Hybrid Markov Clustering Algorithms

This project consist of 2 Hybrid Clustering Method namely MLR-CAw(1.0) and SR-CAw(2.0). 2.0 was mainly focused on SR-CAw, using SR-MCL as the clustering algorithm to the hybrid FSWeight - SR-MCL - Core Attachment combination, differing from V1 yung MLR-MCL instead. SR-CAw is not an improvement of MLR-CAw but rather a variant with a different approach. See the accompanying paper for the discussion of the method and the algorithms.

🏠 related page

Install

for python packages

pip install -r requirement.txt

Usage

basic usage from FSWeighting to Core Attachment

make 
./mclcaw.sh <unweighted_graph_file> <threshold> <alpha> <gamma> <mode> <redundancy> <beta> <quality>

A typical top-level directory layout

understand you file structure and contents(Team 2.0 learnings #6450)

 .
 └──     3.package/                    #Contains important files such as existing relevant libraries from metis, datasets
 │  └────     Lib/                         #Metis Libraries
 │  └────     gold_standard/               #CYC2008 Dataset
 │  └────     graphs/                      #BioGRID and DIP datasets
 │  └────     realGraphNodeName/           #Protein Mappings
 └──     bin/                          #Contains excecutables
 │  └────     Linux/                       #Executables for Linux
 │  └────     OSX/                         #Executables for macOS
 └──     clustering/                   #output directory of mcl variant
 └──     coreattachments/              #output directory of Core-Attachment
 └──     networks/                     #output directory of FSWeight when mclcaw.sh is used
 └──     scoring/                      #directory of F-Scoring, a separate step, see README 
 └──     src/                          #contains source file except CAw.py(located at bin)
 └──     varyingparameters/            #contains varying parameters files used by V 1.0

Author

👤 (1.0) Beltran JC, Montes C, Villar JJ, Valdez AR, (2.0) Cortez JJM, Estrella JKF, Octaviano M, Fabilloren SR, Villar JJ

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