We
describe a fully data driven model that learns to perform a
retrosynthetic reaction prediction task, which is treated as a sequence-to-sequence
mapping problem. The end-to-end trained model has an encoder–decoder
architecture that consists of two recurrent neural networks, which
has previously shown great success in solving other sequence-to-sequence
prediction tasks such as machine translation. The model is trained
on 50,000 experimental reaction examples from the United States patent
literature, which span 10 broad reaction types that are commonly used
by medicinal chemists. We find that our model performs comparably
with a rule-based expert system baseline model, and also overcomes
certain limitations associated with rule-based expert systems and
with any machine learning approach that contains a rule-based expert
system component. Our model provides an important first step toward
solving the challenging problem of computational retrosynthetic analysis.
Modern software systems are built to be used in dynamic environments using configuration capabilities to adapt to changes and external uncertainties. In a self-adaptation context, we are often interested in reasoning about the performance of the systems under different configurations. Usually, we learn a black-box model based on real measurements to predict the performance of the system given a specific configuration. However, as modern systems become more complex, there are many configuration parameters that may interact and we end up learning an exponentially large configuration space. Naturally, this does not scale when relying on real measurements in the actual changing environment. We propose a different solution: Instead of taking the measurements from the real system, we learn the model using samples from other sources, such as simulators that approximate performance of the real system at low cost. We define a cost model that transform the traditional view of model learning into a multi-objective problem that not only takes into account model accuracy but also measurements effort as well. We evaluate our cost-aware transfer learning solution using real-world configurable software including (i) a robotic system, (ii) 3 different stream processing applications, and (iii) a NoSQL database system. The experimental results demonstrate that our approach can achieve (a) a high prediction accuracy, as well as (b) a high model reliability.
Massively parallel reporter assays (MPRAs) are a method to probe the effects of short sequences on transcriptional regulation activity. In a MPRA, short sequences are extracted from suspected regulatory regions, inserted into reporter plasmids, transfected into cell-types of interest, and the transcriptional activity of each reporter is assayed. Recently, Ernst et al. presented MPRA data covering 15750 putative regulatory regions. We trained a multitask convolutional neural network architecture using these sequence expression readouts which predicts as output the expression level outputs across four combinations of cell types and promoters. The model allows for the assigning of importance scores to each base through in silico mutagenesis, and the resulting importance scores correlated well with regions enriched for conservation and transcription factor binding.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.