On the Construction of Medical Test Systems Using Greedy Algorithm and Support Vector Machine

Abstract

The paper presents a formalized statement of the problem of selecting parameters and construction of a genomic classifier for medical test systems with mathematical methods of machine learning without the use of biological and medical knowledge. A method is proposed to solve this problem. The results of testing the method using microarray datasets containing information on genome-wide transcriptome of the samples of estrogen positive breast tumors are discussed. Testing showed that the quality of classification provided by the constructed test system and implemented on the basis of assessments of expression of 12 genes is not inferior to the quality of classification carried out by such test systems as OncotypeDX and MammaPrint.

miRNome of inflammatory breast cancer

“…The construction of a set of 4 miRNAs for the classification of IBC vs. non-IBC samples was performed using the approach of Galatenko et al [ 29 ]. This approach is based on the Support Vector Machine [ 30 ] with linear kernel and greedy-type transcript selection.…”