Assessing Genome-Wide Statistical Significance for Large p Small n Problems

By G. Diao, A. Vidyashankar. 2013

38 citations recorded by the source corpus.

Subfields: Genetics

How researchers see this paper

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

1. Ryuji Hamamoto
2. K. Suvarna
3. Ken Asada
4. Satoshi Takahashi
5. Masayoshi Yamada
6. Ken Takasawa
7. S. Jinnai
8. Kazuma Kobayashi

Citations in the corpus

• Application of Artificial Intelligence Technology in Oncology: Towards the Establishment of Precision Medicine (2020, background): “First, the dimensions of the input are in orders of magnitude larger than the number of samples (Large p, Small n Problems) [107], and the model tends to overfit the training dataset.”
• Predicting Deep Learning Based Multi-Omics Parallel Integration Survival Subtypes in Lung Cancer Using Reverse Phase Protein Array Data (2020, background): “One such issue is generally called as the “Large p Small n Problems”; there are far more parameters or features in input dimension compared to the number of sample [46].”