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Classification of Relapse Ovarian Cancer on MALDI-TOF Mass Spectrometry Data

Ovarian cancer recurs at the rate of 75% within a few months or several years later after therapy. Early recurrence, though responding better to treatment, is difficult to detect. Recently, high-resolution MALDI-TOF (matrix-assisted laser desorption/ionization time-of-flight) mass spectrometry has shown promise as a screening tool for detecting discriminatory protein patterns. The major computational obstacle in analyzing MALDI-TOF data is a large number of mass/charge peaks (a.k.a. features, data points). To tackle this problem, we have developed a multi-step strategy for data preprocessing and afterwards feature selection. The preprocessing is composed of binning, baseline correction, and normalization. For the preprocessed data, we propose a new feature subset selection method. Our scheme is applied to the analysis of ovarian cancer dataset to predict early relapse in ovarian cancer. To validate the performance of the proposed algorithm, experiments are performed in comparison with other feature selection and classification methods. We show that our proposed approach outperforms other algorithms