Avoiding common pitfalls in machine learning omic data science

This Comment describes some of the common pitfalls encountered in deriving and validating predictive statistical models from high-dimensional data. It offers a fresh perspective on some key statistical issues, providing some guidelines to avoid pitfalls, and to help unfamiliar readers better assess...

Full description

Saved in:
Bibliographic Details
Published in:Nature materials 2019-05, Vol.18 (5), p.422-427
Main Author: Teschendorff, Andrew E.
Format: Article
Language:English
Subjects:
631/114/1305
631/1647/48
639/705/531
Big Data
Biology
Biomaterials
Chemistry and Materials Science
Comment
Condensed Matter Physics
Data analysis
Data science
Gene expression
Genotype & phenotype
Machine learning
Materials Science
Nanotechnology
Optical and Electronic Materials
Reliability analysis
Statistical models
Citations: Items that this one cites
Items that cite this one
Online Access:Get full text
Tags: Add Tag
No Tags, Be the first to tag this record!
Description
Summary:This Comment describes some of the common pitfalls encountered in deriving and validating predictive statistical models from high-dimensional data. It offers a fresh perspective on some key statistical issues, providing some guidelines to avoid pitfalls, and to help unfamiliar readers better assess the reliability and significance of their results.
ISSN:1476-1122
1476-4660
DOI:10.1038/s41563-018-0241-z