Machine learning is bringing a paradigm shift to healthcare by changing the process of disease diagnosis and prognosis in clinics and hospitals. We provide a clinical Meta-Dataset derived from the publicly available Cancer Genome Atlas Program (TCGA) that contains 174 tasks.
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.