hocmo 1.0.1

A Generalized Higher-Order Correlation Model (HOCMO) tool to generate scores modeling the strength of the relationship between triplicate entities using a tensor-based approach

Higher-Order Correlation Model

Description:

A Generalized Higher-Order Correlation Model (HOCMO) tool to generate scores modeling the strength of the relationship among triplicate or quadruplet entities using a tensor-based approach

Prerequisites:

Developmental environment is provided via dockerfile. We also provide a requirements.txt if a local environment is preferred. Package is also available for installation on PyPi.

Installation:

pip install hocmo

Contribution:

License:

MIT License Copyright (c) 2018 Mollah Lab Permission is hereby granted, free of charge, to any person obtaining a copy of this software and associated documentation files (the "Software"), to deal in the Software without restriction, including without limitation the right to use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies of the Software, and to permit persons to whom the Software is furnished to do so, subject to the following conditions: The above copyright notice and this permission notice shall be included in all copies or substantial portions of the Software. THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE.

Citation:

References:

• C. Boutsidis, E. Gallopoulos: SVD based initialization: A head start for nonnegative matrix factorization - Pattern Recognition, 2008

• Gauvin, Laetitia, André Panisson, and Ciro Cattuto. 2014. “Detecting the Community Structure and Activity Patterns of Temporal Networks: A Non-Negative Tensor Factorization Approach.” Edited by Yamir Moreno. PLoS ONE 9 (1): e86028.

• Bro, Rasmus, and Henk A. L. Kiers. 2003. “A New Efficient Method for Determining the Number of Components in PARAFAC Models.” Journal of Chemometrics 17 (5): 274–86. https://doi.org/10.1002/cem.801.

Contact:

• [e-mail], [e-mail]