There are three parts of Unicorn pipeline:

1. Unicorn algorithm: produce integrated network;
2. CliXO: accept the networks to infer the ontologies;
3. Unicorn_measurement: automatically measure the performance (recall, precision and F-score);

Here we provide the part 1 and part 3, you also need to download part 2 (CliXO algorithm[1]) in https://github.com/mhk7/clixo_0.3.
First, download all the programs, and unzip them under the same workspace. For these three parts:

1. Unicorn algorithm
%%Unicorn algorithm to produce integrated networks
%inputs:
%   term: the input term index, e.g. GO:xxxxxxx
%   wb: which branch does the inputted term on: 1--BP, 2--CC, 3--MF
%outputs:
%   the integrated network and the filtered original networks are produced and stored in the INT_xxxxxxx folder, 
%   e.g. Output_0071840_ind_cca_ALL.txt
%demo:
%   Unicorn('GO:0071840',1);
%   After running, please find the files in INT_0071840 folder in the current workspace

2. Clixo algorithm
%%For this part you need refer to [1] to know more details about clixo
%Inputs:
%   The output file name of Unicorn, e.g. Output_0071840_ind_cca_ALL.txt
%   Alpha, e.g. 0.01
%   Beta, e.g. 0.5
%Outputs:
%   The ontology structure and annotations, e.g. go_bp_0071840_intermediate_ind_cca_ALL_0.01_0.5.txt
%Demo:
%   ./clixo   Output_0071840_ind_cca_ALL.txt  0.01    0.5 >   go_bp_0071840_intermediate_ind_cca_ALL_0.01_0.5.txt

3. Unicorn_measurement
%%This measurement program process the results files of clixo algorithm,
%%These results must be stored in the 'results' folder;
%%And these results should be specifically named, e.g. 'go_bp_0071840_intermediate_ind_cca_ALL_0.01.txt'
%input:
%   term_idx: the index of GO term, e.g. '0071840' for GO:0071840
%output:
%   the results are stored in Unicorn_measurement/exp_results/0071840_results.mat
%   The struct data 'scores' in this '.mat' contains:
%       'term':         term name,
%       'recall':       recall matrix,
%       'precision':    precision matrix,
%       'f_measure':    F-score matrix,
%       'row_pat':      labels of rows for the above three matrices,
%       'col_pat':      labels of columns for the above three matrices
%demo:
%   Unicorn_measurement('0071840');


Important notes:
To make sure the smooth running of Unicorn and Unicorn_measurement, please strictly follow our naming rules and put the files into the correct folder. Sorry for the inconvenience.
a) The output file of CliXO / input file of Unicorn_measurement should be named as:
File name format: go_p1_p2_intermediate_p3_p4_p5_p6.txt
	p1: bp for BP sub-ontology, cc for CC sub-ontology, mf for MF sub-ontology;
	p2: term index, e.g. 0071840 for GO:0071840;
	p3: network name, e.g. ind_cca: integrated network, ori_net1-4: DRYGIN, SMD, YeastNet, BioGRID;
	p4: range, e.g. ALL for whole ontology, KI for training part, Dhh for the leave-out part;
	p5: alpha value, e.g. 0.01;
	p6: beta value, e.g. 0.5.

b) The output file of CliXO / input file of Unicorn_measurement should be placed under Unicorn_measurement/exp_results/.

c) Unicorn folder and Unicorn_measurement folder should be placed under the same workspace.



Demos:
1. running Unicorn algorithm
change workspace to Unicorn/, run the Unicorn function as follow:
Unicorn('GO:1901363',3); % term GO:1901363 is in MF sub-ontology,e.g. 3;
then the output file will be stored under Unicorn/INT_1901363/

2. running CliXO algorithm
./clixo   Output_1901363_ind_cca_ALL.txt  0.01    0.5 >   go_cc_1901363_intermediate_ind_cca_ALL_0.01_0.5.txt

3. running Unicorn_measurement algorithm
store the output file of CliXO under Unicorn_measurement/results/, and then change workspace to Unicorn_measurement/, run the function as follow:
Unicorn_measurement('0071840')




Reference:
[1]Kramer M, Dutkowski J, Yu M, et al. Inferring gene ontologies from pairwise similarity data[J]. Bioinformatics, 2014, 30(12): i34-i42.