grape.ensmallen.datasets.yue

This sub-module offers methods to automatically retrieve the graphs from Yue repository.

View Source
"""This sub-module offers methods to automatically retrieve the graphs from Yue repository."""

from .node2vecppi import node2vecPPI
from .ctddda import CTDDDA
from .drugbankddi import DrugBankDDI
from .mashupppi import MashupPPI
from .ndfrtdda import NDFRTDDA
from .clintermcooc import ClinTermCOOC
from .stringppi import StringPPI

__all__ = [
	"node2vecPPI", "CTDDDA", "DrugBankDDI", "MashupPPI", "NDFRTDDA", "ClinTermCOOC",
	"StringPPI",
]
#   def node2vecPPI( directed: bool = False, preprocess: bool = True, load_nodes: bool = True, verbose: int = 2, cache: bool = True, cache_path: str = 'graphs/yue', version: str = 'latest', **additional_graph_kwargs: Dict ) -> grape.ensmallen.ensmallen.Graph:
View Source
def node2vecPPI(
    directed: bool = False,
    preprocess: bool = True,
    load_nodes: bool = True,
    verbose: int = 2,
    cache: bool = True,
    cache_path: str = "graphs/yue",
    version: str = "latest",
    **additional_graph_kwargs: Dict
) -> Graph:
    """Return new instance of the node2vecPPI graph.

    The graph is automatically retrieved from the Yue repository.	

    Parameters
    -------------------
    directed: bool = False
        Wether to load the graph as directed or undirected.
        By default false.
    preprocess: bool = True
        Whether to preprocess the graph to be loaded in 
        optimal time and memory.
    load_nodes: bool = True,
        Whether to load the nodes vocabulary or treat the nodes
        simply as a numeric range.
    verbose: int = 2,
        Wether to show loading bars during the retrieval and building
        of the graph.
    cache: bool = True
        Whether to use cache, i.e. download files only once
        and preprocess them only once.
    cache_path: str = "graphs"
        Where to store the downloaded graphs.
    version: str = "latest"
        The version of the graph to retrieve.	
    additional_graph_kwargs: Dict
        Additional graph kwargs.

    Returns
    -----------------------
    Instace of node2vecPPI graph.

	References
	---------------------
	Please cite the following if you use the data:
	
	```bib
	@article{yue2020graph,
	  title={Graph embedding on biomedical networks: methods, applications and evaluations},
	  author={Yue, Xiang and Wang, Zhen and Huang, Jingong and Parthasarathy, Srinivasan and Moosavinasab, Soheil and Huang, Yungui and Lin, Simon M and Zhang, Wen and Zhang, Ping and Sun, Huan},
	  journal={Bioinformatics},
	  volume={36},
	  number={4},
	  pages={1241--1251},
	  year={2020},
	  publisher={Oxford University Press}
	}
	```
    """
    return AutomaticallyRetrievedGraph(
        graph_name="node2vecPPI",
        repository="yue",
        version=version,
        directed=directed,
        preprocess=preprocess,
        load_nodes=load_nodes,
        verbose=verbose,
        cache=cache,
        cache_path=cache_path,
        additional_graph_kwargs=additional_graph_kwargs
    )()

Return new instance of the node2vecPPI graph.

The graph is automatically retrieved from the Yue repository.

Parameters
  • directed (bool = False): Wether to load the graph as directed or undirected. By default false.
  • preprocess (bool = True): Whether to preprocess the graph to be loaded in optimal time and memory.
  • load_nodes (bool = True,): Whether to load the nodes vocabulary or treat the nodes simply as a numeric range.
  • verbose (int = 2,): Wether to show loading bars during the retrieval and building of the graph.
  • cache (bool = True): Whether to use cache, i.e. download files only once and preprocess them only once.
  • cache_path (str = "graphs"): Where to store the downloaded graphs.
  • version (str = "latest"): The version of the graph to retrieve.
  • additional_graph_kwargs (Dict): Additional graph kwargs.
Returns

- Instace of node2vecPPI graph.: References

Please cite the following if you use the data:

@article{yue2020graph,
  title={Graph embedding on biomedical networks: methods, applications and evaluations},
  author={Yue, Xiang and Wang, Zhen and Huang, Jingong and Parthasarathy, Srinivasan and Moosavinasab, Soheil and Huang, Yungui and Lin, Simon M and Zhang, Wen and Zhang, Ping and Sun, Huan},
  journal={Bioinformatics},
  volume={36},
  number={4},
  pages={1241--1251},
  year={2020},
  publisher={Oxford University Press}
}
#   def CTDDDA( directed: bool = False, preprocess: bool = True, load_nodes: bool = True, verbose: int = 2, cache: bool = True, cache_path: str = 'graphs/yue', version: str = 'latest', **additional_graph_kwargs: Dict ) -> grape.ensmallen.ensmallen.Graph:
View Source
def CTDDDA(
    directed: bool = False,
    preprocess: bool = True,
    load_nodes: bool = True,
    verbose: int = 2,
    cache: bool = True,
    cache_path: str = "graphs/yue",
    version: str = "latest",
    **additional_graph_kwargs: Dict
) -> Graph:
    """Return new instance of the CTDDDA graph.

    The graph is automatically retrieved from the Yue repository.	

    Parameters
    -------------------
    directed: bool = False
        Wether to load the graph as directed or undirected.
        By default false.
    preprocess: bool = True
        Whether to preprocess the graph to be loaded in 
        optimal time and memory.
    load_nodes: bool = True,
        Whether to load the nodes vocabulary or treat the nodes
        simply as a numeric range.
    verbose: int = 2,
        Wether to show loading bars during the retrieval and building
        of the graph.
    cache: bool = True
        Whether to use cache, i.e. download files only once
        and preprocess them only once.
    cache_path: str = "graphs"
        Where to store the downloaded graphs.
    version: str = "latest"
        The version of the graph to retrieve.	
    additional_graph_kwargs: Dict
        Additional graph kwargs.

    Returns
    -----------------------
    Instace of CTDDDA graph.

	References
	---------------------
	Please cite the following if you use the data:
	
	```bib
	@article{yue2020graph,
	  title={Graph embedding on biomedical networks: methods, applications and evaluations},
	  author={Yue, Xiang and Wang, Zhen and Huang, Jingong and Parthasarathy, Srinivasan and Moosavinasab, Soheil and Huang, Yungui and Lin, Simon M and Zhang, Wen and Zhang, Ping and Sun, Huan},
	  journal={Bioinformatics},
	  volume={36},
	  number={4},
	  pages={1241--1251},
	  year={2020},
	  publisher={Oxford University Press}
	}
	```
    """
    return AutomaticallyRetrievedGraph(
        graph_name="CTDDDA",
        repository="yue",
        version=version,
        directed=directed,
        preprocess=preprocess,
        load_nodes=load_nodes,
        verbose=verbose,
        cache=cache,
        cache_path=cache_path,
        additional_graph_kwargs=additional_graph_kwargs
    )()

Return new instance of the CTDDDA graph.

The graph is automatically retrieved from the Yue repository.

Parameters
  • directed (bool = False): Wether to load the graph as directed or undirected. By default false.
  • preprocess (bool = True): Whether to preprocess the graph to be loaded in optimal time and memory.
  • load_nodes (bool = True,): Whether to load the nodes vocabulary or treat the nodes simply as a numeric range.
  • verbose (int = 2,): Wether to show loading bars during the retrieval and building of the graph.
  • cache (bool = True): Whether to use cache, i.e. download files only once and preprocess them only once.
  • cache_path (str = "graphs"): Where to store the downloaded graphs.
  • version (str = "latest"): The version of the graph to retrieve.
  • additional_graph_kwargs (Dict): Additional graph kwargs.
Returns

- Instace of CTDDDA graph.: References

Please cite the following if you use the data:

@article{yue2020graph,
  title={Graph embedding on biomedical networks: methods, applications and evaluations},
  author={Yue, Xiang and Wang, Zhen and Huang, Jingong and Parthasarathy, Srinivasan and Moosavinasab, Soheil and Huang, Yungui and Lin, Simon M and Zhang, Wen and Zhang, Ping and Sun, Huan},
  journal={Bioinformatics},
  volume={36},
  number={4},
  pages={1241--1251},
  year={2020},
  publisher={Oxford University Press}
}
#   def DrugBankDDI( directed: bool = False, preprocess: bool = True, load_nodes: bool = True, verbose: int = 2, cache: bool = True, cache_path: str = 'graphs/yue', version: str = 'latest', **additional_graph_kwargs: Dict ) -> grape.ensmallen.ensmallen.Graph:
View Source
def DrugBankDDI(
    directed: bool = False,
    preprocess: bool = True,
    load_nodes: bool = True,
    verbose: int = 2,
    cache: bool = True,
    cache_path: str = "graphs/yue",
    version: str = "latest",
    **additional_graph_kwargs: Dict
) -> Graph:
    """Return new instance of the DrugBankDDI graph.

    The graph is automatically retrieved from the Yue repository.	

    Parameters
    -------------------
    directed: bool = False
        Wether to load the graph as directed or undirected.
        By default false.
    preprocess: bool = True
        Whether to preprocess the graph to be loaded in 
        optimal time and memory.
    load_nodes: bool = True,
        Whether to load the nodes vocabulary or treat the nodes
        simply as a numeric range.
    verbose: int = 2,
        Wether to show loading bars during the retrieval and building
        of the graph.
    cache: bool = True
        Whether to use cache, i.e. download files only once
        and preprocess them only once.
    cache_path: str = "graphs"
        Where to store the downloaded graphs.
    version: str = "latest"
        The version of the graph to retrieve.	
    additional_graph_kwargs: Dict
        Additional graph kwargs.

    Returns
    -----------------------
    Instace of DrugBankDDI graph.

	References
	---------------------
	Please cite the following if you use the data:
	
	```bib
	@article{yue2020graph,
	  title={Graph embedding on biomedical networks: methods, applications and evaluations},
	  author={Yue, Xiang and Wang, Zhen and Huang, Jingong and Parthasarathy, Srinivasan and Moosavinasab, Soheil and Huang, Yungui and Lin, Simon M and Zhang, Wen and Zhang, Ping and Sun, Huan},
	  journal={Bioinformatics},
	  volume={36},
	  number={4},
	  pages={1241--1251},
	  year={2020},
	  publisher={Oxford University Press}
	}
	```
    """
    return AutomaticallyRetrievedGraph(
        graph_name="DrugBankDDI",
        repository="yue",
        version=version,
        directed=directed,
        preprocess=preprocess,
        load_nodes=load_nodes,
        verbose=verbose,
        cache=cache,
        cache_path=cache_path,
        additional_graph_kwargs=additional_graph_kwargs
    )()

Return new instance of the DrugBankDDI graph.

The graph is automatically retrieved from the Yue repository.

Parameters
  • directed (bool = False): Wether to load the graph as directed or undirected. By default false.
  • preprocess (bool = True): Whether to preprocess the graph to be loaded in optimal time and memory.
  • load_nodes (bool = True,): Whether to load the nodes vocabulary or treat the nodes simply as a numeric range.
  • verbose (int = 2,): Wether to show loading bars during the retrieval and building of the graph.
  • cache (bool = True): Whether to use cache, i.e. download files only once and preprocess them only once.
  • cache_path (str = "graphs"): Where to store the downloaded graphs.
  • version (str = "latest"): The version of the graph to retrieve.
  • additional_graph_kwargs (Dict): Additional graph kwargs.
Returns

- Instace of DrugBankDDI graph.: References

Please cite the following if you use the data:

@article{yue2020graph,
  title={Graph embedding on biomedical networks: methods, applications and evaluations},
  author={Yue, Xiang and Wang, Zhen and Huang, Jingong and Parthasarathy, Srinivasan and Moosavinasab, Soheil and Huang, Yungui and Lin, Simon M and Zhang, Wen and Zhang, Ping and Sun, Huan},
  journal={Bioinformatics},
  volume={36},
  number={4},
  pages={1241--1251},
  year={2020},
  publisher={Oxford University Press}
}
#   def MashupPPI( directed: bool = False, preprocess: bool = True, load_nodes: bool = True, verbose: int = 2, cache: bool = True, cache_path: str = 'graphs/yue', version: str = 'latest', **additional_graph_kwargs: Dict ) -> grape.ensmallen.ensmallen.Graph:
View Source
def MashupPPI(
    directed: bool = False,
    preprocess: bool = True,
    load_nodes: bool = True,
    verbose: int = 2,
    cache: bool = True,
    cache_path: str = "graphs/yue",
    version: str = "latest",
    **additional_graph_kwargs: Dict
) -> Graph:
    """Return new instance of the MashupPPI graph.

    The graph is automatically retrieved from the Yue repository.	

    Parameters
    -------------------
    directed: bool = False
        Wether to load the graph as directed or undirected.
        By default false.
    preprocess: bool = True
        Whether to preprocess the graph to be loaded in 
        optimal time and memory.
    load_nodes: bool = True,
        Whether to load the nodes vocabulary or treat the nodes
        simply as a numeric range.
    verbose: int = 2,
        Wether to show loading bars during the retrieval and building
        of the graph.
    cache: bool = True
        Whether to use cache, i.e. download files only once
        and preprocess them only once.
    cache_path: str = "graphs"
        Where to store the downloaded graphs.
    version: str = "latest"
        The version of the graph to retrieve.	
    additional_graph_kwargs: Dict
        Additional graph kwargs.

    Returns
    -----------------------
    Instace of MashupPPI graph.

	References
	---------------------
	Please cite the following if you use the data:
	
	```bib
	@article{yue2020graph,
	  title={Graph embedding on biomedical networks: methods, applications and evaluations},
	  author={Yue, Xiang and Wang, Zhen and Huang, Jingong and Parthasarathy, Srinivasan and Moosavinasab, Soheil and Huang, Yungui and Lin, Simon M and Zhang, Wen and Zhang, Ping and Sun, Huan},
	  journal={Bioinformatics},
	  volume={36},
	  number={4},
	  pages={1241--1251},
	  year={2020},
	  publisher={Oxford University Press}
	}
	```
    """
    return AutomaticallyRetrievedGraph(
        graph_name="MashupPPI",
        repository="yue",
        version=version,
        directed=directed,
        preprocess=preprocess,
        load_nodes=load_nodes,
        verbose=verbose,
        cache=cache,
        cache_path=cache_path,
        additional_graph_kwargs=additional_graph_kwargs
    )()

Return new instance of the MashupPPI graph.

The graph is automatically retrieved from the Yue repository.

Parameters
  • directed (bool = False): Wether to load the graph as directed or undirected. By default false.
  • preprocess (bool = True): Whether to preprocess the graph to be loaded in optimal time and memory.
  • load_nodes (bool = True,): Whether to load the nodes vocabulary or treat the nodes simply as a numeric range.
  • verbose (int = 2,): Wether to show loading bars during the retrieval and building of the graph.
  • cache (bool = True): Whether to use cache, i.e. download files only once and preprocess them only once.
  • cache_path (str = "graphs"): Where to store the downloaded graphs.
  • version (str = "latest"): The version of the graph to retrieve.
  • additional_graph_kwargs (Dict): Additional graph kwargs.
Returns

- Instace of MashupPPI graph.: References

Please cite the following if you use the data:

@article{yue2020graph,
  title={Graph embedding on biomedical networks: methods, applications and evaluations},
  author={Yue, Xiang and Wang, Zhen and Huang, Jingong and Parthasarathy, Srinivasan and Moosavinasab, Soheil and Huang, Yungui and Lin, Simon M and Zhang, Wen and Zhang, Ping and Sun, Huan},
  journal={Bioinformatics},
  volume={36},
  number={4},
  pages={1241--1251},
  year={2020},
  publisher={Oxford University Press}
}
#   def NDFRTDDA( directed: bool = False, preprocess: bool = True, load_nodes: bool = True, verbose: int = 2, cache: bool = True, cache_path: str = 'graphs/yue', version: str = 'latest', **additional_graph_kwargs: Dict ) -> grape.ensmallen.ensmallen.Graph:
View Source
def NDFRTDDA(
    directed: bool = False,
    preprocess: bool = True,
    load_nodes: bool = True,
    verbose: int = 2,
    cache: bool = True,
    cache_path: str = "graphs/yue",
    version: str = "latest",
    **additional_graph_kwargs: Dict
) -> Graph:
    """Return new instance of the NDFRTDDA graph.

    The graph is automatically retrieved from the Yue repository.	

    Parameters
    -------------------
    directed: bool = False
        Wether to load the graph as directed or undirected.
        By default false.
    preprocess: bool = True
        Whether to preprocess the graph to be loaded in 
        optimal time and memory.
    load_nodes: bool = True,
        Whether to load the nodes vocabulary or treat the nodes
        simply as a numeric range.
    verbose: int = 2,
        Wether to show loading bars during the retrieval and building
        of the graph.
    cache: bool = True
        Whether to use cache, i.e. download files only once
        and preprocess them only once.
    cache_path: str = "graphs"
        Where to store the downloaded graphs.
    version: str = "latest"
        The version of the graph to retrieve.	
    additional_graph_kwargs: Dict
        Additional graph kwargs.

    Returns
    -----------------------
    Instace of NDFRTDDA graph.

	References
	---------------------
	Please cite the following if you use the data:
	
	```bib
	@article{yue2020graph,
	  title={Graph embedding on biomedical networks: methods, applications and evaluations},
	  author={Yue, Xiang and Wang, Zhen and Huang, Jingong and Parthasarathy, Srinivasan and Moosavinasab, Soheil and Huang, Yungui and Lin, Simon M and Zhang, Wen and Zhang, Ping and Sun, Huan},
	  journal={Bioinformatics},
	  volume={36},
	  number={4},
	  pages={1241--1251},
	  year={2020},
	  publisher={Oxford University Press}
	}
	```
    """
    return AutomaticallyRetrievedGraph(
        graph_name="NDFRTDDA",
        repository="yue",
        version=version,
        directed=directed,
        preprocess=preprocess,
        load_nodes=load_nodes,
        verbose=verbose,
        cache=cache,
        cache_path=cache_path,
        additional_graph_kwargs=additional_graph_kwargs
    )()

Return new instance of the NDFRTDDA graph.

The graph is automatically retrieved from the Yue repository.

Parameters
  • directed (bool = False): Wether to load the graph as directed or undirected. By default false.
  • preprocess (bool = True): Whether to preprocess the graph to be loaded in optimal time and memory.
  • load_nodes (bool = True,): Whether to load the nodes vocabulary or treat the nodes simply as a numeric range.
  • verbose (int = 2,): Wether to show loading bars during the retrieval and building of the graph.
  • cache (bool = True): Whether to use cache, i.e. download files only once and preprocess them only once.
  • cache_path (str = "graphs"): Where to store the downloaded graphs.
  • version (str = "latest"): The version of the graph to retrieve.
  • additional_graph_kwargs (Dict): Additional graph kwargs.
Returns

- Instace of NDFRTDDA graph.: References

Please cite the following if you use the data:

@article{yue2020graph,
  title={Graph embedding on biomedical networks: methods, applications and evaluations},
  author={Yue, Xiang and Wang, Zhen and Huang, Jingong and Parthasarathy, Srinivasan and Moosavinasab, Soheil and Huang, Yungui and Lin, Simon M and Zhang, Wen and Zhang, Ping and Sun, Huan},
  journal={Bioinformatics},
  volume={36},
  number={4},
  pages={1241--1251},
  year={2020},
  publisher={Oxford University Press}
}
#   def ClinTermCOOC( directed: bool = False, preprocess: bool = True, load_nodes: bool = True, verbose: int = 2, cache: bool = True, cache_path: str = 'graphs/yue', version: str = 'latest', **additional_graph_kwargs: Dict ) -> grape.ensmallen.ensmallen.Graph:
View Source
def ClinTermCOOC(
    directed: bool = False,
    preprocess: bool = True,
    load_nodes: bool = True,
    verbose: int = 2,
    cache: bool = True,
    cache_path: str = "graphs/yue",
    version: str = "latest",
    **additional_graph_kwargs: Dict
) -> Graph:
    """Return new instance of the ClinTermCOOC graph.

    The graph is automatically retrieved from the Yue repository.	

    Parameters
    -------------------
    directed: bool = False
        Wether to load the graph as directed or undirected.
        By default false.
    preprocess: bool = True
        Whether to preprocess the graph to be loaded in 
        optimal time and memory.
    load_nodes: bool = True,
        Whether to load the nodes vocabulary or treat the nodes
        simply as a numeric range.
    verbose: int = 2,
        Wether to show loading bars during the retrieval and building
        of the graph.
    cache: bool = True
        Whether to use cache, i.e. download files only once
        and preprocess them only once.
    cache_path: str = "graphs"
        Where to store the downloaded graphs.
    version: str = "latest"
        The version of the graph to retrieve.	
    additional_graph_kwargs: Dict
        Additional graph kwargs.

    Returns
    -----------------------
    Instace of ClinTermCOOC graph.

	References
	---------------------
	Please cite the following if you use the data:
	
	```bib
	@article{yue2020graph,
	  title={Graph embedding on biomedical networks: methods, applications and evaluations},
	  author={Yue, Xiang and Wang, Zhen and Huang, Jingong and Parthasarathy, Srinivasan and Moosavinasab, Soheil and Huang, Yungui and Lin, Simon M and Zhang, Wen and Zhang, Ping and Sun, Huan},
	  journal={Bioinformatics},
	  volume={36},
	  number={4},
	  pages={1241--1251},
	  year={2020},
	  publisher={Oxford University Press}
	}
	```
    """
    return AutomaticallyRetrievedGraph(
        graph_name="ClinTermCOOC",
        repository="yue",
        version=version,
        directed=directed,
        preprocess=preprocess,
        load_nodes=load_nodes,
        verbose=verbose,
        cache=cache,
        cache_path=cache_path,
        additional_graph_kwargs=additional_graph_kwargs
    )()

Return new instance of the ClinTermCOOC graph.

The graph is automatically retrieved from the Yue repository.

Parameters
  • directed (bool = False): Wether to load the graph as directed or undirected. By default false.
  • preprocess (bool = True): Whether to preprocess the graph to be loaded in optimal time and memory.
  • load_nodes (bool = True,): Whether to load the nodes vocabulary or treat the nodes simply as a numeric range.
  • verbose (int = 2,): Wether to show loading bars during the retrieval and building of the graph.
  • cache (bool = True): Whether to use cache, i.e. download files only once and preprocess them only once.
  • cache_path (str = "graphs"): Where to store the downloaded graphs.
  • version (str = "latest"): The version of the graph to retrieve.
  • additional_graph_kwargs (Dict): Additional graph kwargs.
Returns

- Instace of ClinTermCOOC graph.: References

Please cite the following if you use the data:

@article{yue2020graph,
  title={Graph embedding on biomedical networks: methods, applications and evaluations},
  author={Yue, Xiang and Wang, Zhen and Huang, Jingong and Parthasarathy, Srinivasan and Moosavinasab, Soheil and Huang, Yungui and Lin, Simon M and Zhang, Wen and Zhang, Ping and Sun, Huan},
  journal={Bioinformatics},
  volume={36},
  number={4},
  pages={1241--1251},
  year={2020},
  publisher={Oxford University Press}
}
#   def StringPPI( directed: bool = False, preprocess: bool = True, load_nodes: bool = True, verbose: int = 2, cache: bool = True, cache_path: str = 'graphs/yue', version: str = 'latest', **additional_graph_kwargs: Dict ) -> grape.ensmallen.ensmallen.Graph:
View Source
def StringPPI(
    directed: bool = False,
    preprocess: bool = True,
    load_nodes: bool = True,
    verbose: int = 2,
    cache: bool = True,
    cache_path: str = "graphs/yue",
    version: str = "latest",
    **additional_graph_kwargs: Dict
) -> Graph:
    """Return new instance of the StringPPI graph.

    The graph is automatically retrieved from the Yue repository.	

    Parameters
    -------------------
    directed: bool = False
        Wether to load the graph as directed or undirected.
        By default false.
    preprocess: bool = True
        Whether to preprocess the graph to be loaded in 
        optimal time and memory.
    load_nodes: bool = True,
        Whether to load the nodes vocabulary or treat the nodes
        simply as a numeric range.
    verbose: int = 2,
        Wether to show loading bars during the retrieval and building
        of the graph.
    cache: bool = True
        Whether to use cache, i.e. download files only once
        and preprocess them only once.
    cache_path: str = "graphs"
        Where to store the downloaded graphs.
    version: str = "latest"
        The version of the graph to retrieve.	
    additional_graph_kwargs: Dict
        Additional graph kwargs.

    Returns
    -----------------------
    Instace of StringPPI graph.

	References
	---------------------
	Please cite the following if you use the data:
	
	```bib
	@article{yue2020graph,
	  title={Graph embedding on biomedical networks: methods, applications and evaluations},
	  author={Yue, Xiang and Wang, Zhen and Huang, Jingong and Parthasarathy, Srinivasan and Moosavinasab, Soheil and Huang, Yungui and Lin, Simon M and Zhang, Wen and Zhang, Ping and Sun, Huan},
	  journal={Bioinformatics},
	  volume={36},
	  number={4},
	  pages={1241--1251},
	  year={2020},
	  publisher={Oxford University Press}
	}
	```
    """
    return AutomaticallyRetrievedGraph(
        graph_name="StringPPI",
        repository="yue",
        version=version,
        directed=directed,
        preprocess=preprocess,
        load_nodes=load_nodes,
        verbose=verbose,
        cache=cache,
        cache_path=cache_path,
        additional_graph_kwargs=additional_graph_kwargs
    )()

Return new instance of the StringPPI graph.

The graph is automatically retrieved from the Yue repository.

Parameters
  • directed (bool = False): Wether to load the graph as directed or undirected. By default false.
  • preprocess (bool = True): Whether to preprocess the graph to be loaded in optimal time and memory.
  • load_nodes (bool = True,): Whether to load the nodes vocabulary or treat the nodes simply as a numeric range.
  • verbose (int = 2,): Wether to show loading bars during the retrieval and building of the graph.
  • cache (bool = True): Whether to use cache, i.e. download files only once and preprocess them only once.
  • cache_path (str = "graphs"): Where to store the downloaded graphs.
  • version (str = "latest"): The version of the graph to retrieve.
  • additional_graph_kwargs (Dict): Additional graph kwargs.
Returns

- Instace of StringPPI graph.: References

Please cite the following if you use the data:

@article{yue2020graph,
  title={Graph embedding on biomedical networks: methods, applications and evaluations},
  author={Yue, Xiang and Wang, Zhen and Huang, Jingong and Parthasarathy, Srinivasan and Moosavinasab, Soheil and Huang, Yungui and Lin, Simon M and Zhang, Wen and Zhang, Ping and Sun, Huan},
  journal={Bioinformatics},
  volume={36},
  number={4},
  pages={1241--1251},
  year={2020},
  publisher={Oxford University Press}
}