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13 papers · ranked by Valyu relevance
Erfan Farhangi Maleki, Nasser Ghadiri, Maryam Lotfi Shahreza, Zeinab Maleki
Heterogeneous complex networks are large graphs consisting of different types of nodes and edges. The process of mining and knowledge extraction from these networks is so complicated. Moreover, the scale of these networks is steadily increasing. Thus, scalable methods are required. In this paper, two distributed label…
Guohao Dou
We propose an algorithm to simulate Markovian SIS epidemics with homogeneous rates and pairwise interactions on a fixed undirected graph, assuming a distributed memory model of parallel programming and limited bandwidth. We offer an implementation of the algorithm in the form of pseudocode in the Appendix. Also, we…
Arjun Chandrasekhar, Deborah M. Gordon, Saket Navlakha
We study how the arboreal turtle ant (Cephalotes goniodontus) solves a fundamental computing problem: maintaining a trail network and finding alternative paths to route around broken links in the network. Turtle ants form a routing backbone of foraging trails linking several nests together. This species travels only in…
Wilfried Agbeto, Camille Coti, Vladimir Reinharz
Advances in graph algorithmics have allowed in-depth study of many natural objects from molecular biology or chemistry to social networks. Particularly in molecular biology and cheminformatics, understanding complex structures by identifying conserved sub-structures is a key milestone towards the artificial design of…
Tobias Røikjer, Asger Hobolth, Kasper Munch
Phase-type distributions model the time until absorption in continuous or discrete-time Markov chains on a finite state space. The multivariate phase-type distributions have diverse and important applications by modeling rewards accumulated at visited states. However, even moderately-sized state spaces make the…
Haotian Li
Machine learning and deep learning are novel and trending approaches to solving real-world scientific problems. Graph machine learning is dedicated to performing learning methods, such as graph neural networks, on non-Euclidean data such as graphs. Molecules, with their natural graph structures, could be analyzed by…
Jing Xie, Qi Duan
Biological pathway analysis often requires identifying interventions that block reachability to an undesirable state, such as a disease-associated module, toxic byproduct, or adverse phenotype, while preserving reachability among essential biological functions. Motivated by this setting, we study the Reachability…
Fawaz Dabbaghie
The representation of genomes and genomic sequences through graph structures has undergone a period of rapid development in recent years, particularly to accommodate the growing size of genome sequences that are being produced. Genome graphs have been employed extensively for a variety of purposes, including assembly…
Jimmy Wu, Alex Khodaverdian, Benjamin Weitz, Nir Yosef
Network connectivity problems are abundant in computational biology research, where graphs are used to represent a range of phenomena: from physical interactions between molecules to more abstract relationships such as gene co-expression. One common challenge in studying biological networks is the need to extract…
Jacob Porter
Fast Neighbor-Net is a command-line Java program that has fast implementations of the popular Neighbor-Net phylo-genetic split network approach. This allows more efficiency in computationally intensive tasks such as larger scale data analysis and bootstrapping. The canonical search phase iteratively finds a pair of…
Gertjan Bisschop
Extracting information on the selective and demographic past of populations that is contained in samples of genome sequences requires a description of the distribution of the underlying genealogies. Using the Laplace transform, this distribution can be generated with a simple recursive procedure, regardless of model…
Pratha Sah, Lisa O. Singh, Aaron Clauset, Shweta Bansal
A modular pattern, also called community structure, is ubiquitous in biological networks. There has been an increased interest in unraveling the community structure of biological systems as it may provide important insights into a system’s functional components and the impact of local structures on dynamics at a global…
Swier Garst, Julian Dekker, Marcel Reinders
Federated learning is an upcoming machine learning paradigm which allows data from multiple sources to be used for training of classifiers without the data leaving the source it originally resides. This can be highly valuable for use cases such as medical research, where gathering data at a central location can be…