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.
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Disclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.