17,746 research outputs found
Optimal topology for consensus using genetic algorithm
In the Multi-Agent Systems (MAS), graph network topologies play a crucial role in building consensus among the connected agents. Consensus may be achieved on many network graphs using distributed control theory. However, the optimal network topology is not addressed in most of the literature, which is an important part of building stable consensus among networked agents. In this paper, the optimal topology is obtained irrespective of the agent dynamics by using two-dimensional Genetic Algorithm (GA), which is a new approach in this context. Simulation result for agents with first, and second-order linear dynamic is obtained. These results show that the proposed method achieves consensus using the optimal network topology satisfactorily
A rapid and scalable method for multilocus species delimitation using Bayesian model comparison and rooted triplets
Multilocus sequence data provide far greater power to resolve species limits than the single locus data typically used for broad surveys of clades. However, current statistical methods based on a multispecies coalescent framework are computationally demanding, because of the number of possible delimitations that must be compared and time-consuming likelihood calculations. New methods are therefore needed to open up the power of multilocus approaches to larger systematic surveys. Here, we present a rapid and scalable method that introduces two new innovations. First, the method reduces the complexity of likelihood calculations by decomposing the tree into rooted triplets. The distribution of topologies for a triplet across multiple loci has a uniform trinomial distribution when the 3 individuals belong to the same species, but a skewed distribution if they belong to separate species with a form that is specified by the multispecies coalescent. A Bayesian model comparison framework was developed and the best delimitation found by comparing the product of posterior probabilities of all triplets. The second innovation is a new dynamic programming algorithm for finding the optimum delimitation from all those compatible with a guide tree by successively analyzing subtrees defined by each node. This algorithm removes the need for heuristic searches used by current methods, and guarantees that the best solution is found and potentially could be used in other systematic applications. We assessed the performance of the method with simulated, published and newly generated data. Analyses of simulated data demonstrate that the combined method has favourable statistical properties and scalability with increasing sample sizes. Analyses of empirical data from both eukaryotes and prokaryotes demonstrate its potential for delimiting species in real cases
Principal components analysis in the space of phylogenetic trees
Phylogenetic analysis of DNA or other data commonly gives rise to a
collection or sample of inferred evolutionary trees. Principal Components
Analysis (PCA) cannot be applied directly to collections of trees since the
space of evolutionary trees on a fixed set of taxa is not a vector space. This
paper describes a novel geometrical approach to PCA in tree-space that
constructs the first principal path in an analogous way to standard linear
Euclidean PCA. Given a data set of phylogenetic trees, a geodesic principal
path is sought that maximizes the variance of the data under a form of
projection onto the path. Due to the high dimensionality of tree-space and the
nonlinear nature of this problem, the computational complexity is potentially
very high, so approximate optimization algorithms are used to search for the
optimal path. Principal paths identified in this way reveal and quantify the
main sources of variation in the original collection of trees in terms of both
topology and branch lengths. The approach is illustrated by application to
simulated sets of trees and to a set of gene trees from metazoan (animal)
species.Comment: Published in at http://dx.doi.org/10.1214/11-AOS915 the Annals of
Statistics (http://www.imstat.org/aos/) by the Institute of Mathematical
Statistics (http://www.imstat.org
Towards Optimal Distributed Node Scheduling in a Multihop Wireless Network through Local Voting
In a multihop wireless network, it is crucial but challenging to schedule
transmissions in an efficient and fair manner. In this paper, a novel
distributed node scheduling algorithm, called Local Voting, is proposed. This
algorithm tries to semi-equalize the load (defined as the ratio of the queue
length over the number of allocated slots) through slot reallocation based on
local information exchange. The algorithm stems from the finding that the
shortest delivery time or delay is obtained when the load is semi-equalized
throughout the network. In addition, we prove that, with Local Voting, the
network system converges asymptotically towards the optimal scheduling.
Moreover, through extensive simulations, the performance of Local Voting is
further investigated in comparison with several representative scheduling
algorithms from the literature. Simulation results show that the proposed
algorithm achieves better performance than the other distributed algorithms in
terms of average delay, maximum delay, and fairness. Despite being distributed,
the performance of Local Voting is also found to be very close to a centralized
algorithm that is deemed to have the optimal performance
Integration of molecular network data reconstructs Gene Ontology.
Motivation: Recently, a shift was made from using Gene Ontology (GO) to evaluate molecular network data to using these data to construct and evaluate GO. Dutkowski et al. provide the first evidence that a large part of GO can be reconstructed solely from topologies of molecular networks. Motivated by this work, we develop a novel data integration framework that integrates multiple types of molecular network data to reconstruct and update GO. We ask how much of GO can be recovered by integrating various molecular interaction data. Results: We introduce a computational framework for integration of various biological networks using penalized non-negative matrix tri-factorization (PNMTF). It takes all network data in a matrix form and performs simultaneous clustering of genes and GO terms, inducing new relations between genes and GO terms (annotations) and between GO terms themselves. To improve the accuracy of our predicted relations, we extend the integration methodology to include additional topological information represented as the similarity in wiring around non-interacting genes. Surprisingly, by integrating topologies of bakers’ yeasts protein–protein interaction, genetic interaction (GI) and co-expression networks, our method reports as related 96% of GO terms that are directly related in GO. The inclusion of the wiring similarity of non-interacting genes contributes 6% to this large GO term association capture. Furthermore, we use our method to infer new relationships between GO terms solely from the topologies of these networks and validate 44% of our predictions in the literature. In addition, our integration method reproduces 48% of cellular component, 41% of molecular function and 41% of biological process GO terms, outperforming the previous method in the former two domains of GO. Finally, we predict new GO annotations of yeast genes and validate our predictions through GIs profiling. Availability and implementation: Supplementary Tables of new GO term associations and predicted gene annotations are available at http://bio-nets.doc.ic.ac.uk/GO-Reconstruction/. Contact: [email protected] Supplementary information: Supplementary data are available at Bioinformatics online
Network Inference from Consensus Dynamics
We consider the problem of identifying the topology of a weighted, undirected
network from observing snapshots of multiple independent consensus
dynamics. Specifically, we observe the opinion profiles of a group of agents
for a set of independent topics and our goal is to recover the precise
relationships between the agents, as specified by the unknown network . In order to overcome the under-determinacy of the problem at hand, we
leverage concepts from spectral graph theory and convex optimization to unveil
the underlying network structure. More precisely, we formulate the network
inference problem as a convex optimization that seeks to endow the network with
certain desired properties -- such as sparsity -- while being consistent with
the spectral information extracted from the observed opinions. This is
complemented with theoretical results proving consistency as the number of
topics grows large. We further illustrate our method by numerical experiments,
which showcase the effectiveness of the technique in recovering synthetic and
real-world networks.Comment: Will be presented at the 2017 IEEE Conference on Decision and Control
(CDC
- …