Adaptive pagerank model for protein function prediction
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calcAUC.m updated0120 Jan 20, 2016
calcMAP.m updated0120 Jan 20, 2016
colstonnz.m updated0120 Jan 20, 2016
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runBirgRank.m disp Jan 23, 2016
splitR.m updated0120 Jan 20, 2016

A Quick Start of AptRank


AptRank is a network-based method for protein function prediction. We make our codes and data available in the spirit of reproducible research. The codes are written in MATLAB/R2013a.


AptRank was tested using 4 datasets in /data directory:

  • yeast;
  • human2010;
  • fly (oversized, 345701688 bytes); and
  • human2015.

The raw datasets of yeast, human2010 and fly were downloaded from GeneMANIA-SW website: and then were re-organized for use of AptRank.

In each dataset, there are 3 matrices:

  • G, m-by-m protein-protein association network;
  • R, m-by-n protein-function annotations; and
  • H, n-by-n functoin-function hierarchy of Gene Ontology.

To load and reassemble the oversized fly dataset, please type:

load fly_G11.mat;
load fly_G12.mat;
load fly_G21.mat;
load fly_G22.mat;
G = [G11,G12;G21,G22];
load fly_RH.mat;


BirgRank is the prototype of AptRank and a direct application of PageRank on a Bi-relational graph. It is simpler, faster but less accurate than AptRank. To run BirgRank, please execute runBirgRank.m, or simply type:

% Load one of the datasets. Here take the yeast for example:
load yeast.mat;

% Split R into Rtrain and Rtest:
rho = 0.5; % the percentage of annotations used in training.
[Rtrain,Rtest] = splitR(R,rho);

% Convert H into bi-directional:
lambda = 0.5;
dH = directH(H,lambda);

% Execution:
alpha = 0.5; % PageRank coefficient
theta = 0.5; % percentage of Rtrain in seeding
mu = 0.5; % percentage of random walks staying in G
Xh = birgrank(G,Rtrain,dH,alpha,theta,mu);

% Evaluation:
auc = calcAUC(Xh,Rtrain,Rtest);
disp(['AUROC = ',num2str(auc)])
map = calcMAP(Xh,Rtrain,Rtest);
disp(['MAP = ',num2str(map)])


AptRank can provide more accurate prediction, but is more computationally expensive than BirgRank. It requires two important set-ups before execution:

  • CVX: a Matlab-based convex modeling framework (;
  • Multi-thread computing resources.

To run AptRank, please execute runAptRank.m after you download and unzip the CVX package, or follow the instruction below:

% Set up cvx
% (1) Download cvx from and unzip the package.
% (2) Add the cvx path
% (3) Set it up:

% Load one of the dataset:
load yeast.mat;

% Split R into Rtrain and Rtest:
rho = 0.5; % the percentage of annotations used in training.
[Rtrain,Rtest] = splitR(R,rho);

% Convert H into bi-directional:
lambda = 0.5;
dH = directH(H,lambda);

% Launch MATLAB parallel computing pool:
matlabpool('open',12); % Set the number of cores
% NOTE: If you use MATLAB/R2013b or higher version,
% please use parpool() instead of matlabpool().
% e.g., parpool('MyCluster',12);
% For more information about parpool(),
% please refer to

% Execution:
K = 8; % Markov chain iterations
S = 5; % Number of shuffles
t = 0.5; % Split percentage of Rtrian into Rfit and Reval
diffusion_type = 'twoway'; % Input either 'oneway' or 'twoway'.
Xa = aptrank(G,Rtrain,dH,K,S,t,diffusion_type);

% Evaluation:
auc = calcAUC(Xa,Rtrain,Rtest);
disp(['AUROC = ',num2str(auc)])
map = calcMAP(Xa,Rtrain,Rtest);
disp(['MAP = ',num2str(map)])

% Close MATLAB parallel computing pool:
% For MATLAB/R2013b or higher, please shut down a parallel pool using:
% p = gcp; delete(p);


If you use AptRank in your academic research, please cite:

B. Jiang, K. Kloster, D. F. Gleich, and M. Gribskov. AptRank: An Adaptive PageRank Model for Protein Function Prediction on Bi-relational Graph. Preprint:


For technical problems, please contact Biaobin Jiang (bjiang-AT-purdue-DOT-edu).