Link prediction aims to uncover missing links or predict the emergence of future relationships from the current network structure. Plenty of algorithms have been developed for link prediction in unweighted networks, but only a few have been extended to weighted networks. In this paper, we present what we call a “reliable-route method” to extend unweighted local similarity indices to weighted ones. Using these indices, we can predict both the existence of links and their weights. Experiments on various real-world networks suggest that our reliable-route weighted resource-allocation index performs noticeably better than others with respect to weight prediction. For existence prediction it is either the highest or very close to the highest. Further analysis shows a strong positive correlation between the clustering coefficient and prediction accuracy. Finally, we apply our method to the prediction of missing protein-protein interactions and their confidence scores from known PPI networks. Once again, our reliable-route method shows the highest accuracy.

This paper studies the online adaptive optimal controller design for a class of nonlinear systems through a novel policy iteration (PI) algorithm. By using the technique of neural network linear differential inclusion (LDI) to linearize the nonlinear terms in each iteration, the optimal law for controller design can be solved through the relevant algebraic Riccati equation (ARE) without using the system internal parameters. Based on PI approach, the adaptive optimal control algorithm is developed with the online linearization and the two-step iteration, i.e., policy evaluation and policy improvement. The convergence of the proposed PI algorithm is also proved. Finally, two numerical examples are given to illustrate the effectiveness and applicability of the proposed method.

Shexiang Baoxin Pill (SBP), a traditional Chinese medicine formula, is commonly used to treat cardiovascular disease (CVD) in China. However, the complexity of composition and targets has deterred our understanding of its mechanism of action. Using network pharmacology-based approaches, we established the mechanism of action for SBP to treat CVD by analyzing protein-protein interactions and pathways. The computational results were confirmed at the gene expression level in microarray-based studies. Two of the SBP’s targets were further confirmed at the protein level by Western blot. In addition, we validated the theory that SBP’s plasma absorbed compounds play major therapeutic role in treating CVD.

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providing relevant details, so we can investigate your claim. Download date:03. Nov. 2020 Abstract-In this paper, an online adaptive optimal control problem of a class of continuous-time Markov jump linear systems (MJLSs) is investigated by using a parallel reinforcement learning (RL) algorithm with completely unknown dynamics. Before collecting and learning the subsystems information of states and inputs, the exploration noise is firstly added to describe the actual control input. Then, a novel parallel RL algorithm is used to parallelly compute the corresponding N coupled algebraic Riccati equations (AREs) by online learning. By this algorithm, we will not need to know the dynamic information of the MJLSs. The convergence of the proposed algorithm is also proved. Finally, the effectiveness and applicability of this novel algorithm is illustrated by two simulation examples. Index Terms-Markov jump linear systems (MJLSs); adaptive optimal control; online; reinforcement learning (RL); coupled algebraic Riccati equations (AREs).

An online adaptive optimal control problem for a class of nonlinear Markov jump systems (MJSs) is studied. It is worth noting that the dynamic information of MJSs is partially unknown. Applying the neural network linear differential inclusion techniques, the nonlinear terms in MJSs are approximately converted to linear forms. By using subsystem transformation schemes, we can transfer the nonlinear MJSs to N new coupled linear subsystems. Then a new online policy iteration algorithm is put forward to obtain the adaptive optimal controller. Some theorems are given afterward to ensure the convergence of the new algorithm. At last, a simulation example is provided to verify the applicability of the algorithm.

Huang-Lian-Jie-Du-Tang (HLJDT) is a classic TCM formula to clear “heat” and “poison” that exhibits antirheumatic activity. Here we investigated the therapeutic mechanisms of HLJDT at protein network level using bioinformatics approach. It was found that HLJDT shares 5 target proteins with 3 types of anti-RA drugs, and several pathways in immune system and bone formation are significantly regulated by HLJDT's components, suggesting the therapeutic effect of HLJDT on RA. By defining an antirheumatic effect score to quantitatively measure the therapeutic effect, we found that the score of each HLJDT's component is very low, while the whole HLJDT achieves a much higher effect score, suggesting a synergistic effect of HLJDT achieved by its multiple components acting on multiple targets. At last, topological analysis on the RA-associated PPI network was conducted to illustrate key roles of HLJDT's target proteins on this network. Integrating our findings with TCM theory suggests that HLJDT targets on hub nodes and main pathway in the Hot ZENG network, and thus it could be applied as adjuvant treatment for Hot-ZENG-related RA. This study may facilitate our understanding of antirheumatic effect of HLJDT and it may suggest new approach for the study of TCM pharmacology.

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