The Experts below are selected from a list of 41205 Experts worldwide ranked by ideXlab platform
Sheung Tat Fan - One of the best experts on this subject based on the ideXlab platform.
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artificial neural networks and Decision Tree Model analysis of liver cancer proteomes
Biochemical and Biophysical Research Communications, 2007Co-Authors: John M Luk, Brian Y H Lam, Nikki P Lee, Pak C Sham, Lei Chen, Jirun Peng, Xisheng Leng, Philip J R Day, Sheung Tat FanAbstract:Hepatocellular carcinoma (HCC) is a heterogeneous cancer and usually diagnosed at late advanced tumor stages of high lethality. The present study attempted to obtain a proteome-wide analysis of HCC in comparison with adjacent non-tumor liver tissues, in order to facilitate biomarkers' discovery and to investigate the mechanisms of HCC development. A cohort of 66 Chinese patients with HCC was included for proteomic profiling study by two-dimensional gel electrophoresis (2-DE) analysis. Artificial neural network (ANN) and Decision Tree (CART) data-mining methods were employed to analyze the profiling data and to delineate significant patterns and trends for discriminating HCC from non-malignant liver tissues. Protein markers were identified by tandem MS/MS. A total of 132 proteome datasets were generated by 2-DE expression profiling analysis, and each with 230 consolidated protein expression intensities. Both the data-mining algorithms successfully distinguished the HCC phenotype from other non-malignant liver samples. The detection sensitivity and specificity of ANN were 96.97% and 87.88%, while those of CART were 81.82% and 78.79%, respectively. The three biological classifiers in the CART Model were identified as cytochrome b5, heat shock 70 kDa protein 8 isoform 2, and cathepsin B. The 2-DE-based proteomic profiling approach combined with the ANN or CART algorithm yielded satisfactory performance on identifying HCC and revealed potential candidate cancer biomarkers.
Lei Chen - One of the best experts on this subject based on the ideXlab platform.
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artificial neural networks and Decision Tree Model analysis of liver cancer proteomes
Biochemical and Biophysical Research Communications, 2007Co-Authors: John M Luk, Brian Y H Lam, Nikki P Lee, Pak C Sham, Lei Chen, Jirun Peng, Xisheng Leng, Philip J R Day, Sheung Tat FanAbstract:Hepatocellular carcinoma (HCC) is a heterogeneous cancer and usually diagnosed at late advanced tumor stages of high lethality. The present study attempted to obtain a proteome-wide analysis of HCC in comparison with adjacent non-tumor liver tissues, in order to facilitate biomarkers' discovery and to investigate the mechanisms of HCC development. A cohort of 66 Chinese patients with HCC was included for proteomic profiling study by two-dimensional gel electrophoresis (2-DE) analysis. Artificial neural network (ANN) and Decision Tree (CART) data-mining methods were employed to analyze the profiling data and to delineate significant patterns and trends for discriminating HCC from non-malignant liver tissues. Protein markers were identified by tandem MS/MS. A total of 132 proteome datasets were generated by 2-DE expression profiling analysis, and each with 230 consolidated protein expression intensities. Both the data-mining algorithms successfully distinguished the HCC phenotype from other non-malignant liver samples. The detection sensitivity and specificity of ANN were 96.97% and 87.88%, while those of CART were 81.82% and 78.79%, respectively. The three biological classifiers in the CART Model were identified as cytochrome b5, heat shock 70 kDa protein 8 isoform 2, and cathepsin B. The 2-DE-based proteomic profiling approach combined with the ANN or CART algorithm yielded satisfactory performance on identifying HCC and revealed potential candidate cancer biomarkers.
Andrew Tauro - One of the best experts on this subject based on the ideXlab platform.
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positron emission tomography with selected mediastinoscopy compared to routine mediastinoscopy offers cost and clinical outcome benefits for pre operative staging of non small cell lung cancer
European Journal of Nuclear Medicine and Molecular Imaging, 2005Co-Authors: Kelvin K Yap, Kenneth S K Yap, Amanda J Byrne, Salvatore U Berlangieri, Aurora Poon, Paul Mitchell, Simon Knight, Peter C Clarke, Anthony Harris, Andrew TauroAbstract:Purpose 18F-fluorodeoxyglucose positron emission tomography (FDG-PET) imaging is an important staging procedure in patients with non-small cell lung cancer (NSCLC). We aimed to demonstrate, through a Decision Tree Model and the incorporation of real costs of each component, that routine FDG-PET imaging as a prelude to curative surgery will reduce requirements for routine mediastinoscopy and overall hospital costs.
Yishay Mansou - One of the best experts on this subject based on the ideXlab platform.
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learning Decision Trees using the fourier spectrum
SIAM Journal on Computing, 1993Co-Authors: Eyal Kushilevitz, Yishay MansouAbstract:This work gives a polynomial time algorithm for learning Decision Trees with respect to the uniform distribution. (This algorithm uses membership queries.) The Decision Tree Model that is considered is an extension of the traditional boolean Decision Tree Model that allows linear operations in each node (i.e., summation of a subset of the input variables over $GF(2)$).This paper shows how to learn in polynomial time any function that can be approximated (in norm $L_2 $) by a polynomially sparse function (i.e., a function with only polynomially many nonzero Fourier coefficients). The authors demonstrate that any function f whose $L_1 $-norm (i.e., the sum of absolute value of the Fourier coefficients) is polynomial can be approximated by a polynomially sparse function, and prove that boolean Decision Trees with linear operations are a subset of this class of functions. Moreover, it is shown that the functions with polynomial $L_1 $-norm can be learned deterministically.The algorithm can also exactly identi...
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learning Decision Trees using the fourier spectrum
Symposium on the Theory of Computing, 1991Co-Authors: Eyal Kushilevitz, Yishay MansouAbstract:This work gives apolynomial time algorithm for learning Decision Trees with respect to the uniform distribution. (This algorithm uses membership queries.) The Decision Tree Model that is considered is an extension of the traditional boolean Decision Tree Model that allows linear operations in each node (i.e., summation of a subset of the input variables over GF(2)). This paper shows how to learn in polynomial time any function that can be approximated (in norm L2) by a polynomially sparse function (i.e., a function with only polynomially many nonzero Fourier coefficients). The authors demonstrate that any functionf whose L -norm (i.e., the sum of absolute value of the Fourier coefficients) is polynomial can be approximated by a polynomially sparse function, and prove that boolean Decision Trees with linear operations are a subset of this class of functions. Moreover, it is shown that the functions with polynomial L -norm can be learned deterministically. The algorithm can also exactly identify a Decision Tree of depth d in time polynomial in 2 a and n. This result implies that Trees of logarithmic depth can be identified in polynomial time.
John M Luk - One of the best experts on this subject based on the ideXlab platform.
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artificial neural networks and Decision Tree Model analysis of liver cancer proteomes
Biochemical and Biophysical Research Communications, 2007Co-Authors: John M Luk, Brian Y H Lam, Nikki P Lee, Pak C Sham, Lei Chen, Jirun Peng, Xisheng Leng, Philip J R Day, Sheung Tat FanAbstract:Hepatocellular carcinoma (HCC) is a heterogeneous cancer and usually diagnosed at late advanced tumor stages of high lethality. The present study attempted to obtain a proteome-wide analysis of HCC in comparison with adjacent non-tumor liver tissues, in order to facilitate biomarkers' discovery and to investigate the mechanisms of HCC development. A cohort of 66 Chinese patients with HCC was included for proteomic profiling study by two-dimensional gel electrophoresis (2-DE) analysis. Artificial neural network (ANN) and Decision Tree (CART) data-mining methods were employed to analyze the profiling data and to delineate significant patterns and trends for discriminating HCC from non-malignant liver tissues. Protein markers were identified by tandem MS/MS. A total of 132 proteome datasets were generated by 2-DE expression profiling analysis, and each with 230 consolidated protein expression intensities. Both the data-mining algorithms successfully distinguished the HCC phenotype from other non-malignant liver samples. The detection sensitivity and specificity of ANN were 96.97% and 87.88%, while those of CART were 81.82% and 78.79%, respectively. The three biological classifiers in the CART Model were identified as cytochrome b5, heat shock 70 kDa protein 8 isoform 2, and cathepsin B. The 2-DE-based proteomic profiling approach combined with the ANN or CART algorithm yielded satisfactory performance on identifying HCC and revealed potential candidate cancer biomarkers.
