Economics at your fingertips  

Finding causative genes from high-dimensional data: an appraisal of statistical and machine learning approaches

Wang Chamont () and Gevertz Jana L.
Additional contact information
Gevertz Jana L.: Department of Mathematics and Statistics, The College of New Jersey, Ewing, NJ 08628, USA

Statistical Applications in Genetics and Molecular Biology, 2016, vol. 15, issue 4, 321-347

Abstract: Modern biological experiments often involve high-dimensional data with thousands or more variables. A challenging problem is to identify the key variables that are related to a specific disease. Confounding this task is the vast number of statistical methods available for variable selection. For this reason, we set out to develop a framework to investigate the variable selection capability of statistical methods that are commonly applied to analyze high-dimensional biological datasets. Specifically, we designed six simulated cancers (based on benchmark colon and prostate cancer data) where we know precisely which genes cause a dataset to be classified as cancerous or normal – we call these causative genes. We found that not one statistical method tested could identify all the causative genes for all of the simulated cancers, even though increasing the sample size does improve the variable selection capabilities in most cases. Furthermore, certain statistical tools can classify our simulated data with a low error rate, yet the variables being used for classification are not necessarily the causative genes.

Keywords: classification; false discovery rate; gene identification; shrinkage and regularization techniques; variable selection (search for similar items in EconPapers)
Date: 2016
References: View references in EconPapers View complete reference list from CitEc
Citations: Track citations by RSS feed

Downloads: (external link) (text/html)
For access to full text, subscription to the journal or payment for the individual article is required.

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link:

Ordering information: This journal article can be ordered from

DOI: 10.1515/sagmb-2015-0072

Access Statistics for this article

Statistical Applications in Genetics and Molecular Biology is currently edited by Michael P. H. Stumpf

More articles in Statistical Applications in Genetics and Molecular Biology from De Gruyter
Bibliographic data for series maintained by Peter Golla ().

Page updated 2021-05-07
Handle: RePEc:bpj:sagmbi:v:15:y:2016:i:4:p:321-347:n:4