Practical Screening Method for Cancer Gene Diagnosis, How to Choose Cancer and Normal Patients by Four Principles

We developed a new theory of discriminant analysis (Theory1). Physicians can use it for practical medical  diagnoses. Only Revised IP Optimal-LDF (RIP) obtains the minimum number of misclassification (MNM). RIP can  discriminate linearly separable data (LSD) theoretically. It discriminated against 169 microarrays with two classes and found  that 169 MNMs are zero and LSD. It can split high-dimensional arrays into many small LSD with less than n (patient’s  number) genes that are the candidates of multivariate oncogenes. We completed a new theory of high-dimensional gene data  analysis (Theory2). A 100-fold Cross-Validation (Method1) can rank all candidates for the importance of diagnosis. Thus, if  physicians firstly use Theory2 as the screening method, they can start their medical studies with the correct small sizes of  candidates. This paper analyzes four arrays in detail and proposes correctly choosing cancer and normal patients using four  principles. 

The authors declare no conflict of interest.

Not applicable

The datasets used in this study are openly available at [repository link] and the source code is available on GitHub at [GitHub link].

This work did not receive any external funding.