PyGTED: Python']Python Application for Computing Graph Traversal Edit Distance

被引:0
|
作者
Boroojeny, Ali Ebrahimpour [1 ]
Shrestha, Akash [1 ]
Sharifi-zarchi, Ali [2 ]
Gallagher, Suzanne Renick [1 ]
Sahinalp, Suleyman Cenk [3 ]
Chitsaz, Hamidreza [1 ]
机构
[1] Colorado State Univ, Dept Comp Sci, 279 Comp Sci Bldg,1873 Campus Delivery, Ft Collins, CO 80523 USA
[2] Sharif Univ Technol, Dept Comp Engn, Tehran, Iran
[3] NCI, NIH, Bethesda, MD 20892 USA
关键词
clustering genera; coassembly; de novo variation detaction; graph comparison; graph kernel; linear programming;
D O I
10.1089/cmb.2019.0510
中图分类号
Q5 [生物化学];
学科分类号
071010 ; 081704 ;
摘要
Graph Traversal Edit Distance (GTED) is a measure of distance (or dissimilarity) between two graphs introduced. This measure is based on the minimum edit distance between two strings formed by the edge labels of respective Eulerian traversals of the two graphs. GTED was motivated by and provides the first mathematical formalism for sequence coassembly and de novo variation detection in bioinformatics. Many problems in applied machine learning deal with graphs (also called networks), including social networks, security, web data mining, protein function prediction, and genome informatics. The kernel paradigm beautifully decouples the learning algorithm from the underlying geometric space, which renders graph kernels important for the aforementioned applications. In this article, we introduce a tool, PyGTED to compute GTED. It implements the algorithm based on the polynomial time algorithm devised for it by the authors. Informally, the GTED is the minimum edit distance between two strings formed by the edge labels of respective Eulerian traversals of the two graphs.
引用
收藏
页码:436 / 439
页数:4
相关论文
共 50 条
  • [41] Scikit-network: Graph Analysis in Python']Python
    Bonald, Thomas
    de Lara, Nathan
    Lutz, Quentin
    Charpentier, Bertrand
    JOURNAL OF MACHINE LEARNING RESEARCH, 2020, 21
  • [42] High-performance Python']Python for crystallographic computing
    Boulle, A.
    Kieffer, J.
    JOURNAL OF APPLIED CRYSTALLOGRAPHY, 2019, 52 : 882 - 897
  • [43] Agent-oriented computing platform in Python']Python
    Kazirod, Maciej
    Korczynski, Wojciech
    Byrski, Aleksander
    2014 IEEE/WIC/ACM INTERNATIONAL JOINT CONFERENCES ON WEB INTELLIGENCE (WI) AND INTELLIGENT AGENT TECHNOLOGIES (IAT), VOL 3, 2014, : 365 - 372
  • [44] Python']Python accelerators for high-performance computing
    Marowka, Ami
    JOURNAL OF SUPERCOMPUTING, 2018, 74 (04): : 1449 - 1460
  • [45] Little Ball of Fur: A Python']Python Library for Graph Sampling
    Rozemberczki, Benedek
    Kiss, Oliver
    Sarkar, Rik
    CIKM '20: PROCEEDINGS OF THE 29TH ACM INTERNATIONAL CONFERENCE ON INFORMATION & KNOWLEDGE MANAGEMENT, 2020, : 3133 - 3140
  • [46] pyUDLF: A Python']Python Framework for Unsupervised Distance Learning Tasks
    Leticio, Gustavo Rosseto
    Valem, Lucas Pascotti
    Lopes, Leonardo Tadeu
    Guimaraes Pedronette, Daniel Carlos
    PROCEEDINGS OF THE 31ST ACM INTERNATIONAL CONFERENCE ON MULTIMEDIA, MM 2023, 2023, : 9680 - 9684
  • [47] Systems Neuroscience Computing in Python']Python (SyNCoPy): a python']python package for large-scale analysis of electrophysiological data
    Moenke, Gregor
    Schaefer, Tim
    Parto-Dezfouli, Mohsen
    Kajal, Diljit Singh
    Fuertinger, Stefan
    Schmiedt, Joscha Tapani
    Fries, Pascal
    FRONTIERS IN NEUROINFORMATICS, 2024, 18
  • [48] GPU Computing with Python']Python: Performance, Energy Efficiency and Usability
    Holm, Havard H.
    Brodtkorb, Andre R.
    Saetra, Martin L.
    COMPUTATION, 2020, 8 (01)
  • [49] WHY PYTHON']PYTHON IS THE NEXT WAVE IN EARTH SCIENCES COMPUTING
    Lin, Johnny Wei-Bing
    BULLETIN OF THE AMERICAN METEOROLOGICAL SOCIETY, 2012, 93 (12) : 1823 - 1824
  • [50] TrustML: A Python']Python package for computing the trustworthiness of ML models
    Manzano, Marti
    Ayala, Claudia
    Gomez, Cristina
    SOFTWAREX, 2024, 26