ArticleBioinformatics (Oxford, England)2018
graphkernels: R and Python packages for graph comparison.
Article in Bioinformatics (Oxford, England), 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
8 citing papers in PubMed.
- Complex dynamics in psychological data: Mapping individual symptom trajectories to group-level patterns.Behavior research methods · 2026Article
- A cancer graph: a lung cancer property graph database in Neo4j.BMC research notes · 2022Article
- Bacterial low-abundant taxa are key determinants of a healthy airway metagenome in the early years of human life.Computational and structural biotechnology journal · 2022Article
- Biological network analysis with deep learning.Briefings in bioinformatics · 2021Review
- Bioinformatic Analysis Combined With Experimental Validation Reveals Novel Hub Genes and Pathways Associated With Focal Segmental Glomerulosclerosis.Frontiers in molecular biosciences · 2021Article
- Comparing methods for comparing networks.Scientific reports · 2019Article
- Pathway and network analysis of genes related to osteoporosis.Molecular medicine reports · 2019Article
- Evaluation of multidisciplinary collaboration in pediatric trauma care using EHR data.Journal of the American Medical Informatics Association : JAMIA · 2019Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
4 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Summary: Measuring the similarity of graphs is a fundamental step in the analysis of graph-structured data, which is omnipresent in computational biology. Graph kernels have been proposed as a powerful and efficient approach to this problem of graph comparison. Here we provide graphkernels, the first R and Python graph kernel libraries including baseline kernels such as label histogram based kernels, classic graph kernels such as random walk based kernels, and the state-of-the-art Weisfeiler-Lehman graph kernel. The core of all graph kernels is implemented in C ++ for efficiency. Using the kernel matrices computed by the package, we can easily perform tasks such as classification, regression and clustering on graph-structured samples. Availability and implementation: The R and Python packages including source code are available at https://CRAN.R-project.org/package=graphkernels and https://pypi.python.org/pypi/graphkernels. Contact: mahito@nii.ac.jp or elisabetta.ghisu@bsse.ethz.ch. Supplementary information: Supplementary data are available online at Bioinformatics.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.