Addressing the safety aspects of new chemicals has historically been undertaken through animal testing studies, which are expensive and time-consuming. Computational toxicology is a promising alternative approach that utilizes machine learning (ML) and deep learning (DL) techniques to predict toxicity potentials of chemicals. Although the applications of ML and DL based computational models in chemicals toxicity predictions are attractive, many toxicity models are “black box” in nature and difficult to interpret by toxicologists, which hampers the chemical risk assessments using these models. The recent progress of interpretable ML (IML) in the computer science field meets this urgent need to unveil the underlying toxicity mechanisms and elucidate domain knowledge of toxicity models. In this new modeling framework, the toxicity feature data, model interpretation methods, and the use of toxicity knowledgebase in IML development advance the applications of computational models in chemical risk assessments. The challenges and future directions of IML modeling in toxicology are strongly driven by heterogenous big data and newly revealed toxicity mechanisms. The big data mining, analysis, and mechanistic modeling using IML methods will advance artificial intelligence in the big data era to pave the road to future computational chemical toxicology and will have a significant impact on the risk assessment procedure and drug safety.

 Gibson Hall 414

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Proximal Markov Chain Monte Carlo (MCMC) is a flexible and general Bayesian inference framework for constrained or regularized parametric estimation. The basic idea of proximal MCMC is to approximate non-smooth regularization terms via the Moreau-Yosida envelope. Initial proximal MCMC strategies, however, fixed nuisance and regularization parameters as constants and relied on the Langevin algorithm for the posterior sampling. Proximal MCMC is extended to a fully Bayesian framework with modeling and data-adaptive estimation of all parameters, including regularization parameters. More efficient sampling algorithms, such as the Hamiltonian Monte Carlo, are employed to scale proximal MCMC to high-dimensional problems. The proposed proximal MCMC offers a versatile and modularized procedure for the inference of constrained and non-smooth problems that are mostly tuning parameter-free. Its utility is illustrated in various statistical estimation and machine-learning tasks.

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In many phylogenetic models, the number of parameters to estimate grows with the number of taxa under study. However, parsimonious models of evolution demand local similarity in parameters on subtrees. To achieve scalable inference in such a setting, we employ auto-correlated, shrinkage-based models. We compare inference under these models to previous state-of-the art in a variety of applied settings. In one example, we investigate the heritable clock structure of various surface glycoproteins of influenza A virus in the absence of prior knowledge about molecular clock placement. In another example, we estimate the phylogenetic location of environmental shifts in the ancestry of Anolis lizards.