NDLI: Mass spectrometry-based proteomic pattern analysis for prostate cancer detection using neural networks with statistical significance test-based feature selection

Content Provider	IEEE Xplore Digital Library
Author	Xu, Q. Mohamed, S.S. Salama, M.M.A. Kamel, M. Rizkalla, K.
Copyright Year	2009
Description	Author affiliation: ECE Dept., University of Waterloo, Ontario, Canada (Xu, Q.; Mohamed, S.S.; Salama, M.M.A.; Kamel, M.) \|\| Pathology Dept., Schulich School of Medicine and Dentistry, University of Western Ontario, London, Canada (Rizkalla, K.)
Abstract	Mass spectrometry-based proteomics provides a promising approach for accurate diagnosis of different diseases. However, there are some problems in the mass spectral data such as huge volume, data complexity and the presence of noise. These problems make analyzing the proteomic pattern difficult. In this paper, a neural network-based system is proposed for proteomic pattern analysis for prostate cancer screening. The system consists of three stages: feature selection based on statistical significant test, classification by a Radial Basis Function Neural Network (RBFNN) and a probabilistic neural network (PNN), and finally results optimization through ROC analysis. The experimental results show that the proposed system's performance is excellent in comparison with the existing tools. The high sensitivity (97.1%) and specificity (96.8%) of the proposed system when combined with prostatic biopsy are expected to help in early detection of prostate cancer.
Starting Page	837
Ending Page	842
File Size	515028
Page Count	6
File Format	PDF
ISBN	9781424438778
DOI	10.1109/TIC-STH.2009.5444384
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2009-09-26
Publisher Place	Canada
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	System testing Probabilistic Neural Network Mass spectroscopy Cancer detection Significance Test-based Feature Selection Radial Basis Function Neural Network Diseases System performance Neural networks Proteomics Radial basis function networks Pattern analysis Prostate cancer Mass spectrometry Prostate
Content Type	Text
Resource Type	Article

Sl.	Authority	Responsibilities	Communication Details
1	Ministry of Education (GoI), Department of Higher Education	Sanctioning Authority	https://www.education.gov.in/ict-initiatives
2	Indian Institute of Technology Kharagpur	Host Institute of the Project: The host institute of the project is responsible for providing infrastructure support and hosting the project	https://www.iitkgp.ac.in
3	National Digital Library of India Office, Indian Institute of Technology Kharagpur	The administrative and infrastructural headquarters of the project	Dr. B. Sutradhar bsutra@ndl.gov.in
4	Project PI / Joint PI	Principal Investigator and Joint Principal Investigators of the project	Dr. B. Sutradhar bsutra@ndl.gov.in Prof. Saswat Chakrabarti will be added soon
5	Website/Portal (Helpdesk)	Queries regarding NDLI and its services	support@ndl.gov.in
6	Contents and Copyright Issues	Queries related to content curation and copyright issues	content@ndl.gov.in
7	National Digital Library of India Club (NDLI Club)	Queries related to NDLI Club formation, support, user awareness program, seminar/symposium, collaboration, social media, promotion, and outreach	clubsupport@ndl.gov.in
8	Digital Preservation Centre (DPC)	Assistance with digitizing and archiving copyright-free printed books	dpc@ndl.gov.in
9	IDR Setup or Support	Queries related to establishment and support of Institutional Digital Repository (IDR) and IDR workshops	idr@ndl.gov.in

A significance test-based feature selection method for the detection of prostate cancer from proteomic patterns (2004).

Comparative study between radial basis probabilistic neural networks and radial basis function neural.

Speech recognition with a competitive Probabilistic Radial Basis Neural Network

Wavelet Transform and Bagging Predictor Approaches to Cancer Identification from Mass Spectrometry-Based Proteomic Data

Analysis of Radial Basis Function network for localization framework in Wireless Sensor Networks

Training strategies for Radial Basis Function Neural Networks: A study applied to prostate cancer prognosis

Human posture recognition using active contours and radial basis function neural network

Mammographic Image Analysis by Use the Rank M-Type Radial Basis Function Network

Selection of Radial Basis Functions via Genetic Algorithms in Pattern Recognition Problems

Mass spectrometry-based proteomic pattern analysis for prostate cancer detection using neural networks with statistical significance test-based feature selection

Similar Documents

A significance test-based feature selection method for the detection of prostate cancer from proteomic patterns (2004).

Comparative study between radial basis probabilistic neural networks and radial basis function neural.

Speech recognition with a competitive Probabilistic Radial Basis Neural Network

Wavelet Transform and Bagging Predictor Approaches to Cancer Identification from Mass Spectrometry-Based Proteomic Data

Analysis of Radial Basis Function network for localization framework in Wireless Sensor Networks

Training strategies for Radial Basis Function Neural Networks: A study applied to prostate cancer prognosis

Human posture recognition using active contours and radial basis function neural network

Mammographic Image Analysis by Use the Rank M-Type Radial Basis Function Network

Selection of Radial Basis Functions via Genetic Algorithms in Pattern Recognition Problems

Mass spectrometry-based proteomic pattern analysis for prostate cancer detection using neural networks with statistical significance test-based feature selection