Chemical toxicity prediction using machine learning is important in drug development to reduce repeated animal and human testing, thus saving cost and time.
no code implementations • • Vijil Chenthamarakshan, Payel Das, Samuel C. Hoffman, Hendrik Strobelt, Inkit Padhi, Kar Wai Lim, Benjamin Hoover, Matteo Manica, Jannis Born, Teodoro Laino, Aleksandra Mojsilovic
CogMol also includes insilico screening for assessing toxicity of parent molecules and their metabolites with a multi-task toxicity classifier, synthetic feasibility with a chemical retrosynthesis predictor, and target structure binding with docking simulations.
This paper looks into the problem of detecting network anomalies by analyzing NetFlow records.
We propose a simulation method for multidimensional Hawkes processes based on superposition theory of point processes.
Exploiting this additional information, we propose the Twitter-Network (TN) topic model to jointly model the text and the social network in a full Bayesian nonparametric way.
In this article, we present efficient methods for the use of these processes in this hierarchical context, and apply them to latent variable models for text analytics.
Although social media data like tweets are laden with opinions, their "dirty" nature (as natural language) has discouraged researchers from applying LDA-based opinion model for product review mining.
In this paper, we combine these three in a topic model that produces a bibliographic model of authors, topics and documents, using a nonparametric extension of a combination of the Poisson mixed-topic link model and the author-topic model.