Serum protein fingerprinting coupled with a pattern-matching algorithm distinguishes prostate cancer from benign prostate hyperplasia and healthy men.

By Bao-Ling Adam, Yinsheng Qu, John W. Davis, M. Ward, M. Clements, L. Cazares, O. Semmes, Paul Schellhammer, Y. Yasui, Ziding Feng and 1 more. Cancer Research, 2002

948 citations recorded by the source corpus.

View Paper: https://www.semanticscholar.org/paper/31e62ea1c38b216f73ea89850128fef5107d5bf0

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Citations in the corpus

Proteomics: the first decade and beyond (2003, background): “We expect that precise clinical diagnosis based on highly discriminating patterns of proteins in easily accessible samples, particularly body fluids, may be the area in which proteomics will make its first significant contributio”
Class prediction and discovery using gene microarray and proteomics mass spectroscopy data: curses, caveats, cautions (2003, methodology): “…with the development of microarray technology in genomics (Eisen et al., 1998; Alon et al., 1999; Brown et al., 2000) and the application of MALDI and SELDI TOF mass spectroscopy in proteomics (Wright et al., 1999; Vlahou et al., 2001; Adam et al., 2002; Li et al., 2002; Petricoin et al., 2002).”
Mass Spectrometry as a Diagnostic and a Cancer Biomarker Discovery Tool (2004, background): “The only agreements were two peaks for distinguishing non-cancer versus cancer from the same group of investigators and the same datasets (40, 41).”
Test for Bandedness of High Dimensional Covariance Matrices with Bandwidth Estimation (2012, methodology): “In this section, we report an empirical study on a prostate cancer data set [Adam et al. (2003)] from protein mass spectroscopy, which was aimed to distinguish the healthy people from the ones with the cancer by analyzing the constituents of the proteins in the blood. Adam et al. (2003) recorded for”
Testing mutual independence in high dimension via distance covariance (2016, background): “The original prostate cancer data were analysed by Adam et al. (2002) to study the protein profiling technologies that can simultaneously resolve and analyse multiple proteins in early detection of prostate cancer. Surface-enhanced laser desorption–ionization mass spectrometry protein profiles of pa”
A data review and re-assessment of ovarian cancer serum proteomic profiling (2003, result): “The presence of statistically significant signals at M/Z values less than 500 was unexpected as some investigators, in their systems, conservatively disregard data beneath M/Z values of 2000 as possible noise [12].”
HITON: A Novel Markov Blanket Algorithm for Optimal Variable Selection (2003, background): “Finally, the fifth task is diagnosis of prostate cancer from analysis of massspectrometry signal peaks obtained from human sera [18].”
Sparsity and smoothness via the fused lasso (2005, methodology): “E-mail: tibs@stat.stanford.edu There has been much interest in this problem in the past few years; see for example Petricoin et al. (2002) and Adam et al. (2003).”
Reproducibility of SELDI-TOF protein patterns in serum: comparing datasets from different experiments (2004, background): “One or two other groups have been willing to make their data available (Adam et al., 2002), but this laudable practice is not yet universal.”
Preinvasive and invasive ductal pancreatic cancer and its early detection in the mouse. (2003, background): “Second, and as a direct consequence, such animals are much less likely to have developedtients with ovarian (Petricoin et al., 2002b), breast (Li et al., 2002), and prostate cancer (Adam et al., 2002; Petricoin et al., invasive ductal cancer.”
Partially Supervised Learning Using an EM‐Boosting Algorithm (2004, methodology): “…artiﬁcially created data sets ﬁrst using the true labels of the disease statuses, and then assessed in a test data set of 45 cancer/BPH samples and 15 normal samples that were saved for an independent assessment of classiﬁers’ performances in the prostate cancer biomarker study (Adam et al., 2002).”
Multitask Quantile Regression under the Transnormal Model (2016, methodology): “This subsection illustrates the power of the proposed rank-based method in an application to study the prostate cancer using the protein mass spectroscopy data (Adam et al. 2002), which was previously analyzed by Levina et al.”
Feature extraction and quantification for mass spectrometry in biomedical applications using the mean spectrum (2005, background): “…proteomic patterns in complex mixtures of proteins derived from tissue samples, or from more easily obtained biological fluids such as serum, urine or nipple aspirate fluid (Paweletz et al., 2000, 2001; Wellmann et al., 2002; Adam et al., 2002, 2003; Zhukov et al., 2003; Schaub et al., 2004).”