Serum proteomic patterns for detection of prostate cancer.

By E. Petricoin, D. Ornstein, C. Paweletz, A. Ardekani, Paul S Hackett, B. Hitt, A. Velassco, Christian Trucco, Laura Wiegand, Kamillah Wood and 7 more. Journal of the National Cancer Institute, 2002

759 citations recorded by the source corpus.

Subfields: Molecular Biology, Pulmonary and Respiratory Medicine, Spectroscopy

View Paper: https://www.semanticscholar.org/paper/1f5f8bbf04f465602d8d52ec6c84b4b7bb4970d9

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Molecular Biology

1. Jieping Ye
2. Arul M. Chinnaiyan
3. Feiping Nie
4. Heng Huang
5. D. Tuveson
6. Debashis Ghosh
7. Jun Liu
8. Xiao Cai

In Pulmonary and Respiratory Medicine

1. Arul M. Chinnaiyan
2. Mark A. Rubin

In Spectroscopy

1. Timothy D. Veenstra
2. Thomas P. Conrads

Citations in the corpus

• Stability of feature selection algorithms: a study on high-dimensional spaces (2005, neutral): “We worked with three different mass spectrometry datasets: one for ovarian cancer [7], (version 8-07-02), another for prostate cancer [8] and an extended version of the early stroke diagnosis dataset used in [9].”
• Integrative genomic and proteomic analysis of prostate cancer reveals signatures of metastatic progression. (2005, background): “To begin to understand prostate cancer progression with a systems perspective, we need to characterize and integrate the molecular components involved (Grubb et al., 2003; Hood et al., 2004; Paweletz et al., 2001; Petricoin et al., 2002).”
• Preinvasive and invasive ductal pancreatic cancer and its early detection in the mouse. (2003, background): “Second, in constructs driven by heterologous promoters,et al., 1992; Jacks et al., 1994), p16INK4a (Serrano et al., 1996), the normal physiological regulation of gene expression gov-DPC4 (Sirard et al., 1998), and BRCA2 (Suzuki et al., 1997), has erned by the endogenous promoter is lost.”
• Regression on Fixed-Rank Positive Semidefinite Matrices: A Riemannian Approach (2010, methodology): “ (2007) Iris 150 4 3 Asuncion and Newman (2007) Soybean 532 35 17 Asuncion and Newman (2007) USPS 2,007 256 10 LeCun et al. (1989) Isolet 7,797 617 26 Asuncion and Newman (2007) Prostate 322 15,154 2 Petricoin et al. (2002) Table 2: Considered datasets The implementation of the proposed algorithms2 ”
• An efficient algorithm for a class of fused lasso problems (2010, methodology): “The Prostate Cancer [24] data set used in our experiments is based on the protein mass spectrometry, where the features are indexed by many time-of-flight values.”
• Robust Feature Selection Using Ensemble Feature Selection Techniques (2008, neutral): “1 [19] Pancreatic 80 101 6771 2.”
• Mass Spectrometry as a Diagnostic and a Cancer Biomarker Discovery Tool (2004, neutral): “(39) Hydrophobic C16 2,092, 2,367, 2,582, 3,080, 4,819, 5,439, 18,220 95%; 78–83% Adam et al.”
• Efficient and Robust Feature Selection via Joint ℓ2, 1-Norms Minimization (2010, methodology): “In our experiments, we used five publicly available microarray data sets and one Mass Spectrometry (MS) data sets: ALLAML data set [6], the malignant glioma (GLIOMA) data set [17], the human lung carcinomas (LUNG) data set [2], Human Carcinomas (Carcinomas) data set [24, 27], Prostate Cancer gene ex”
• Application of the GA/KNN method to SELDI proteomics data (2004, methodology): “We applied the range transformation described by Petricoin et al. (2002a): for eachm/z ratio, the transformed intensity was formed by taking the difference between the observed intensity and the minimum intensity and dividing that difference by the difference between the maximum and minimum…”
• The Role of MicroRNAs in Human Diseases (2010, background): “In early 90's, it was discovered that two small RNAs (instead of a protein) were the product of lin-4, a gene controlling the timing Avicenna J Med Biotech 2010; 2(4): 161-179 * Corresponding author:”