Training Data for Diagnostic Models

A training dataset for diagnostic models, containing sample IDs, binary outcomes, and gene expression features.

Usage

train_dia

A data frame with rows for samples and 22 columns:

sample: character. Unique identifier for each sample.
outcome: integer. The binary outcome, where 1 typically represents a positive case and 0 a negative case.
AC004637.1: numeric. Gene expression level.
AC008459.1: numeric. Gene expression level.
AC009242.1: numeric. Gene expression level.
AC016735.1: numeric. Gene expression level.
AC090125.1: numeric. Gene expression level.
AC104237.3: numeric. Gene expression level.
AC112721.2: numeric. Gene expression level.
AC246817.1: numeric. Gene expression level.
AL135841.1: numeric. Gene expression level.
AL139241.1: numeric. Gene expression level.
HYMAI: numeric. Gene expression level.
KCNIP2.AS1: numeric. Gene expression level.
LINC00639: numeric. Gene expression level.
LINC00922: numeric. Gene expression level.
LINC00924: numeric. Gene expression level.
LINC00958: numeric. Gene expression level.
LINC01028: numeric. Gene expression level.
LINC01614: numeric. Gene expression level.
LINC01644: numeric. Gene expression level.
PRDM16.DT: numeric. Gene expression level.

Stored in data/train_dia.rda.

This dataset is used to train machine learning models for diagnosis. The column names starting with 'AC', 'AL', 'LINC', etc., are feature variables.