14 papers · ranked by Valyu relevance
Yuchen Zhou, Hongtao Huo, Zhiwen Hou, Fanliang Bu + 1 more
Graph Convolutional Networks (GCNs) are powerful deep learning methods for non-Euclidean structure data and achieve impressive performance in many fields. But most of the state-of-the-art GCN models are shallow structures with depths of no more than 3 to 4 layers, which greatly limits the ability of GCN models to…
Matthias M Dehmer, Nicola N Barbarini, Kurt K Varmuza, Armin A Graber
'Armin A Graber'] Background Topological descriptors, other graph measures, and in a broader sense, graph-theoretical methods, have been proven as powerful tools to perform biological network analysis. However, the majority of the developed descriptors and graph-theoretical methods does not have the ability to take…
Serkan Ucer, Tansel Ozyer, Reda Alhajj
We propose a new type of supervised visual machine learning classifier, GSNAc, based on graph theory and social network analysis techniques. In a previous study, we employed social network analysis techniques and introduced a novel classification model (called Social Network Analysis-based Classifier-SNAc) which…
Ilya Makarov, Dmitrii Kiselev, Nikita Nikitinsky, Lovro Subelj + 1 more
Dealing with relational data always required significant computational resources, domain expertise and task-dependent feature engineering to incorporate structural information into a predictive model. Nowadays, a family of automated graph feature engineering techniques has been proposed in different streams of…
Bo Jin, Brian Muller, Chengxiang Zhai, Xinghua Lu
Background The Gene Ontology is a controlled vocabulary for representing knowledge related to genes and proteins in a computable form. The current effort of manually annotating proteins with the Gene Ontology is outpaced by the rate of accumulation of biomedical knowledge in literature, which urges the development of…
Ali Deeb, Abdalrahman Ibrahim, Mohamed Salem, Joachim Pichler + 6 more
'Sergii Tkachov' 'Anjeza Karaj' 'Fadi Al Machot' 'Kyamakya Kyandoghere' 'Marco Lanuzza' 'Lionel Trojman'] Analog mixed-signal (AMS) verification is one of the essential tasks in the development process of modern systems-on-chip (SoC). Most parts of the AMS verification flow are already automated, except for stimuli…
Manuel Middendorf, Etay Ziv, Carter Adams, Jen Hom + 5 more
'Chaya Levovitz' 'Gregory Woods' 'Linda Chen' 'Chris Wiggins'] Background Recent genomic and bioinformatic advances have motivated the development of numerous network models intending to describe graphs of biological, technological, and sociological origin. In most cases the success of a model has been evaluated by how…
Anthony Gillioz, Kaspar Riesen
Despite great efforts done in research in the last decades, the classification of general graphs, i.e., graphs with unconstrained labeling and structure, remains a challenging task. Due to the inherent relational structure of graphs it is difficult, or even impossible, to apply standard pattern recognition methods to…
Manfred Jaeger
Reasoning about graphs, and learning from graph data is a field of artificial intelligence that has recently received much attention in the machine learning areas of graph representation learning and graph neural networks. Graphs are also the underlying structures of interest in a wide range of more traditional fields…
Vincent Guillemot, Arthur Tenenhaus, Laurent Le Brusquet, Vincent Frouin + 1 more
'Vincent Frouin' 'Arkady B. Khodursky'] Integrating gene regulatory networks (GRNs) into the classification process of DNA microarrays is an important issue in bioinformatics, both because this information has a true biological interest and because it helps in the interpretation of the final classifier. We present a…
Stephen K. Grady, Faisal N. Abu-Khzam, Ronald D. Hagan, Hesam Shams + 1 more
Deciding the size of a minimum dominating set is a classic NP-complete problem. It has found increasing utility as the basis for classifying vertices in networks derived from protein-protein, noncoding RNA, metabolic, and other biological interaction data. In this context it can be helpful, for example, to identify…
Yuan Li, Duan Huang, Rosario Lo Franco, Andreas Wichert
In terms of the logical structure of data in machine learning (ML), we apply a novel graphical encoding method in quantum computing to build the mapping between feature space of sample data and two-level nested graph state that presents a kind of multi-partite entanglement state. By implementing swap-test circuit on…
Miguel E. Coimbra, Alexandre P. Francisco, Luís Veiga
The value of graph-based big data can be unlocked by exploring the topology and metrics of the networks they represent, and the computational approaches to this exploration take on many forms. For the use-case of performing global computations over a graph, it is first ingested into a graph processing system from one…
Miguel Ángel Rodríguez-García, Robert Hoehndorf
Background Ontologies are representations of a conceptualization of a domain. Traditionally, ontologies in biology were represented as directed acyclic graphs (DAG) which represent the backbone taxonomy and additional relations between classes. These graphs are widely exploited for data analysis in the form of ontology…