Combining Context and Knowledge Representations for Chemical-Disease Relation Extraction

Automatically extracting the relationships between chemicals and diseases is significantly important to various areas of biomedical research and health care. Biomedical experts have built many large-scale knowledge bases (KBs) to advance the development of biomedical research. KBs contain huge amounts of structured information about entities and relationships, therefore plays a pivotal role in chemical-disease relation (CDR) extraction. However, previous researches pay less attention to the prior knowledge existing in KBs. This paper proposes a neural network-based attention model (NAM) for CDR extraction, which makes full use of context information in documents and prior knowledge in KBs. For a pair of entities in a document, an attention mechanism is employed to select important context words with respect to the relation representations learned from KBs. Experiments on the BioCreative V CDR dataset show that combining context and knowledge representations through the attention mechanism, could significantly improve the CDR extraction performance while achieve comparable results with state-of-the-art systems.Comment: Published on IEEE/ACM Transactions on Computational Biology and Bioinformatics, 11 pages, 5 figure

Opposing effects of estradiol and progesterone on the oxidative stress-induced production of chemokine and proinflammatory cytokines in murine peritoneal macrophages

In inflammatory and oxidative liver injury, virus proteins and reactive oxygen species are involved in the regulation of proinflammatory cytokine production by macrophages. This study investigated the effects of estradiol (E2) and progesterone on the unstimulated and oxidative stress-stimulated production of tumor necrosis factor (TNF)-α, interleukin (IL)-1β, macrophage inflammatory protein (MIP)-2, and macrophage chemotactic protein (MCP)-1 by peritoneal macrophages isolated from male and female mice. E2 inhibited the cytokine production of TNF-α, IL-1β, MIP-2, and MCP-1 by the unstimulated macrophages from males and females, which was then further stimulated by progesterone. The exposure to hydrogen peroxide in the macrophages from both sexes induced the production of cytokine. The hydrogen peroxide-stimulated cytokine production was suppressed by E2 and enhanced by progesterone. The sex hormone effects on the unstimulated and stimulated macrophages were blocked by their receptor antagonists and showed no significant difference between male and female subjects. These findings suggest that E2 may play a favorable role in the course of persistent liver injury, by inhibiting proinflammatory cytokine production, which, in addition, progesterone may counteract the favorable E2 effects through their receptors

Hedge Scope Detection in Biomedical Texts: An Effective Dependency-Based Method.

Hedge detection is used to distinguish uncertain information from facts, which is of essential importance in biomedical information extraction. The task of hedge detection is often divided into two subtasks: detecting uncertain cues and their linguistic scope. Hedge scope is a sequence of tokens including the hedge cue in a sentence. Previous hedge scope detection methods usually take all tokens in a sentence as candidate boundaries, which inevitably generate a large number of negatives for classifiers. The imbalanced instances seriously mislead classifiers and result in lower performance. This paper proposes a dependency-based candidate boundary selection method (DCBS), which selects the most likely tokens as candidate boundaries and removes the exceptional tokens which have less potential to improve the performance based on dependency tree. In addition, we employ the composite kernel to integrate lexical and syntactic information and demonstrate the effectiveness of structured syntactic features for hedge scope detection. Experiments on the CoNLL-2010 Shared Task corpus show that our method achieves 71.92% F1-score on the golden standard cues, which is 4.11% higher than the system without using DCBS. Although the candidate boundary selection method is only evaluated on hedge scope detection here, it can be popularized to other kinds of scope learning tasks

System architecture.

<p>System architecture.</p

An example of the L-scope candidate selection process with DCBS.

<p>(a) Initialize all nodes’ color; Select the L-scope candidate boundary from (b) to (f); (g) Output the L-scope candidate nodes.</p

Comparison with the related work.

<p>Comparison with the related work.</p

Peer-to-Peer Trading for Energy-Saving Based on Reinforcement Learning

This paper proposes a new peer-to-peer (P2P) energy trading method between energy sellers and consumers in a community based on multi-agent reinforcement learning (MARL). Each user of the community is treated as a smart agent who can choose the amount and the price of the electric energy to sell/buy. There are two aspects we need to examine: the profits for the individual user and the utility for the community. For a single user, we consider that they want to realise both a comfortable living environment to enhance happiness and satisfaction by adjusting usage loads and certain economic benefits by selling the surplus electric energy. Taking the whole community into account, we care about the balance between energy sellers and consumers so that the surplus electric energy can be locally absorbed and consumed within the community. To this end, MARL is applied to solve the problem, where the decision making of each user in the community not only focuses on their own interests but also takes into account the entire community&rsquo;s welfare. The experimental results prove that our method is profitable both both the sellers and buyers in the community