Integrating multiple fitting regression and Bayes decision for cancer diagnosis with transcriptomic data from tumor-educated blood platelets. Issue 19 (30th August 2017)

• Record Type: Journal Article
• Authors: Huang, Guangzao; Yuan, Mingshun; Chen, Moliang; Li, Lei; You, Wenjie; Li, Hanjie; Cai, James J.; Ji, Guoli
• Is Part Of: Analyst. Volume 142:Issue 19(2017)
• Page Start: 3588
• Page End: 3597
• Publication Date: 2017-08-30
• Subjects: Chemistry, Analytic -- Periodicals; 543
• DOI: 10.1039/c7an00944e
• Languages: English
• ISSNs: 0003-2654

Abstract

Abstract : The application of machine learning in cancer diagnostics has shown great promise and is of importance in clinic settings. Abstract : The application of machine learning in cancer diagnostics has shown great promise and is of importance in clinic settings. Here we consider applying machine learning methods to transcriptomic data derived from tumor-educated platelets (TEPs) from individuals with different types of cancer. We aim to define a reliability measure for diagnostic purposes to increase the potential for facilitating personalized treatments. To this end, we present a novel classification method called MFRB (for Multiple Fitting Regression and Bayes decision), which integrates the process of multiple fitting regression (MFR) with Bayes decision theory. MFR is first used to map multidimensional features of the transcriptomic data into a one-dimensional feature. The probability density function of each class in the mapped space is then adjusted using the Gaussian probability density function. Finally, the Bayes decision theory is used to build a probabilistic classifier with the estimated probability density functions. The output of MFRB can be used to determine which class a sample belongs to, as well as to assign a reliability measure for a given class. The classical support vector machine (SVM) and probabilistic SVM (PSVM) are used to evaluate the performance of the proposed method with simulated and real TEP datasets. Our results indicate that the proposed MFRB method achieves the best performance compared to SVM and PSVM, mainly due to its strong generalization ability for limited, imbalanced, and noisy data.