ProDGe: investigating protein-protein interactions at the domain level

An important goal of systems biology is the identification and investigation of known and predicted protein-protein interactions to obtain more information about new cellular pathways and processes. Proteins interact via domains, thus it is important to know which domains a protein contains and which domains interact with each other. Here we present the Java^TM^ program ProDGe (Protein Domain Gene), which visualizes existing and suggests novel domain-domain interactions and protein-protein interactions at the domain level. The comprehensive dataset behind ProDGe consists of protein, domain and interaction information for both layers, collected and combined appropriately from UniProt, Pfam, DOMINE and IntAct. Based on known domain interactions, ProDGe suggests novel protein interactions and assigns them to four confidence classes, depending on the reliability of the underlying domain interaction. Furthermore, ProDGe is able to identify potential homologous interaction partners in other species, which is particularly helpful when investigating poorly annotated species. We further evaluated and compared experimentally identified protein interactions from IntAct with domain interactions from DOMINE for six species and noticed that 31.13% of all IntAct protein interactions in all six species can be mapped to the actual interacting domains. ProDGe and a comprehensive documentation are freely available at http://www.cogsys.cs.uni-tuebingen.de/software/ProDGe

Hybrid Representations for Composition Optimization and Parallelizing MOEAs

We present a hybrid EA representation suitable to optimize composition optimization problems ranging from optimizing recipes for catalytic materials to cardinality constrained portfolio selection. On several problem instances we can show that this new representation performs better than standard repair mechanisms with Lamarckism. Additionally, we investigate the a clustering based parallelization scheme for MOEAs. We prove that typical "divide and conquer\u27\u27 approaches are not suitable for the standard test functions like ZDT 1-6. Therefore, we suggest a new test function based on the portfolio selection problem and prove the feasibility of "divide and conquer\u27\u27 approaches on this test function

From Prediction to Planning With Goal Conditioned Lane Graph Traversals

The field of motion prediction for automated driving has seen tremendous progress recently, bearing ever-more mighty neural network architectures. Leveraging these powerful models bears great potential for the closely related planning task. In this letter we propose a novel goal-conditioning method and show its potential to transform a state-of-the-art prediction model into a goal-directed planner. Our key insight is that conditioning prediction on a navigation goal at the behaviour level outperforms other widely adopted methods, with the additional benefit of increased model interpretability. We train our model on a large open-source dataset and show promising performance in a comprehensive benchmark

Optimal Stroke Learning with Policy Gradient Approach for Robotic Table Tennis

Learning to play table tennis is a challenging task for robots, as a wide variety of strokes required. Recent advances have shown that deep Reinforcement Learning (RL) is able to successfully learn the optimal actions in a simulated environment. However, the applicability of RL in real scenarios remains limited due to the high exploration effort. In this work, we propose a realistic simulation environment in which multiple models are built for the dynamics of the ball and the kinematics of the robot. Instead of training an end-to-end RL model, a novel policy gradient approach with TD3 backbone is proposed to learn the racket strokes based on the predicted state of the ball at the hitting time. In the experiments, we show that the proposed approach significantly outperforms the existing RL methods in simulation. Furthermore, to cross the domain from simulation to reality, we adopt an efficient retraining method and test it in three real scenarios. The resulting success rate is 98% and the distance error is around 24.9 cm. The total training time is about 1.5 hours