Mining Medline for New Possible Relations of Concepts

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1 Mining Medline for New ossible elations of Concepts Wei Huang,, Yoshiteru Nakamori, Shouyang Wang, and Tieju Ma School of Knowledge Science, Japan Advanced Institute of Science and Technology, Asahidai -, Tatsunokuchi, Ishikawa, 93-9, Japan {w-huang, nakamori, Institute of Systems Science, Academy of Mathematics and Systems Sciences, Chinese Academy of Sciences, Beijing, 00080, China {whuang, Abstract. Scientific bibliographies in online databases provide a rich source of information for scientists in support of their research. In this paper, we propose a new method to predict possible relations between a starting, known concept of interest and other concepts by mining scientific literature databases like Medline. The central novel feature of our method is to predict new relations based on finding brothers of middle concepts within the concept hierarchy. The method can help researchers explore new research directions from current scientific literature. Introduction The availability of scientific bibliographies in online databases is a rich source of information for scientists to support their research. Thus, the process of efficiently discovering knowledge from the huge collection of scientific literature has begun to attract more and more attention. Swanson first proposed that complementary but disjointed non-interactive structures in the literature of science do exist and can lead to novel scientific hypotheses that are worth testing [6-9](see also Figure ). Various methods were developed to systematically analyze scientific literature in order to generate novel and plausible hypotheses [-5, 0-4]. Most research on literaturebased discoveries is based on Swanson s theory. Two concepts are assumed to be related, if they co-occur in the same scientific literature. B A C Fig.. The relation AB and BC are reported separately in the documents. The implicit relation AC is a putative new discovery J. Zhang, J.-H. He, and Y. Fu (Eds.): CIS 004, LNCS 334, pp , 004. Springer-Verlag Berlin Heidelberg 004

2 Mining Medline for New ossible elations of Concepts 795 In this paper, we propose a new method to predict potential relations between A and C by mining scientific literature databases like Medline. The remainder of this paper is organized as follows. In Section, we describe our method in detail. In Section 3, we apply the method to ten diseases and compare the results with those from the previous research. Some conclusions are given in Section 4. Our Method As in most of the previous research on literature-based discoveries, we choose biomedicine as the field in which to demonstrate our method.. Medline and MeSH Tree Medline is a premier source for bibliographic coverage of biomedical literature produced by the U.S. National Library of Medicine (NLM). An example of a Medline citation is shown as follows: MID MH - Multiple Sclerosis/*genetics/ immunology MH - Optic Neuritis/*genetics/immunology MH - olymerase Chain eaction MID and MH mean unique identification number, Medical Subject Headings (MeSH) associated to each Medline citation. MeSH terms are the controlled vocabulary created, maintained and provided by NLM. Indexers always choose the most specific MeSH terms available to describe the subject content of a Medline citation. MeSH terms provide a consistent way to retrieve information that may use different terminology for the same concepts. NLM arranges MeSH terms with their associated tree numbers in a hierarchical structure way, called MeSH Tree. The sample text from MeSH Trees is shown as follows: eptiles;b0.833 Alligators and Crocodiles;B Dinosaurs;B Lizards;B Iguanas;B Snakes;B Boidae;B Colubridae;B Brother concepts are concepts that belong to the same direct super-concept in a hierarchical structure of concepts. According to the tree number associated with each MeSH term, we can easily find its brother concepts in MeSH Tree. For example, from the above sample text, we can know that the concept Alligators and Crocodiles has at least three brothers, namely Dinosaurs, Lizards, Snakes. They belong to the same direct super-concepts eptiles.

3 796 W. Huang et al.. Algorithm Step. Let A be a given starting concept of interest. Step. Find all the middle concepts B that co-occur with A in a given set of scientific literature. Step 3. Find all targeting concepts C that are brothers of B in the hierarchical structure of concepts. Step 4. Eliminate those targeting concepts C that already co-occur with A in the given set of scientific literature. Step 5. The remaining targeting concepts C are candidates for new relations between A and C. Since relation AB has been already reported in scientific literature, while B and C are similar to each other (brother relation), we suppose that the relation AC will probably hold and be reported in the future..3 Combinatorial roblems and Filtering Functions Because a starting concept A may co-occur with more than one middle concept B in a body of scientific literature, and a middle concept B may also have more than one brother C, the combination result will produce a considerable number of targeting concepts. In order to deal with this combinatorial problem, it is necessary to add filtering functions, which can be interactively enforced by the users. For example, a filtering function is to set the threshold of the co-occurring frequency of a starting concept A and middle concepts B. The threshold can be set to the average co-occurring frequency of a starting concept A and middle concepts B (AVGS), or AVGS. We may only consider those middle concepts B with a co-occurring frequency exceeding the threshold and search the brother concepts of those B in a hierarchical structure of concepts. 3 Experiment In order to clearly demonstrate our method, we apply it to ten diseases used as starting concepts A shown in Table, and compare the prediction results with those of Hristovski et al []. 3. Experiment reparation We divide the Medline database into two segments according to the publication date of literature, namely the old segment ( ) and the new segment ( ). First, we search Medline to collect the citations indexed with the starting concepts in the two segments, respectively. Second, we apply our method on the citations from the old segment to predict targeting concepts that form the new relations with a starting concept. If a targeting concept later co-occurs with the starting concept in at least one citation from the new segment, the new relation between the two concepts is a successful prediction. We use ecall and recision to measure prediction performance.

4 Mining Medline for New ossible elations of Concepts 797 ecall: recision: = l = () k where is the ratio of successfully predicted new relations to all new previously unreported relations; l is the number of successfully predicted new relations; k is the number of relations in the new segment that are not reported in the old one; is the ratio of successfully predicted new relations to all predicted relations; m is the number of targeting concepts that form new relations with starting concepts, based on the old segment. 3. Experiment esults Table and show the prediction results of new relations between starting concepts and targeting concepts by using our method and Hristovski s method, respectively. Column k is the number of new relations in the new segment that are not present in the old one. Column m is the number of targeting concepts that form new relations with starting concepts, based on the old segment. Column l is the number of successfully predicted new relations. Column and are ecall and recision, respectively. The column headings with subscript and are prediction results when the threshold of the co-occurring frequency of a starting concept A and middle concepts B is set to AVGS and AVGS, respectively. Here we analyze the results for the starting concept multiple sclerosis (MS). MS co-occurs with 807 new concepts in the new segment. When the threshold is AVGS, our method proposes 44 targeting concepts that form new relations with the starting concept MS. 3 of the 44 targeting concepts belong to the 807 new concepts. That is to say, our method successfully predicts 3 new relations. Therefore, ecall is 0.8 and recision is When the threshold is AVGS, the number of targeting concepts drops to 57; 5 of them belong to the 807 new concepts. Therefore, ecall is 0.9 and recision is 0.0. In other words, increasing the threshold can reduces ecall greatly, but improves recision a little. It should be noted that we only give examples of setting threshold value to limit the number of new possible relations of concepts. In practical use, researchers can select appropriate threshold values to obtain a suitable number of new relations. Let s compare the prediction results of the two methods. First, our method s ecall is lower than those of Hristovski s method. However, the values of m and m in Table are both smaller than those in Table for the corresponding diseases. That is to say, our method proposes fewer targeting concepts to form new relations with a starting concept. Second, when the threshold is AVGS, in eight cases our method s recision is higher; in the other two cases, recision for the two methods is the same. Third, when the threshold is AVGS, our method s recision is higher in four cases and lower in four cases; in the last two cases, recision for the two methods is the same. It indicates that our method performs a little better in term of recision. In fact, l m ()

5 798 W. Huang et al. both of the two methods propose a considerable number of targeting concepts. Users need to choose some of the candidates as actual research themes based on their research interest and background knowledge. Therefore, users are more interested in the quality of such candidates instead of the quantity. In other words, recision is more important than ecall in measuring the prediction performance. Table. The prediction result of our method Starting Concept A k m m multiple sclerosis temporal arteritis melanoma parkinson disease incontinentia pigmenti chondrodysplasia punctata charcot-marie-tooth disease focal dermal hypoplasia noonan syndrome ectodermal dysplasia Table. The prediction result of Hristovski et al Starting Concept A k m m multiple sclerosis temporal arteritis melanoma parkinson disease incontinentia pigmenti chondrodysplasia punctata charcot-marie-tooth disease focal dermal hypoplasia noonan syndrome ectodermal dysplasia Conclusions In this paper, we propose a new method to predict possible relations between a starting, known concept of interest and other concepts by mining scientific literature databases like Medline. The central novel feature of our method is to predict new relations based on finding brothers of middle concepts within the concept hierarchy. The

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