Prostate cancer biomarker selection through a novel combination of sequential global thresholding, particle swarm optimization, and PNN classification of MS-spectra

Panagiotis Bougioukos, Dionisis Cavouras, Antonis Daskalakis, Spiros Kostopoulos, George Nikiforidis, Anastasios Bezerianos

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

Proteomic analysis using mass spectrometry data is a powerful tool for biomarker discovery. However, proteomic data suffers from two crucial problems i/ are inherently very noisy and ii/ the number of features that finally characterize each spectrum is usually very large. In the present study, a well-established framework of data preprocessing steps was developed to deal with the problem of noise, incorporating smoothing, normalization, peak detection, and peak alignment algorithms. In addition, to alleviate the problem of feature dimensionality, a novel iterative peak selection method was developed for choosing peaks (features) from the preprocessed spectra, based on sequential global thresholding followed by particle swarm optimization. These features were fed into a probabilistic neural network algorithm, in order to discriminate healthy from prostate cancer cases and, thus, to determine, through the algorithm's optimal design, biomarkers related to prostate cancer.

Original languageEnglish (US)
Title of host publicationProceedings - International Conference on Tools with Artificial Intelligence, ICTAI
Pages85-90
Number of pages6
Volume1
DOIs
Publication statusPublished - 2007
Externally publishedYes
Event19th IEEE International Conference on Tools with Artificial Intelligence, ICTAI 2007 - Patras, Greece
Duration: Oct 29 2007Oct 31 2007

Other

Other19th IEEE International Conference on Tools with Artificial Intelligence, ICTAI 2007
CountryGreece
CityPatras
Period10/29/0710/31/07

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ASJC Scopus subject areas

  • Engineering(all)

Cite this

Bougioukos, P., Cavouras, D., Daskalakis, A., Kostopoulos, S., Nikiforidis, G., & Bezerianos, A. (2007). Prostate cancer biomarker selection through a novel combination of sequential global thresholding, particle swarm optimization, and PNN classification of MS-spectra. In Proceedings - International Conference on Tools with Artificial Intelligence, ICTAI (Vol. 1, pp. 85-90). [4410267] https://doi.org/10.1109/ICTAI.2007.21