16 research outputs found
LogGENE: A smooth alternative to check loss for Deep Healthcare Inference Tasks
Mining large datasets and obtaining calibrated predictions from tem is of
immediate relevance and utility in reliable deep learning. In our work, we
develop methods for Deep neural networks based inferences in such datasets like
the Gene Expression. However, unlike typical Deep learning methods, our
inferential technique, while achieving state-of-the-art performance in terms of
accuracy, can also provide explanations, and report uncertainty estimates. We
adopt the Quantile Regression framework to predict full conditional quantiles
for a given set of housekeeping gene expressions. Conditional quantiles, in
addition to being useful in providing rich interpretations of the predictions,
are also robust to measurement noise. Our technique is particularly
consequential in High-throughput Genomics, an area which is ushering a new era
in personalized health care, and targeted drug design and delivery. However,
check loss, used in quantile regression to drive the estimation process is not
differentiable. We propose log-cosh as a smooth-alternative to the check loss.
We apply our methods on GEO microarray dataset. We also extend the method to
binary classification setting. Furthermore, we investigate other consequences
of the smoothness of the loss in faster convergence. We further apply the
classification framework to other healthcare inference tasks such as heart
disease, breast cancer, diabetes etc. As a test of generalization ability of
our framework, other non-healthcare related data sets for regression and
classification tasks are also evaluated