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With fast development of viral hepatitis research, a large number of the research achievements have been generated and scattered in various literatures. Information service providers are meeting the challenge of satisfying readers’ needs for more efficient and intelligent retrieval. Data mining and information integration are basically the promising and effective ways which become more and more important. Our study describes how to build the viral hepatitis bibliographic database, how the viral hepatitis related protein information is mined from the viral hepatitis bibliographic database, and integrated with corresponding information in the Universal protein resource - the Uniprot database from EBI. With the help of Chinese and English bilingual protein control vocabulary built by ourselves, mining of the viral hepatitis related protein text in the bilingual bibliographic database is realized and integration with corresponding protein information in the Uniprot database is achieved. In a word, our paper describes the integration and mapping between Chinese-English bilingual bibliographic databases and the authoritative factual databases (the Uniprot database) through relevant text mining works. It would be useful for extension, utilization and mining of Chinese-English bilingual bibliographic well as cross lingual retrieval, integration, and resources, as information mining. 1
At present, global mass information floods and affects all aspects of human life. As one of the most active research fields, life science generates countless achievements and datasets that scatter in various literatures every year. In life science field, viral hepatitis is a seriously infecting disease resulted from various hepatitis viruses. So, viral hepatitis is, arguably, one of the most intensely studied viruses in the history of biomedical research over the world. With fast development of viral hepatitis research, a large number of the research achievements have been generated and scattered in various literatures. Although most of them are accessible through databases and web sites, it is still a problem for readers to identify what they really need from enormous search results. So mining and information integration are essential to meet readers’ needs for more efficient and intelligent retrieval. Different useful information resources can be further integrated after the information is filtered , digitized and mined, The integration of information resources could be chosen, organized and processed according to the needs of different readers or users so as to yield the new information resources and new knowledge formation. The integration of digital information resources includes: data integration, information integration, knowledge integration, in which knowledge integration is at the highest level of resource integration system, which is based on the inevitable requirement and result of data and information integration to a certain stage. Knowledge mining is a complex process of identifying effective, novel, potentially useful information and knowledge from the information database (Feng and Wang, 2008). Information integration allows users to get the most extensive information, while knowledge mining allows users to quickly find the knowledge they want from the infinite information ocean. The application of information integration and knowledge mining technology and the establishment of linked and integrated database knowledge service system will allow users to quickly and efficiently find the necessary information and knowledge (Zhang et al., 2010).
Nowadays, many professional databases have been developed to the era of data mining and integration, knowledge mining and discovery, and greatly focus on information integration and knowledge mining so as to realize link and integration between different type of database through the one-way or two-way mode, which makes the relevant different types of database connected into a interactive organic whole, and enriches the extension and expansion capabilities of the relevant database. Some successful works have been carried out, such as GOPubMed, which can automatically recognize concepts from user’s search query to PubMed and display papers containing relevant terms (Doms and Schroeder, 2005), and Entrez, an integrated search system that enables access to multiple National Center for Biotechnology Information (NCBI) databases (Maglott et al., 2011). Similar works are also reported by Alexopoulou et al (2008), Chen et al. (2013), McGarry et al. (2006), Pasquier (2008), and Sahoo et al. (2007). Different useful information resources can be further integrated after this information is filtered, digitized and mined. The innovation of database design and construction makes users deeply experience the charm and potential of information integration and knowledge mining.
In summary, with the development of international scientific database, information integration and knowledge mining has become the mainstream and the trend of digital information resources processing and utilization. the semantic network is the environment of information integration, ontology is the core of semantic web construction and foundation. Construction of the professional domain ontology, based on the integration and mining of digital information resources will become the focus of information integration and knowledge mining research (Yan, 2008). Based on the analysis of domestic and foreign database information integration and knowledge mining theory and application, authors learning from advanced foreign information integration and knowledge mining technology explore the association and integration of the Chinese and English bilingual literature databases of viral hepatitis and the related scientific data databases at home and abroad in the innovation construction of the viral hepatitis special literature knowledge database, moreover, the authors further study the deep processing of the subject classification index of the literature in the knowledge database from the user's needs so as to facilitate the readers’ use and retrieval.
As you know, literature database and protein science database are the ones of the most important support source for hepatitis virus researchers. So in this paper, we build the viral hepatitis bilingual bibliographic database and perform viral hepatitis related protein text mining and integrating with the Uniprot protein database so as to give our vigorous support for the sino-foreign hepatitis virus researchers’ information retrieval and knowledge discovery.
2.1
Data resources: Medline database which is from NCBI for English dataset, CNKI database which is from China National Knowledge Infrastructure for Chinese dataset, and Uniprot protein database which is from EBI (European Bioinformatics Institute) for protein dataset.
① Collect, select and process the viral hepatitis and hepatitis virus A, B, and C related dataset (literature data) from the above Chinese and English database;
② Build the bilingual text mining control vocabulary (dictionary);
③ Perform text mining of viral hepatitis related proteins in the viral hepatitis bilingual literature database;
④ Perform preliminary research on eliminating the false positive ones from mining results;
⑤ Integrate the viral hepatitis bilingual literature database with the Uniprot protein database on the basis of the mined hepatitis virus A, B and C related protein. 2.2
1. System architecture: 3-tier structure based on B/S model ( separateness of web server and database server). See fig.1 as follows: 2. System hardware platform: IBM 4 core servers 3. System software platform: Operating system: Linux, Ubuntu 9.04 WEB server: Nginx 0.87 Database software: MySql 5.6.22 Development language: C++ for information index module and data mining module, and PHP for web application module. 4. Integration design architecture of database system platform. See fig.2 as follows: Figure 2 demonstration: On the one hand, literature records about viral hepatitis A, B and C from Medline database of Web of Science platform in English and from CNKI database of China in Chinese were screened, collected and processed into the viral hepatitis related literature knowledge data warehouse. On the other hand. The control vocabulary of Uniprot protein database from EBI was also screened, collected, processed and translated into the Chinese & English bilingual viral hepatitis related protein text mining control vocabulary. Then the indexed viral hepatitis subject literature knowledge database was built by index program including improved index procedure control and optimizing index algorithm through application of the protein text mining control vocabulary in the processed viral hepatitis related literature data warehouse. Finally, integration of the indexed viral hepatitis subject literature knowledge database and Uniprot protein database was realized by mapping ruler through protein text or knowledge mining algorithm and machine learning. 5. Viral hepatitis related literature indexing and processing. See fig.3 as follows:
Figure 3 literature indexing and processing flow chart Figure 3 demonstration: The literatures in the viral hepatitis knowledge data warehouse were indexed and processed according to three stages in the flow chart. Stage 1 is preprocessing before index. Stage 2 is control during indexing procedure. Stage 3 is feedback control after index. Aim of all three stages above is to protect protein text mining from false positive indexing and mining results. 6. Database system function module components: ① Information issue/management system ② Literature knowledge database processing/maintaining system ③ Administration system for user right and
IP address ④ Information index system ⑤ Knowledge mining system ⑥ Knowledge inquiry system ⑦ Data maintaining system ⑧ Web site visiting and statistical system
Part exemplary diagram for the bilingual control vocabulary. See fig.4 as follows:
Figure 4 Demonstration diagram of part exemplary for the bilingual control vocabulary of viral hepatitis (A, B, C) protein
Using the HBV related protein text as example to demonstrate information integrating and hyperlinking regulation for the mined English protein text in literature. See as follows: ① HBeAg, http://lifecenter.sgst.cn/protein/cn/quic kSearch.do?entrezWord=HBeAg ② Capsid protein, http://lifecenter.sgst.cn/protein/cn/quickSearch.do?e ntrezWord=Capsid%20protein ③ Large envelope protein, http://lifecenter.sgst.cn/protein/cn/quickSearch.do?e ntrezWord=Large%20envelope%20protein ④ RNA-directed DNA polymerase http://lifecenter.sgst.cn/protein/cn/quickSearch.do?e ntrezWord=RNA-irected%20DNA%20polymerase While for the mined Chinese protein text in literature: Translate the Chinese protein into English protein text in advance, such as “乙型肝炎 e 抗原”is translated into “ HBeAg”, “ 衣 壳 蛋 白 质 ” is translated into “Capsid protein ” , then performing information integrating and hyperlinking according to regulations above and examples.
Main performance index of the database system: 1. The biggest record number for the literature information: 0.2 billion.
2. Index and data mining time: at current condition of the database system containing one million four hundred and seventy thousand (1,470,000) control vocabularies and about twenty thousand (20,000) literature records, the index and data mining time is about eighteen minutes.
The index and data mining time is about five minutes after the single literature record is added. 3. The average retrieval time: < 0.03s (second) 4. The amount of concurrency (the number of users simultaneous access): >50 people Viral hepatitis subject literature knowledge database extends three functions through data mining, information integration and hyperlinking
1. Obtain the protein sequence and annotation information
2. Perform homological analysis of the protein sequences (BLAST)
3. Perform different alignment of the protein sequences and evolutionary tree mapping 2.3
Homepage of the viral hepatitis subject literature knowledge database. See fig.5 as follows:
Figure 5 Homepage of the viral hepatitis subject literature knowledge database
The viral hepatitis related proteins are successfully mined by using the bilingual control vocabulary, algorithm and computer program in the viral hepatitis bilingual bibliographic database. Moreover, the viral hepatitis bilingual bibliographic database is protein database through the protein mining and information integration. See the fig.6, 7, 8 as follows:
Obtain the hepatitis viral protein sequence and annotation information. See fig.9 as follows: Result of homological analysis of the protein sequences (BLAST). See fig.10 as follows: Obtain the evolutionary tree mapping. See fig.11 as follows: 3.1
The viral hepatitis bilingual bibliographic database was successfully built, and protein text was also successfully mined, and two different classes of databases were also triumphantly integrated, but we encountered some problems, especially such as false positive mining results in bilingual protein text mining. Having investigated the false positive questions, we think there are probably three causes resulting in the false positive mining results: 1) Low quality of the original datasets collected; 2) The accuracy and unity of a specialized word usage is not enough in building of bilingual control vocabulary;
3) In data mining and integration, computer algorithms, mining mode and route selection, and algorithm itself are unreasonable or the system has defects.
As for the problems above, we use artificial quality control to handle the collected original datasets; refer to specialized dictionary and consult the experts to solve the accuracy and unity question of a specialized word usage; try to explore different algorithms, mining mode and route to solve accuracy and efficiency question of data mining and integration.
After the viral hepatitis bilingual bibliographic database was used and demonstrated, we have got many feedbacks from users. Most of them love the convenience of easily searching hepatitis viral protein names, locating highlighted viral protein names in search results, and accessing UniProt database for the detailed protein information through information integration and links. But they also raised some questions and proposed many advices. Overall, however, the feedback is very positive so far. According to users’ suggestions and problems, we have discovered, following issues are currently being considered and actually some of them are being undertaken in order to further enhance the system and make it more efficient and convenient:
1) add more hepatitis viral protein names and their features into the English-Chinese Controlled-vocabulary dictionary. This work is continuously being conducted and actually we also plan to add relationships of hepatitis viral proteins and other relevant information so as to finally construct a Chinese hepatitis viral protein ontology. Then it would be possible to realize semantic-based text mining and provide users with knowledge-based information service.
2) integrate more factual scientific databases, especially factual gene databases. Some users are also interested in other special fields, such as evidence-based medicine, AIDS, etc. If search results of a special topic from a bibliographic database can be integrated with relevant factual scientific databases, it is certainly very helpful and convenient for users. This is an interesting direction for information integration and knowledge mining. 3.2
With the fast development of the viral hepatitis research, to satisfy user’s information needs is becoming an inevitable challenge. So, construction of the viral hepatitis bilingual literature database is important, significant and useful. Integration of two different classes of databases via data mining and linking is innovative and trend for database development. Moreover, information integration and data mining are playing a more and more important role in big data era. 3.3
In order to solve the problems above, future work must be done as follows:
1) Constantly extend and update datasets in viral hepatitis bilingual literature database; 2) Constantly improve mining and integrating quality so as to decrease the false positive results as low as possible through algorithm improvement and machine learning;
3) Further improve accuracy and unity of the bilingual control vocabulary;
4) The viral hepatitis bilingual literature database will be linked more factual scientific atabase via data mining and information integration.
This work is supported by The Chosen Excellent Program for Introduced Outstanding Talent of Chinese Academy of Sciences in the Fields of Bibliographical Information and Periodical Publication 2010 (Subject field 100 talent program) and Chinese National Science and Technology Support Project (No.2013BAH21B06)