Simple decision rules for classifying human cancers from gene expression profiles

By Aik Choon Tan, Daniel Q. Naiman, Lei Xu, Raimond L. Winslow, Donald Geman. Bioinform., 2005

433 citations recorded by the source corpus.

Subfields: Information Systems, Molecular Biology

View Paper: https://www.semanticscholar.org/paper/2a0b9cadef82314d4b75c9710b18c55e58f8ee09

How researchers see this paper

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

In Information Systems

1. Oliver Stegle
2. Hemant Ishwaran

In Molecular Biology

1. John C. Marioni
2. Sarah A. Teichmann
3. Oliver Stegle
4. Fabian J Theis
5. Antonio Scialdone
6. Andy J. Minn
7. Feng Chu
8. Wei Xie

Citations in the corpus

• Computational assignment of cell-cycle stage from single-cell transcriptome data. (2015, positive): “The pairs method is less affected by this issue, possibly due to the higher robustness of the signal captured by the relative rankings of pairs of genes [23,24].”
• An interferon-related gene signature for DNA damage resistance is a predictive marker for chemotherapy and radiation for breast cancer (2008, background): “ISG15 is a ubiquitin-like protein involved in posttranslational modification (10), and IFIT1 has been shown to regulate translation initiation (11).”
• Accurate Cancer Classification Using Expressions of Very Few Genes (2007, methodology): “In [26], Tan et al. honestly tested the GCM data and obtained 67.39 percent testing accuracy with 134 genes.”