Relation extraction for biological pathway construction using node2vec.

BACKGROUND: Systems biology is an important field for understanding whole biological mechanisms composed of interactions between biological components. One approach for understanding complex and diverse mechanisms is to analyze biological pathways. However, because these pathways consist of important interactions and information on these interactions is disseminated in a large number of biomedical reports, text-mining techniques are essential for extracting these relationships automatically. RESULTS: In this study, we applied node2vec, an algorithmic framework for feature learning in networks, for relationship extraction. To this end, we extracted genes from paper abstracts using pkde4j, a text-mining tool for detecting entities and relationships. Using the extracted genes, a co-occurrence network was constructed and node2vec was used with the network to generate a latent representation. To demonstrate the efficacy of node2vec in extracting relationships between genes, performance was evaluated for gene-gene interactions involved in a type 2 diabetes pathway. Moreover, we compared the results of node2vec to those of baseline methods such as co-occurrence and DeepWalk. CONCLUSIONS: Node2vec outperformed existing methods in detecting relationships in the type 2 diabetes pathway, demonstrating that this method is appropriate for capturing the relatedness between pairs of biological entities involved in biological pathways. The results demonstrated that node2vec is useful for automatic pathway construction.

Application of a novel electrochemical sensor containing organo-modified sericite for the detection of low-level arsenic.

A simple cyclic voltammetry (CV) analytical method with organo-modified sericite for the working electrode was investigated to detect As(III) in an aquatic environment, and optimal conditions for the reliable measurement of trace amounts of As(III) were studied. A distinct, specific peak was clearly observed at 0.8 V due to the reduction of H3AsO4 to H3AsO3. The specific peak current of arsenic increased with increasing the concentration of As(III) and initially increased proportionally to the scan rates. However, it disappeared as the scan rate increased over 400 mV/s. Because the surface of the organo-modified sericite electrode rapidly became saturated with As(III) when the deposition time increased, an optimal deposition time was determined as 60 s. Pb(2+) had no significant influence on the peak signal of As(III), whereas it was reduced as the ratio of Cu/As increased. Considering the detection limit of arsenic (1 ppb), this system can be used to detect low levels of As(III) in water systems.