TY - GEN
T1 - Prostate cancer biomarker selection through a novel combination of sequential global thresholding, particle swarm optimization, and PNN classification of MS-spectra
AU - Bougioukos, Panagiotis
AU - Cavouras, Dionisis
AU - Daskalakis, Antonis
AU - Kostopoulos, Spiros
AU - Nikiforidis, George
AU - Bezerianos, Anastasios
PY - 2007
Y1 - 2007
N2 - 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.
AB - 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.
UR - http://www.scopus.com/inward/record.url?scp=48649094799&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=48649094799&partnerID=8YFLogxK
U2 - 10.1109/ICTAI.2007.21
DO - 10.1109/ICTAI.2007.21
M3 - Conference contribution
AN - SCOPUS:48649094799
SN - 076953015X
SN - 9780769530154
T3 - Proceedings - International Conference on Tools with Artificial Intelligence, ICTAI
SP - 85
EP - 90
BT - Proceedings 19th IEEE International Conference on Tools with Artificial Intelligence, ICTAI 2007
T2 - 19th IEEE International Conference on Tools with Artificial Intelligence, ICTAI 2007
Y2 - 29 October 2007 through 31 October 2007
ER -