scholarly journals Optimizing phylogenetic supertrees using answer set programming

2015 ◽  
Vol 15 (4-5) ◽  
pp. 604-619 ◽  
Author(s):  
LAURA KOPONEN ◽  
EMILIA OIKARINEN ◽  
TOMI JANHUNEN ◽  
LAURA SÄILÄ
Keyword(s):  
Phylogenetic Trees ◽  
Optimization Problem ◽  
Matrix Representation ◽  
Answer Set Programming ◽  
Heuristic Methods ◽  
Construction Problem ◽  
Single Tree ◽  
Conflicting Information ◽  
The Family ◽  
Answer Set

AbstractThe supertree construction problem is about combining several phylogenetic trees with possibly conflicting information into a single tree that has all the leaves of the source trees as its leaves and the relationships between the leaves are as consistent with the source trees as possible. This leads to an optimization problem that is computationally challenging and typically heuristic methods, such as matrix representation with parsimony (MRP), are used. In this paper we consider the use of answer set programming to solve the supertree construction problem in terms of two alternative encodings. The first is based on an existing encoding of trees using substructures known as quartets, while the other novel encoding captures the relationships present in trees through direct projections. We use these encodings to compute a genus-level supertree for the family of cats (Felidae). Furthermore, we compare our results to recent supertrees obtained by the MRP method.

scholarly journals Polynomial Supertree Methods Revisited

2011 ◽  
Vol 2011 ◽  
pp. 1-21 ◽  
Author(s):  
Malte Brinkmeyer ◽  
Thasso Griebel ◽  
Sebastian Böcker
Keyword(s):  
Simulation Study ◽  
Phylogenetic Trees ◽  
Optimization Problem ◽  
Matrix Representation ◽  
Distance Matrix ◽  
Extensive Simulation ◽  
Supertree Method ◽  
Pros And Cons ◽  
To Come ◽  
Supertree Methods

Supertree methods allow to reconstruct large phylogenetic trees by combining smaller trees with overlapping leaf sets into one, more comprehensive supertree. The most commonly used supertree method, matrix representation with parsimony (MRP), produces accurate supertrees but is rather slow due to the underlying hard optimization problem. In this paper, we present an extensive simulation study comparing the performance of MRP and the polynomial supertree methods MinCut Supertree, Modified MinCut Supertree, Build-with-distances, PhySIC, PhySIC_IST, and super distance matrix. We consider both quality and resolution of the reconstructed supertrees. Our findings illustrate the tradeoff between accuracy and running time in supertree construction, as well as the pros and cons of voting- and veto-based supertree approaches. Based on our results, we make some general suggestions for supertree methods yet to come.

Explainable Planning Using Answer Set Programming

2020 ◽  
Author(s):  
Van Nguyen ◽  
Stylianos Loukas Vasileiou ◽  
Tran Cao Son ◽  
William Yeoh
Keyword(s):  
Optimization Problem ◽  
Answer Set Programming ◽  
Popular Approach ◽  
Planning Problems ◽  
Do So ◽  
Answer Set

In human-aware planning problems, the planning agent may need to explain its plan to a human user, especially when the plan appears infeasible or suboptimal for the user. A popular approach to do so is called model reconciliation, where the planning agent tries to reconcile the differences between its model and the model of the user such that its plan is also feasible and optimal to the user. This problem can be viewed as an optimization problem, where the goal is to find a subset-minimal explanation that one can use to modify the model of the user such that the plan of the agent is also feasible and optimal to the user. This paper presents an algorithm for solving such problems using answer set programming.

Inferring Phylogenetic Trees Using Answer Set Programming

2007 ◽  
Vol 39 (4) ◽  
pp. 471-511 ◽  
Author(s):  
Daniel R. Brooks ◽  
Esra Erdem ◽  
Selim T. Erdoğan ◽  
James W. Minett ◽  
Don Ringe
Keyword(s):  
Phylogenetic Trees ◽  
Answer Set Programming ◽  
Answer Set

scholarly journals Extensive Phylogenetic Analysis of Piscine Orthoreovirus Genomic Sequences Shows the Robustness of Subgenotype Classification

Pathogens ◽  
2021 ◽  
Vol 10 (1) ◽  
pp. 41
Author(s):  
Marcos Godoy ◽  
Daniel A. Medina ◽  
Rudy Suarez ◽  
Sandro Valenzuela ◽  
Jaime Romero ◽  
...  
Keyword(s):  
Phylogenetic Analysis ◽  
Genetic Variation ◽  
Sequence Analysis ◽  
Molecular Phylogeny ◽  
Phylogenetic Trees ◽  
Genomic Segment ◽  
Double Stranded Rna ◽  
The Family ◽  
Original Classification

Piscine orthoreovirus (PRV) belongs to the family Reoviridae and has been described mainly in association with salmonid infections. The genome of PRV consists of about 23,600 bp, with 10 segments of double-stranded RNA, classified as small (S1 to S4), medium (M1, M2 and M3) and large (L1, L2 and L3); these range approximately from 1000 bp (segment S4) to 4000 bp (segment L1). How the genetic variation among PRV strains affects the virulence for salmonids is still poorly understood. The aim of this study was to describe the molecular phylogeny of PRV based on an extensive sequence analysis of the S1 and M2 segments of PRV available in the GenBank database to date (May 2020). The analysis was extended to include new PRV sequences for S1 and M2 segments. In addition, subgenotype classifications were assigned to previously published unclassified sequences. It was concluded that the phylogenetic trees are consistent with the original classification using the PRV genomic segment S1, which differentiates PRV into two major genotypes, I and II, and each of these into two subgenotypes, designated as Ia and Ib, and IIa and IIb, respectively. Moreover, some clusters of country- and host-specific PRV subgenotypes were observed in the subset of sequences used. This work strengthens the subgenotype classification of PRV based on the S1 segment and can be used to enhance research on the virulence of PRV.

scholarly journals Open answer set programming with guarded programs

2008 ◽  
Vol 9 (4) ◽  
pp. 1-53 ◽  
Author(s):  
Stijn Heymans ◽  
Davy Van Nieuwenborgh ◽  
Dirk Vermeir
Keyword(s):  
Answer Set Programming ◽  
Answer Set

scholarly journals Exhaustively characterizing feasible logic models of a signaling network using Answer Set Programming

2013 ◽  
Vol 29 (18) ◽  
pp. 2320-2326 ◽  
Author(s):  
Carito Guziolowski ◽  
Santiago Videla ◽  
Federica Eduati ◽  
Sven Thiele ◽  
Thomas Cokelaer ◽  
...  
Keyword(s):  
Answer Set Programming ◽  
Signaling Network ◽  
Logic Models ◽  
Answer Set

CoreALMlib: An library translated from the Component Library

2016 ◽  
Vol 16 (5-6) ◽  
pp. 800-816 ◽  
Author(s):  
DANIELA INCLEZAN
Keyword(s):  
Natural Language ◽  
Answer Set Programming ◽  
Natural Language Understanding ◽  
Inference Engine ◽  
Lexical Resources ◽  
Language Understanding ◽  
Commonsense Knowledge ◽  
Component Library ◽  
Action Language ◽  
Answer Set

AbstractThis paper presents CoreALMlib, an $\mathscr{ALM}$ library of commonsense knowledge about dynamic domains. The library was obtained by translating part of the Component Library (CLib) into the modular action language $\mathscr{ALM}$. CLib consists of general reusable and composable commonsense concepts, selected based on a thorough study of ontological and lexical resources. Our translation targets CLibstates (i.e., fluents) and actions. The resulting $\mathscr{ALM}$ library contains the descriptions of 123 action classes grouped into 43 reusable modules that are organized into a hierarchy. It is made available online and of interest to researchers in the action language, answer-set programming, and natural language understanding communities. We believe that our translation has two main advantages over its CLib counterpart: (i) it specifies axioms about actions in a more elaboration tolerant and readable way, and (ii) it can be seamlessly integrated with ASP reasoning algorithms (e.g., for planning and postdiction). In contrast, axioms are described in CLib using STRIPS-like operators, and CLib's inference engine cannot handle planning nor postdiction.

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