Abstract
Detection of different types of cancers is important in clinical diagnosis and treatment. Leukemia is one of the cancers that has different subtypes: acute lymphoblastic leukemia (ALL) and acute myeloid leukemia (AML). The detection of these subtypes according to different genetic markups in leukemia patients will lead to individualized therapies. Gene expression analysis has been used for the study of leukemia patients, and a variety of approaches have been developed to classify leukemia subtypes. In this paper, we propose a novel compressive sensing (CS) based approach for the subtyping of leukemia. The CS method is an emerging approach in statistics and mathematical signal analysis, which enables the reconstruction of signals from a small set of incoherent projections. We develop a CS based detector to classify ALL and AML, based on ours selected genes out of 7129 samples. The accuracy of the classification is as high as 97% evaluated with cross validation method among 38 subjects (27 ALL and 11 AML). This work demonstrates that the CS method can be effectively used to detect subtypes of leukemia subjects, implying improved accuracy of diagnosing leukemia patients.
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