ResearchTrend.AI
  • Papers
  • Communities
  • Events
  • Blog
  • Pricing
Papers
Communities
Social Events
Terms and Conditions
Pricing
Parameter LabParameter LabTwitterGitHubLinkedInBlueskyYoutube

© 2025 ResearchTrend.AI, All rights reserved.

  1. Home
  2. Papers
  3. 0903.2003
  4. Cited By
Feature selection in omics prediction problems using cat scores and
  false nondiscovery rate control
v1v2v3v4 (latest)

Feature selection in omics prediction problems using cat scores and false nondiscovery rate control

11 March 2009
M. Ahdesmaki
K. Strimmer
ArXiv (abs)PDFHTML

Papers citing "Feature selection in omics prediction problems using cat scores and false nondiscovery rate control"

4 / 4 papers shown
Title
Nested cross-validation when selecting classifiers is overzealous for
  most practical applications
Nested cross-validation when selecting classifiers is overzealous for most practical applications
Jacques Wainer
G. Cawley
55
216
0
25 Sep 2018
Optimal whitening and decorrelation
Optimal whitening and decorrelation
A. Kessy
A. Lewin
K. Strimmer
97
405
0
02 Dec 2015
Sparse Proteomics Analysis - A compressed sensing-based approach for
  feature selection and classification of high-dimensional proteomics mass
  spectrometry data
Sparse Proteomics Analysis - A compressed sensing-based approach for feature selection and classification of high-dimensional proteomics mass spectrometry data
Tim Conrad
Martin Genzel
Nada Cvetkovic
Niklas Wulkow
A. Leichtle
J. Vybíral
Gitta Kutyniok
Christof Schütte
13
34
0
11 Jun 2015
Higher Criticism for Large-Scale Inference, Especially for Rare and Weak
  Effects
Higher Criticism for Large-Scale Inference, Especially for Rare and Weak Effects
D. Donoho
Jiashun Jin
122
131
0
17 Oct 2014
1