17 research outputs found
Hyperuricemia and Cardiovascular Disease.
Uric acid (UA), the metabolic mediator of gout and urate renal stones, is associated with increased cardiovascular risk burden. Hyperuricemia is a common metabolic disorder, and interaction among UA and cardiovascular diseases has been clearly described. Several illnesses, including hypertension, myocardial infarction, metabolic syndrome, and heart failure, are related to increases in UA levels. In this article, we discuss the pathophysiology of hyperuricemia and describe the biologic plausibility of this metabolite's participation in the pathogenesis of cardiovascular illness. We conclude by discussing the implications of lowering plasma UA concentrations to reduce the risk of cardiovascular events, including myocardial infarction, stroke, heart failure, and cardiovascular death
Optimization of binding affinities in chemical space with generative pre-trained transformer and deep reinforcement learning [version 2; peer review: 2 approved, 3 approved with reservations]
Background The key challenge in drug discovery is to discover novel compounds with desirable properties. Among the properties, binding affinity to a target is one of the prerequisites and usually evaluated by molecular docking or quantitative structure activity relationship (QSAR) models. Methods In this study, we developed SGPT-RL, which uses a generative pre-trained transformer (GPT) as the policy network of the reinforcement learning (RL) agent to optimize the binding affinity to a target. SGPT-RL was evaluated on the Moses distribution learning benchmark and two goal-directed generation tasks, with Dopamine Receptor D2 (DRD2) and Angiotensin-Converting Enzyme 2 (ACE2) as the targets. Both QSAR model and molecular docking were implemented as the optimization goals in the tasks. The popular Reinvent method was used as the baseline for comparison. Results The results on the Moses benchmark showed that SGPT-RL learned good property distributions and generated molecules with high validity and novelty. On the two goal-directed generation tasks, both SGPT-RL and Reinvent were able to generate valid molecules with improved target scores. The SGPT-RL method achieved better results than Reinvent on the ACE2 task, where molecular docking was used as the optimization goal. Further analysis shows that SGPT-RL learned conserved scaffold patterns during exploration. Conclusions The superior performance of SGPT-RL in the ACE2 task indicates that it can be applied to the virtual screening process where molecular docking is widely used as the criteria. Besides, the scaffold patterns learned by SGPT-RL during the exploration process can assist chemists to better design and discover novel lead candidates
Interpreting Deep Learning-Based Networking Systems
While many deep learning (DL)-based networking systems have demonstrated
superior performance, the underlying Deep Neural Networks (DNNs) remain
blackboxes and stay uninterpretable for network operators. The lack of
interpretability makes DL-based networking systems prohibitive to deploy in
practice. In this paper, we propose Metis, a framework that provides
interpretability for two general categories of networking problems spanning
local and global control. Accordingly, Metis introduces two different
interpretation methods based on decision tree and hypergraph, where it converts
DNN policies to interpretable rule-based controllers and highlight critical
components based on analysis over hypergraph. We evaluate Metis over several
state-of-the-art DL-based networking systems and show that Metis provides
human-readable interpretations while preserving nearly no degradation in
performance. We further present four concrete use cases of Metis, showcasing
how Metis helps network operators to design, debug, deploy, and ad-hoc adjust
DL-based networking systems.Comment: To appear at ACM SIGCOMM 202
Information Security in Online Stochastic Systems
The presented research studies information security under two broad contexts --- guarding against information injection in stochastic control systems and guarding against information extraction from inference. We pick specific classes of applications wit
Stealthy Control Signal Attacks in Linear Quadratic Gaussian Control Systems: Detectability Reward Tradeoff
MUSTv2: An Improved De Novo Detection Program for Recently Active Miniature Inverted Repeat Transposable Elements (MITEs)
Miniature inverted repeat transposable element (MITE) is a short transposable element, carrying no protein-coding regions. However, its high proliferation rate and sequence-specific insertion preference renders it as a good genetic tool for both natural evolution and experimental insertion mutagenesis. Recently active MITE copies are those with clear signals of Terminal Inverted Repeats (TIRs) and Direct Repeats (DRs), and are recently translocated into their current sites. Their proliferation ability renders them good candidates for the investigation of genomic evolution