MILP_k: Mixed Integer Linear Programming for multiple-biomarker panel identification

  • Version 0.0.1
  • Last updated: May 6, 2015.


Multi-biomarker panels can capture the nonlinear synergy among biomarkers and they are important to aid in the early diagnosis and ultimately battle complex diseases. However, the multi-biomarker panel identification is challenging from case and control data. For example, the exhaustive search method is computationally infeasible when the data dimension is high. Here, we aim to propose a novel method to identify multi-biomarker panel and apply it to distinguish colorectal cancers (CRC) from benign colorectal tumors based on serum measurement.


We developed a novel mixed integer programming model for multi-biomarker panel detection. This model directly minimizes the classification error (maximizes the classification accuracy) given the number of biomarkers in the optimal multi-biomarker panel. This mixed integer programming model allows us to go through all the optimal combinations by varying parameter from 1 to n. Moreover, we can check their accuracy and compare the selected combinations. In particular, an optimal multi-biomarker panel can be selected by balancing the parameter k and the classification accuracy.


CPLEX Optimizer is required in your MATLAB. CPLEX is available on the website:

This version of the program is in very preliminary stage and provided just for testing purpose. The program is still under development.


The dataset used in our paper serves as an example to demonstrate the implementation for MILP_k. The raw data(.xlsx) and processed data(.mat) are available as follows.


  • Meng Zou, Peng-Jun Zhang, Xin-Yu Wen, Luonan Chen, Ya-Ping Tian, and Yong Wang, A novel mixed integer programming for multi-biomarker panel identification by distinguishing malignant from benign colorectal tumors. Methods, In submission.

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