The TCGA Meta-Dataset Clinical Benchmark

TL;DR

This paper introduces a comprehensive TCGA Meta-Dataset with 174 tasks for multi-outcome clinical prediction using gene expression data, aiming to standardize benchmarks and facilitate development of methods for small sample sizes.

Contribution

It provides a large, unified benchmark dataset from TCGA for multi-task clinical prediction, addressing inconsistencies and enabling research on few-sample gene expression analysis.

Findings

Neural networks outperform regression on multiple tasks.

150 samples suffice for baseline model training.

Diverse clinical variables can be predicted from gene expression.

Abstract

Machine learning is bringing a paradigm shift to healthcare by changing the process of disease diagnosis and prognosis in clinics and hospitals. This development equips doctors and medical staff with tools to evaluate their hypotheses and hence make more precise decisions. Although most current research in the literature seeks to develop techniques and methods for predicting one particular clinical outcome, this approach is far from the reality of clinical decision making in which you have to consider several factors simultaneously. In addition, it is difficult to follow the recent progress concretely as there is a lack of consistency in benchmark datasets and task definitions in the field of Genomics. To address the aforementioned issues, we provide a clinical Meta-Dataset derived from the publicly available data hub called The Cancer Genome Atlas Program (TCGA) that contains 174 tasks. We believe those tasks could be good proxy tasks to develop methods which can work on a few samples of gene expression data. Also, learning to predict multiple clinical variables using gene-expression data is an important task due to the variety of phenotypes in clinical problems and lack of samples for some of the rare variables. The defined tasks cover a wide range of clinical problems including predicting tumor tissue site, white cell count, histological type, family history of cancer, gender, and many others which we explain later in the paper. Each task represents an independent dataset. We use regression and neural network baselines for all the tasks using only 150 samples and compare their performance.