Comparison of named entity recognition methodologies in biomedical documents.

BACKGROUND: Biomedical named entity recognition (Bio-NER) is a fundamental task in handling biomedical text terms, such as RNA, protein, cell type, cell line, and DNA. Bio-NER is one of the most elementary and core tasks in biomedical knowledge discovery from texts. The system described here is developed by using the BioNLP/NLPBA 2004 shared task. Experiments are conducted on a training and evaluation set provided by the task organizers. RESULTS: Our results show that, compared with a baseline having a 70.09% F1 score, the RNN Jordan- and Elman-type algorithms have F1 scores of approximately 60.53% and 58.80%, respectively. When we use CRF as a machine learning algorithm, CCA, GloVe, and Word2Vec have F1 scores of 72.73%, 72.74%, and 72.82%, respectively. CONCLUSIONS: By using the word embedding constructed through the unsupervised learning, the time and cost required to construct the learning data can be saved.

Molecular dynamics simulation of cytotoxicity of graphene nanosheets to blood-coagulation protein.

Graphene is a nanomaterial that is widely used in electronics, biomedicine, and drug-delivery systems. Although it has many industrial applications, the cytotoxicity of graphene has not been sufficiently studied. In this study, the authors used molecular dynamics simulation to investigate how a graphene nanosheet affects a blood-coagulation protein, namely, a tissue factor/FVIIa binary complex bound to a lipid bilayer membrane, in a 4:1 1-palmitoyl-2-oleoyl-sn-glycero-3-phosphocholine/1-palmitoyl-2-oleoyl-sn-glycero-3-phospho-l-serine lipid bilayer mixture. Based on the results, the authors suggest a mechanism for the cytotoxicity of graphene nanosheets to blood-coagulation protein at the molecular level.