Biomedical subjects

Minlie Huang

Publications and source records attributed to Minlie Huang.

3 recordsLinked to original sources

PubMed2005

A hybrid method for relation extraction from biomedical literature.

PURPOSE: Over recent years, there has been a growing interest in extracting entities and relations from biomedical literature. There are a vast number of systems and approaches being proposed to extract biological relations, but none of them achieves satisfactory results. These methodologies are either parsing-based or pattern-based, which are not competent to handle the grammatical complexities of biomedical texts, or too complicated to be adapted. It is well known that appositive, coordinative propositions and such grammatical structures are extremely common in biomedical texts, particularly in full texts. However, these problems are still untouched for most of researchers. METHODS: In this paper, we have proposed a new approach, which is hybrid with both shallow parsing and pattern matching, to extract relations between proteins from scientific papers of biomedical themes. In the method, appositive and coordinative structures are interpreted based on the shallow parsing analysis, with both syntactic and semantic constraints. Then long sentences are splitted into sub-ones, from which relations are extracted by a greedy pattern matching algorithm, along with automatically generated patterns. RESULTS: Our approach is experimented to extract protein-protein interactions from full biomedical texts, and has achieved an average F-score of 80% on individual verbs, and 66% on all verbs. With the help of shallow parsing analysis, pattern matching is improved remarkably. Compared with the traditional pattern matching algorithm, our approach achieves about 7% improvement of both precision and F-score. In contrast to other systems, our approach achieves performance comparable to the best. A demo system has been available at http://spies.cs.tsinghua.edu.cn.

Abstracting and Indexing↗

PubMed2005

Discovering patterns to extract protein-protein interactions from the literature: Part II.

MOTIVATION: An enormous number of protein-protein interaction relationships are buried in millions of research articles published over the years, and the number is growing. Rediscovering them automatically is a challenging bioinformatics task. Solutions to this problem also reach far beyond bioinformatics. RESULTS: We study a new approach that involves automatically discovering English expression patterns, optimizing them and using them to extract protein-protein interactions. In a sister paper, we described how to generate English expression patterns related to protein-protein interactions, and this approach alone has already achieved precision and recall rates significantly higher than those of other automatic systems. This paper continues to present our theory, focusing on how to improve the patterns. A minimum description length (MDL)-based pattern-optimization algorithm is designed to reduce and merge patterns. This has significantly increased generalization power, and hence the recall and precision rates, as confirmed by our experiments. AVAILABILITY: http://spies.cs.tsinghua.edu.cn.

Abstracting and Indexing↗

PubMed2004

Discovering patterns to extract protein-protein interactions from full texts.

MOTIVATION: Although there are several databases storing protein-protein interactions, most such data still exist only in the scientific literature. They are scattered in scientific literature written in natural languages, defying data mining efforts. Much time and labor have to be spent on extracting protein pathways from literature. Our aim is to develop a robust and powerful methodology to mine protein-protein interactions from biomedical texts. RESULTS: We present a novel and robust approach for extracting protein-protein interactions from literature. Our method uses a dynamic programming algorithm to compute distinguishing patterns by aligning relevant sentences and key verbs that describe protein interactions. A matching algorithm is designed to extract the interactions between proteins. Equipped only with a dictionary of protein names, our system achieves a recall rate of 80.0% and precision rate of 80.5%. AVAILABILITY: The program is available on request from the authors.

Algorithms↗