23 papers · ranked by Valyu relevance
Patrick Wang, Henry Ye, Wayne Hayes
—We introduce the first algorithm to perform topology-only local graph matching (a.k.a. local network alignment or subgraph isomorphism): BLANT, for Basic Local Alignment of Network Topology. BLANT first creates a limited, highspecificity index of a single graph containing connected k-node induced subgraphs called…
Pramesh Singh, Hannah Kuder, Anna Ritz, Sofia Forslund
Graphlets are defined as connected, induced, non-isomorphic subgraphs of a specific size (). Graphlets describe the structure of a network without requiring the specification of a null model and thus differ from motifs (). The edges of every graphlet are partitioned into a set of automorphism groups called orbits such…
Bartłomiej Szawulak, Piotr Formanowicz
Capability to compare biological models is a crucial step needed in an analysis of complex organisms. Petri nets as a popular modelling technique, needs a possibility to determine the degree of structural similarities (e.g., comparison of metabolic or signaling pathways). However, existing comparison methods use…
Pramesh Singh, Hannah Kuder, Anna Ritz
Higher-order interaction patterns among proteins have the potential to reveal mechanisms behind molecular processes and diseases. While clustering methods are used to identify functional groups within molecular interaction networks, these methods largely focus on edge density and do not explicitly take into…
Deukryeol Yoon, Dongjin Lee, Minyoung Choe, Kijung Shin
Counting the instances of each graphlet (i.e., an induced subgraph isomorphism class) has been successful in characterizing local structures of graphs, with many applications. While graphlets have been extended for temporal graphs, the extensions are designed for examining temporally-local subgraphs composed of edges…
Daniel Tello Velasco, Sam F. L. Windels, Mikhail Rotkevich, Noël Malod-Dognin + 1 more
Spatial Analysis of Functional Enrichment (SAFE) is a popular tool for biologists to investigate the functional organisation of biological networks via highly intuitive 2D functional maps. To create these maps, SAFE uses Spring embedding to project a given network into a 2D space in which nodes connected in the network…
Mingshan Jia, Maité Van Alboom, Liesbet Goubert, Piet Bracke + 3 more
'Bogdan Gabrys' 'Katarzyna Musial' 'Lun Hu'] Graph embedding approaches have been attracting increasing attention in recent years mainly due to their universal applicability. They convert network data into a vector space in which the graph structural information and properties are maximumly preserved. Most existing…
Syed Ibtisam Tauhidi, Arindam Karmakar, Thai Son Mai, Hans Vandierendonck
Counting the orbits of graphlets in a network is a vital tool for understanding the structural roles of vertices in various graph analytics tasks. While existing algorithms efficiently compute orbits of induced graphlets, many real-world applications require non-induced orbit counts. However, no current method offers…
Jure Pražnikar, K. Diederichs
The components of the graphlet degree vector, which describes the complexity of the wiring of a given atom, can be used in a multiple linear regression model to predict atomic displacement parameters in protein structures.
Altaf Barelvi, Oliver Anderson, Anna Ritz, Mingfu Shao
We generated oxidative stress subnetworks that connected 80% of the oxidative stress response proteins within each species network and identified frequently occurring graphlets compared to random networks (Methods). All 98 potential 3-node graphlets were present in at least one of the oxidative stress subnetworks, and…
G Hriday, Pranav Saikiran Sista, Apurba Das
Graphlet counting is an important problem as it has numerous applications in several fields, including social network analysis, biological network analysis, transaction network analysis, etc. Most of the practical networks are dynamic. A graphlet is a subgraph with a fixed number of vertices and can be induced or…
Colin Cleveland, Chin-Yen Lee, Shen-Fu Tsai, Wei-Hsuan Yu + 1 more
'Hsuan‐Wei Lee'] Many applications, ranging from natural to social sciences, rely on graphlet analysis for the intuitive and meaningful characterization of networks employing micro-level structures as building blocks. However, it has not been thoroughly explored in heterogeneous graphs, which comprise various types of…
Altaf Barelvi, Oliver Anderson, Anna Ritz
Graphs are powerful tools for modeling and analyzing molecular interaction networks. Graphs typically represent either undirected physical interactions or directed regulatory relationships, which can obscure a particular protein’s functional context. Graphlets can describe local topologies and patterns within graphs…
M. Kaan Arici, Nurcan Tuncbag
Omics technologies are powerful tools for detecting dysregulated and altered signaling components in various contexts, encompassing disease states, patients, and drug-perturbations. Network inference or reconstruction algorithms play an integral role in the successful analysis and identification of causal relationships…
Bram Mornie, Didier Colle, Pieter Audenaert, Mario Pickavet + 1 more
'Enrique Hernandez-Lemus'] Testing or benchmarking network algorithms in bioinformatics requires a diverse set of networks with realistic properties. Real networks are often supplemented by randomly generated synthetic ones, but most graph generative models do not take into account the distribution of subgraph…
Hartman, David, Pokorná, Aneta + 4 more
Graphlets are small subgraphs rooted at a fixed vertex. The number of occurrences of graphlets aligned to a particular vertex, called graphlet degree sequence, gives a topological description of the surrounding of the analyzed vertex. In this article, we study properties and uniqueness of graphlet degree sequences. The…
Lionel Zoubritzky, François-Xavier Coudert
We present here an open-source Julia library for the topological identification of crystalline materials, with algorithmic and computational improvements over the previously available software in the field, resulting in a speed increase of one order of magnitude. This new algorithm and implementation can therefore be…
Diane Duroux, Kristel Van Steen
Many problems in life sciences can be brought back to a comparison of graphs. Even though a multitude of such techniques exist, often, these assume prior knowledge about the partitioning or the number of clusters and fail to provide statistical significance of observed between-network heterogeneity. Addressing these…
Authors not listed
Computational methods for predictive modeling have been increasingly utilized in the early stages of drug discovery to supplement high-throughput screening. The advent of highly efficient and complex machine learning architectures necessitates new methods of collating the plethora of topological, geometrical, and…
Authors not listed
Genetic Algorithms are a powerful method to solve optimization problems with complex cost functions over vast search spaces that rely in particular on recombining parts of previous solutions. Crossover operators play a crucial role in this context. Here, we describe a large class of these operators designed for…
Jonas Schaub, Julian Zander, Achim Zielesny, Christoph Steinbeck
The concept of molecular scaffolds as defining core structures of organic molecules is utilised in many areas of chemistry and cheminformatics, e.g. drug design, chemical classification, or the analysis of high-throughput screening data. Here, we present Scaffold Generator, a comprehensive open library for the…
David Buterez, Jon Paul Janet, Steven Kiddle, Pietro Liò
We investigate the potential of graph neural networks for transfer learning and improving molecular property prediction on sparse and expensive to acquire high-fidelity data by leveraging low-fidelity measurements as an inexpensive proxy for a targeted property ofinterest. This problem arises in discovery processes…
Authors not listed
We present a unified, set–theoretic framework that extends molecular graphs to hypergraphs and superhypergraphs via iterated power sets. We define Molecular Graphs, Molecular HyperGraphs, and Molecular SuperHyperGraphs, and develop four complements over them: Weighted, Rough, Neural, and Multipolar frameworks. We prove…