下面是Fanessay提供的一篇paper范文--Automatic Extraction of Protein，这篇文章主要讨论的是蛋白质。蛋白质是人体非常重要的组成部分，也是在生物医学领域非常重要的研究对象。现在，科技进步越来越快，生物医疗也是快速发展，对于提取蛋白质的技术已经是非常成熟了，尽管如此，仍然还是有点问题存在.
Protein-protein interaction extraction is the key precondition of the construction of protein knowledge network, and it is very important for the research in the biomedicine. This paper extracted directional protein-protein interaction from the biological text, using the SVM-based method. Experiments were evaluated on the LLL05 corpus with good results. The results show that dependency features are import for the protein-protein interaction extraction and features related to the interaction word are effective for the interaction direction judgment. At last, we analyzed the effects of different features and planed for the next step.
Keywords: Support vector machines(SVM), Bio-Entity Relation, Protein-Protein Interaction, Entity Relation Direction.
With the rapid development of life sciences, the biomedicine literature has been rising very fast. At present, the technology of information extraction has become mature already. As a result, the research in biomedicine information extraction is becoming more and more important, and relation extraction is one of the most important. Not only is it practical by itself, but it is also the foundation of the relation database and the biological knowledge network, besides it also plays a key role in the relation prediction and the drug producing. Now, the relation extraction has already become a hotspot, but there exists some problems, too. For instance, the result is not good enough, and some important information such as direction and type is ignored.
This paper did research from two aspects: improving the result and exacting more information about relation, direction for example. Towards the characters of biomedicine literature, we designed some new features, and extracted relation with the good machine learning model SVM, and the experiments showed that the results were good.
2 Extracting Protein-Protein Interaction
Once protein names have been found, the relationships between them need to be ascertained. The PPI extraction could be defined as a classification problem. When two protein names and one interaction word co-occur in a single sentence, then we could transfer the mission into inferring weather a PPI exist between the pair of proteins. So, firstly, the sentences were filtered by the simple rule that two protein names co-occur in one sentence. Secondly, we used a trained SVM model to solve this classification problem.
After relation extraction, we decided direction of the relation, because the direction is important to construct a biological network. We also transformed this problem into classification.
SVM model was trained on the standard corpus LLL05 corpus(J. Hakenberg, et al., 2005) and the effective features (word features, POS features, logic features and dependency parsing features). In this experiment, we get 38,504 proteins and 51,568 PPIs between them through the SVM-based method.
The SVM-based medel trained on the LLL05 corpus achieves a good preferment of 82.4% precision, 73.7% Recall and 77.8% F-score. The experiments on LLL05 corpus showed that the F value was as high as 80% and the new features had improved the results a lot. In conclusion, the syntactic features had improved both the precision and the recall while the logic features had improved the recall. What’s more, the syntactic features could make a good result even by itself.
result of protein-protein interaction experiment
Word + POS
+ Logic + Syntax
result of direction judgement experiment
Phisical + Clause
Subtree + Clause
Phisical + Subtree + Clause
This paper extracted several groups of rational features according to the characteristic of protein-protein interaction, and designed the dependency features according to the result of the dependency parsing, which improved the experiment’s effect. Then, this paper extracted some features related to the interaction word, and decided the interaction direction, which provided more effective information for the construction of protein knowledge network and biological entity relation network. We conducted experiments on LLL05 corpus, and analyzed the effect of every features. The results showed that the new designed features had effectively improved the results.
Future work include: validating the expansibility of our method, improving the relation extraction more and constructing the visible biological knowledge network.
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