Optimized spatial filters as a new method for mass spectrometry-based cancer diagnosis |
| |
| Affiliation: | 1. Department of Civil Engineering, Noorul Islam University, Tamil Nadu, India;2. Department of Electrical Engineering, College of Engineering, Pathanapuram, Kerala, India;3. Department of Civil Engineering, MEPCO SCHLENK, Sivakasi, Tamil Nadu, India;4. Department of Computer Engineering and Mathematics, University Rovira i Virgili, Spain;1. College of Information Science and Engineering, Ocean University of China, 23 Xianggang Road East, Qingdao 266100, China;2. Department of Automatic Control and Systems Engineering, The University of Sheffield, Sheffield S1 3JD, UK;3. Department of Engineering, University of Leicester, University Road, Leicester LE1 7RH, UK;4. Advanced Manufacturing Research Centre with Boeing, The University of Sheffield, Advanced Manufacturing Park, l Wallis Way, Catcliffe, Rotherham S60 5TZ, UK;5. The University of Sheffield, Department of Mechanical Engineering, Mappin Street, Sheffield S1 3JD, UK;1. Department of Computer Sc. & Information Technology, Institute of Technical Education and Research, Siksha ‘O‘ Anusandhan, University, Khandagiri Square, Bhubaneswar, 751030 Odisha, India;2. Department of Computer Sc. & Engineering, Silicon Institute of Technology, Bhubaneswar, 751024 Odisha, India;1. Department of Computer Science and Artificial Intelligence, University of Granada, Granada, Spain;2. Leeds School of Business, University of Colorado, Boulder, USA;3. Department of Statistics and Operations Research, University of Valencia, Valencia, Spain;4. Department of Computer Science, University of Extremadura, Mérida, Spain;5. Department of Computing and Numerical Analysis, University of Córdoba, Córdoba, Spain |
| |
| Abstract: | In the past two decades, mass spectrometry-based identification of serum proteomic patterns has emerged as a new diagnostic tool for the early detection of various types of cancers. However, due to its high dimensionality, the analysis of mass spectrometry data poses considerable challenges. Existing methods proposed for the analysis of mass spectrometry data usually consist of a number of steps. In this study, a comparatively simple but efficient method, namely, an optimal spatial filter (OSF) method, is proposed for the classification of mass spectrometry data. The newly proposed method is based on the theory of common spatial patterns (CSPs), which are widely used to classify motor imagery EEG signals in brain-computer interface (BCI) applications. The CSP method aims to find spatial filters to project the data into a new space in which optimal discrimination between classes is achieved. Although it has been shown that the CSP method performs quite well in classifying motor imagery EEG signals, it has a major drawback. In the CSP method, the between-class variance is maximized, but the minimization of within-class variance is ignored. As a result, the projected data may have large within-class variances. To overcome this problem, in this study, optimal filters are found by using the differential evolution (DE) algorithm. For the fitness function of the differential evolution algorithm, a divergence analysis is used. In the divergence analysis, both the between-class and within-class distributions of the projected data are considered. The experimental results obtained using publicly available mass spectrometry datasets showed that, when compared to existing methods, the proposed OSF method is quite simple and achieves the minimum classification error for each dataset. Furthermore, the proposed OSF method highlights the importance of certain parts of the spectra, which is highly valuable for the identification of biomarkers that lie outside the pathological pathway of the disease. |
| |
| Keywords: | Common spatial patterns Mass spectrometry Spectroscopy Cancer diagnosis |
| 本文献已被 ScienceDirect 等数据库收录！ |
|
