducible rules described by Sandve et al. [PLoS Comp Biol. 2013]. Laniakea [Tangaro et al. BioRxiv Bioinformatics] provides the possibility to automate the creation of Galaxy-based virtualized environments through an easy setup procedure, providing an on-demand workspace ready to be used by life scientists and bioinformaticians. The final goal is to offer rCASC as a Galaxy flavor in the Laniakea Galaxy on-demand environment

The GreatSPN tool: recent enhancements

GreatSPN is a tool that supports the design and the qualitative and quantitative analysis of Generalized Stochastic Petri Nets (GSPN) and of Stochastic Well-Formed Nets (SWN). The very first version of GreatSPN saw the light in the late eighties of last century: since then two main releases where developed and widely distributed to the research community: GreatSPN1.7 [13], and GreatSPN2.0 [8]. This paper reviews the main functionalities of GreatSPN2.0 and presents some recently added features that significantly enhance the efficacy of the tool

The Mean Drift: Tailoring the Mean Field Theory of Markov Processes for Real-World Applications

The statement of the mean field approximation theorem in the mean field theory of Markov processes particularly targets the behaviour of population processes with an unbounded number of agents. However, in most real-world engineering applications one faces the problem of analysing middle-sized systems in which the number of agents is bounded. In this paper we build on previous work in this area and introduce the mean drift. We present the concept of population processes and the conditions under which the approximation theorems apply, and then show how the mean drift is derived through a systematic application of the propagation of chaos. We then use the mean drift to construct a new set of ordinary differential equations which address the analysis of population processes with an arbitrary size

Chimera: a Bioconductor package for secondary analysis of fusion products

Chimera is a Bioconductor package that organizes, annotates, analyses and validates fusions reported by different fusion detection tools; current implementation can deal with output from bellerophontes, chimeraScan, deFuse, fusionCatcher, FusionFinder, FusionHunter, FusionMap, mapSplice, Rsubread, tophat-fusion and STAR. The core of Chimera is a fusion data structure that can store fusion events detected with any of the aforementioned tools. Fusions are then easily manipulated with standard R functions or through the set of functionalities specifically developed in Chimera with the aim of supporting the user in managing fusions and discriminating falsepositive results