Nonparametric methods for identifying differentially expressed genes in microarray data

By Olga G. Troyanskaya, Mitchell E. Garber, Patrick O. Brown, David Botstein, Russ B. Altman. Bioinform., 2002

350 citations recorded by the source corpus.

Subfields: Molecular Biology

View Paper: https://www.semanticscholar.org/paper/bdb94a04d1deb3aaf012dc85dfd13ec5ebd52516

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. Mihai Pop
2. Niranjan Nagarajan
3. James Robert White
4. Steven L. Salzberg
5. Susan P. Holmes
6. Paul J. McMurdie
7. Sergey Koren
8. Rob Knight

Citations in the corpus

• Statistical Methods for Detecting Differentially Abundant Features in Clinical Metagenomic Samples (2009, methodology): “This procedure, also known as the nonparametric t-test has been shown to provide accurate estimates of significance when the underlying distributions are non-normal [22,23].”
• Applications of Support Vector Machine (SVM) Learning in Cancer Genomics. (2018, background): “For example, genes can be ranked by correlation coefficients (9, 12) and hypothesis testing statistics (30-33).”