Deep neural networks are a promising technology achieving state-of-the-art results in biological and healthcare domains. Unfortunately, DNNs are notorious for their non-interpretability. Clinicians are averse to black boxes and thus interpretability is paramount to broadly adopting this technology. We aim to close this gap by proposing a new general feature ranking method for deep learning. We show that our method outperforms LASSO, Elastic Net, Deep Feature Selection and various heuristics on a simulated dataset. We also compare our method in a multivariate clinical time-series dataset and demonstrate our ranking rivals or outperforms other methods in Recurrent Neural Network setting. Finally, we apply our feature ranking to the Variational Autoencoder recently proposed to predict drug response in cell lines and show that it identifies meaningful genes corresponding to the drug response.
Several works have mentioned using variational dropout to achieve better performance @cite_0 , have a Bayes interpretation of dropout , or compress the model architecture . These works focus on tuning the dropout rate to automatically get the best performance, but do not consider applying it to the feature ranking problems.
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The Hedgehog (Hh) signaling pathway is aberrantly activated in a wide variety of human cancers, and recent clinical studies have demonstrated that pathway inhibitors are effective in advanced basal cell carcinoma (BCC). The majority of these agents have been designed to target SMOOTHENED (SMO), a transmembrane regulator of Hh signaling, but subsequent mutations in SMO have been found to generate drug resistance. In other cancers, oncogenic events that bypass SMO may activate canonical Hh signaling, and SMO antagonists have not demonstrated significant activity in several diseases. Therefore, alternative strategies targeting the Hh pathway downstream of SMO may have clinical utility. Liver X Receptors (LXRs) regulate cholesterol and fatty acid homeostasis, and LXR activation can inhibit the Hh pathway in normal mouse embryonic fibroblasts. We examined the effects of LXR activation on Hh signaling in human multiple myeloma (MM) cells and found that LXR agonists inhibited Hh pathway activity and clonogenic tumor growth in vitro. LXR activation also inhibited putative MM cancer stem cells in vivo leading to the loss of tumor initiating and self-renewal potential. Finally, Hh signaling was inhibited downstream of SMO, suggesting that LXR agonists may represent a novel strategy to target pathogenic Hh signaling as well as treat MM.