文本挖掘在生物医学领域中的应用文献计量学分析

目的:近年来,随着生物医学领域文献数量的急骤增长,大量隐含的规律和新知被掩埋在浩如烟海的文献之中,而将文本挖掘技术应用于生物医学领域则可以对海量生物医学文献数据进行整合、分析,从而获得有价值的信息,提高人们对生物医学现象的认识。本文就我国近十年来文本挖掘技术在生物医学领域的应用现状进行文献计量学分析,旨在为我国科研工作者对该领域的进一步研究提供参考。方法:对国内正式发表的生物医学领域文本挖掘相关文献进行检索和筛选,分别从年度变化、地区分布、研究机构、期刊来源、研究领域等方面进行分析。结果:国内生物医学文本挖掘文献总量呈上升趋势,主要集中在挖掘算法的研究和文本挖掘技术在中医药及系统生物学领域的应用方面;北京、上海、广东等地的研究处于领先地位。结论:相比其他较为成熟的研究课题来说,目前文本挖掘技术在生物医学中的应用在国内还属于一个比较新的研究领域,但国内对该领域的认识正不断提高、研究正不断深入,初步形成了一批在该领域的核心研究地区、核心研究机构和核心研究领域,而对其进一步的研究,必将为生物医学领域的发展注入新的活力。     

A comparative study of cells in inflammation,EAE and MS using biomedical literature data mining

Summary Biomedical literature and database annotations, available in electronic forms, contain a vast amount of knowledge resulting from global research. Users, attempting to utilize the current state-of-the-art research results are frequently overwhelmed by the volume of such information, making it difficult and time-consuming to locate the relevant knowledge. Literature mining, data mining, and domain specific knowledge integration techniques can be effectively used to provide a user-centric view of the information in a real-world biological problem setting. Bioinformatics tools that are based on real-world problems can provide varying levels of information content, bridging the gap between biomedical and bioinformatics research. We have developed a user-centric bioinformatics research tool, called BioMap, that can provide a customized, adaptive view of the information and knowledge space. BioMap was validated by using inflammatory diseases as a problem domain to identify and elucidate the associations among cells and cellular components involved in multiple sclerosis (MS) and its animal model, experimental allergic encephalomyelitis (EAE). The BioMap system was able to demonstrate the associations between cells directly excavated from biomedical literature for inflammation, EAE and MS. These association graphs followed the scale-free network behavior (average γ = 2.1) that are commonly found in biological networks.     

Frontiers of biomedical text mining: current progress

It is now almost 15 years since the publication of the first paper on text mining in the genomics domain, and decades since the first paper on text mining in the medical domain. Enormous progress has been made in the areas of information retrieval, evaluation methodologies and resource construction. Some problems, such as abbreviation-handling, can essentially be considered solved problems, and others, such as identification of gene mentions in text, seem likely to be solved soon. However, a number of problems at the frontiers of biomedical text mining continue to present interesting challenges and opportunities for great improvements and interesting research. In this article we review the current state of the art in biomedical text mining or 'BioNLP' in general, focusing primarily on papers published within the past year.     

The structural and content aspects of abstracts versus bodies of full text journal articles are different

Background  

An increase in work on the full text of journal articles and the growth of PubMedCentral have the opportunity to create a major paradigm shift in how biomedical text mining is done. However, until now there has been no comprehensive characterization of how the bodies of full text journal articles differ from the abstracts that until now have been the subject of most biomedical text mining research.     

A survey of current work in biomedical text mining

The volume of published biomedical research, and therefore the underlying biomedical knowledge base, is expanding at an increasing rate. Among the tools that can aid researchers in coping with this information overload are text mining and knowledge extraction. Significant progress has been made in applying text mining to named entity recognition, text classification, terminology extraction, relationship extraction and hypothesis generation. Several research groups are constructing integrated flexible text-mining systems intended for multiple uses. The major challenge of biomedical text mining over the next 5-10 years is to make these systems useful to biomedical researchers. This will require enhanced access to full text, better understanding of the feature space of biomedical literature, better methods for measuring the usefulness of systems to users, and continued cooperation with the biomedical research community to ensure that their needs are addressed.     

Mining the pharmacogenomics literature--a survey of the state of the art

This article surveys efforts on text mining of the pharmacogenomics literature, mainly from the period 2008 to 2011. Pharmacogenomics (or pharmacogenetics) is the field that studies how human genetic variation impacts drug response. Therefore, publications span the intersection of research in genotypes, phenotypes and pharmacology, a topic that has increasingly become a focus of active research in recent years. This survey covers efforts dealing with the automatic recognition of relevant named entities (e.g. genes, gene variants and proteins, diseases and other pathological phenomena, drugs and other chemicals relevant for medical treatment), as well as various forms of relations between them. A wide range of text genres is considered, such as scientific publications (abstracts, as well as full texts), patent texts and clinical narratives. We also discuss infrastructure and resources needed for advanced text analytics, e.g. document corpora annotated with corresponding semantic metadata (gold standards and training data), biomedical terminologies and ontologies providing domain-specific background knowledge at different levels of formality and specificity, software architectures for building complex and scalable text analytics pipelines and Web services grounded to them, as well as comprehensive ways to disseminate and interact with the typically huge amounts of semiformal knowledge structures extracted by text mining tools. Finally, we consider some of the novel applications that have already been developed in the field of pharmacogenomic text mining and point out perspectives for future research.     

Information retrieval and knowledge discovery utilising a biomedical Semantic Web

Although various ontologies and knowledge sources have been developed in recent years to facilitate biomedical research, it is difficult to assimilate information from multiple knowledge sources. To enable researchers to easily gain understanding of a biomedical concept, a biomedical Semantic Web that seamlessly integrates knowledge from biomedical ontologies, publications and patents would be very helpful. In this paper, current research efforts in representing biomedical knowledge in Semantic Web languages are surveyed. Techniques are presented for information retrieval and knowledge discovery from the Semantic Web that extend traditional keyword search and database querying techniques. Finally, some of the challenges that have to be addressed to make the vision of a biomedical Semantic Web a reality are discussed.     

Bio-Ontology and text: bridging the modeling gap

MOTIVATION: Natural language processing (NLP) techniques are increasingly being used in biology to automate the capture of new biological discoveries in text, which are being reported at a rapid rate. Yet, information represented in NLP data structures is classically very different from information organized with ontologies as found in model organisms or genetic databases. To facilitate the computational reuse and integration of information buried in unstructured text with that of genetic databases, we propose and evaluate a translational schema that represents a comprehensive set of phenotypic and genetic entities, as well as their closely related biomedical entities and relations as expressed in natural language. In addition, the schema connects different scales of biological information, and provides mappings from the textual information to existing ontologies, which are essential in biology for integration, organization, dissemination and knowledge management of heterogeneous phenotypic information. A common comprehensive representation for otherwise heterogeneous phenotypic and genetic datasets, such as the one proposed, is critical for advancing systems biology because it enables acquisition and reuse of unprecedented volumes of diverse types of knowledge and information from text. RESULTS: A novel representational schema, PGschema, was developed that enables translation of phenotypic, genetic and their closely related information found in textual narratives to a well-defined data structure comprising phenotypic and genetic concepts from established ontologies along with modifiers and relationships. Evaluation for coverage of a selected set of entities showed that 90% of the information could be represented (95% confidence interval: 86-93%; n = 268). Moreover, PGschema can be expressed automatically in an XML format using natural language techniques to process the text. To our knowledge, we are providing the first evaluation of a translational schema for NLP that contains declarative knowledge about genes and their associated biomedical data (e.g. phenotypes). AVAILABILITY: http://zellig.cpmc.columbia.edu/PGschema     

生物医学领域本体的构建、评估与应用

介绍了本体的概念和基本特点, 总结了领域本体的一般构建流程和评估方法, 并举例说明了生物医学领域本体在生物学对象注释、富集分析、数据整合、数据库构建、图书馆建设、文本挖掘等方面的实际应用情况, 整理了目前常用的生物医学领域本体数据库、本体描述语言和本体编辑软件, 最后探讨了目前生物医学领域本体研究中普遍存在的问题和该领域未来的发展方向.     

Conceptual biology,hypothesis discovery,and text mining: Swanson's legacy

Innovative biomedical librarians and information specialists who want to expand their roles as expert searchers need to know about profound changes in biology and parallel trends in text mining. In recent years, conceptual biology has emerged as a complement to empirical biology. This is partly in response to the availability of massive digital resources such as the network of databases for molecular biologists at the National Center for Biotechnology Information. Developments in text mining and hypothesis discovery systems based on the early work of Swanson, a mathematician and information scientist, are coincident with the emergence of conceptual biology. Very little has been written to introduce biomedical digital librarians to these new trends. In this paper, background for data and text mining, as well as for knowledge discovery in databases (KDD) and in text (KDT) is presented, then a brief review of Swanson's ideas, followed by a discussion of recent approaches to hypothesis discovery and testing. 'Testing' in the context of text mining involves partially automated methods for finding evidence in the literature to support hypothetical relationships. Concluding remarks follow regarding (a) the limits of current strategies for evaluation of hypothesis discovery systems and (b) the role of literature-based discovery in concert with empirical research. Report of an informatics-driven literature review for biomarkers of systemic lupus erythematosus is mentioned. Swanson's vision of the hidden value in the literature of science and, by extension, in biomedical digital databases, is still remarkably generative for information scientists, biologists, and physicians.     

New directions in biomedical text annotation: definitions, guidelines and corpus construction

Background  

While biomedical text mining is emerging as an important research area, practical results have proven difficult to achieve. We believe that an important first step towards more accurate text-mining lies in the ability to identify and characterize text that satisfies various types of information needs. We report here the results of our inquiry into properties of scientific text that have sufficient generality to transcend the confines of a narrow subject area, while supporting practical mining of text for factual information. Our ultimate goal is to annotate a significant corpus of biomedical text and train machine learning methods to automatically categorize such text along certain dimensions that we have defined.     

Combining literature text mining with microarray data: advances for system biology modeling

A huge amount of important biomedical information is hidden in the bulk of research articles in biomedical fields. At the same time, the publication of databases of biological information and of experimental datasets generated by high-throughput methods is in great expansion, and a wealth of annotated gene databases, chemical, genomic (including microarray datasets), clinical and other types of data repositories are now available on the Web. Thus a current challenge of bioinformatics is to develop targeted methods and tools that integrate scientific literature, biological databases and experimental data for reducing the time of database curation and for accessing evidence, either in the literature or in the datasets, useful for the analysis at hand. Under this scenario, this article reviews the knowledge discovery systems that fuse information from the literature, gathered by text mining, with microarray data for enriching the lists of down and upregulated genes with elements for biological understanding and for generating and validating new biological hypothesis. Finally, an easy to use and freely accessible tool, GeneWizard, that exploits text mining and microarray data fusion for supporting researchers in discovering gene-disease relationships is described.     

Text mining for literature review and knowledge discovery in cancer risk assessment and research

Research in biomedical text mining is starting to produce technology which can make information in biomedical literature more accessible for bio-scientists. One of the current challenges is to integrate and refine this technology to support real-life scientific tasks in biomedicine, and to evaluate its usefulness in the context of such tasks. We describe CRAB - a fully integrated text mining tool designed to support chemical health risk assessment. This task is complex and time-consuming, requiring a thorough review of existing scientific data on a particular chemical. Covering human, animal, cellular and other mechanistic data from various fields of biomedicine, this is highly varied and therefore difficult to harvest from literature databases via manual means. Our tool automates the process by extracting relevant scientific data in published literature and classifying it according to multiple qualitative dimensions. Developed in close collaboration with risk assessors, the tool allows navigating the classified dataset in various ways and sharing the data with other users. We present a direct and user-based evaluation which shows that the technology integrated in the tool is highly accurate, and report a number of case studies which demonstrate how the tool can be used to support scientific discovery in cancer risk assessment and research. Our work demonstrates the usefulness of a text mining pipeline in facilitating complex research tasks in biomedicine. We discuss further development and application of our technology to other types of chemical risk assessment in the future.     

Text mining and ontologies in biomedicine: making sense of raw text

The volume of biomedical literature is increasing at such a rate that it is becoming difficult to locate, retrieve and manage the reported information without text mining, which aims to automatically distill information, extract facts, discover implicit links and generate hypotheses relevant to user needs. Ontologies, as conceptual models, provide the necessary framework for semantic representation of textual information. The principal link between text and an ontology is terminology, which maps terms to domain-specific concepts. This paper summarises different approaches in which ontologies have been used for text-mining applications in biomedicine.     

Literature mining, ontologies and information visualization for drug repurposing

The immense growth of MEDLINE coupled with the realization that a vast amount of biomedical knowledge is recorded in free-text format, has led to the appearance of a large number of literature mining techniques aiming to extract biomedical terms and their inter-relations from the scientific literature. Ontologies have been extensively utilized in the biomedical domain either as controlled vocabularies or to provide the framework for mapping relations between concepts in biology and medicine. Literature-based approaches and ontologies have been used in the past for the purpose of hypothesis generation in connection with drug discovery. Here, we review the application of literature mining and ontology modeling and traversal to the area of drug repurposing (DR). In recent years, DR has emerged as a noteworthy alternative to the traditional drug development process, in response to the decreased productivity of the biopharmaceutical industry. Thus, systematic approaches to DR have been developed, involving a variety of in silico, genomic and high-throughput screening technologies. Attempts to integrate literature mining with other types of data arising from the use of these technologies as well as visualization tools assisting in the discovery of novel associations between existing drugs and new indications will also be presented.     

Trends in computational biology: a summary based on a RECOMB plenary lecture, 1999.

Computational biology, a term coined from analogy to the role of computing in the physical sciences, is now coming into its own as a major element of contemporary biological and biomedical research. Information science and computational science provide essential tools for next generation biological science efforts, from focusing the direction of experimental studies to providing knowledge and insight that can not otherwise be obtained. Going beyond the revolution in biology reflected in the successes of the genome project and driven by the power of molecular biology techniques, computational approaches will provide an underpinning for the integration of broad disciplines for development of a quantitative systems approach to understanding the mechanisms in the life of the cell.     

Data mining and predictive modeling of biomolecular network from biomedical literature databases

In this paper, we present a novel approach Bio-IEDM (biomedical information extraction and data mining) to integrate text mining and predictive modeling to analyze biomolecular network from biomedical literature databases. Our method consists of two phases. In phase 1, we discuss a semisupervised efficient learning approach to automatically extract biological relationships such as protein-protein interaction, protein-gene interaction from the biomedical literature databases to construct the biomolecular network. Our method automatically learns the patterns based on a few user seed tuples and then extracts new tuples from the biomedical literature based on the discovered patterns. The derived biomolecular network forms a large scale-free network graph. In phase 2, we present a novel clustering algorithm to analyze the biomolecular network graph to identify biologically meaningful subnetworks (communities). The clustering algorithm considers the characteristics of the scale-free network graphs and is based on the local density of the vertex and its neighborhood functions that can be used to find more meaningful clusters with different density level. The experimental results indicate our approach is very effective in extracting biological knowledge from a huge collection of biomedical literature. The integration of data mining and information extraction provides a promising direction for analyzing the biomolecular network     

Determination of the Blood,Hormone and Obesity Value Ranges that Indicate the Breast Cancer,Using Data Mining Based Expert System

Breast cancer is a dangerous type of cancer that spreads into other organs over time. Therefore, medical studies are being done for the early diagnosis by means of the anthropometric data and blood analysis values besides the mammographic and histological findings. However, medical studies have identified only cancer-related values but the value ranges indicating the cancer have not been determined yet. Concurrently the automated diagnostic systems are being developed to assist medical specialists in biomedical engineering studies. The range of values or boundaries indicating the cancer are automatically determined in biomedical methods, but only the diagnostic result is presented. Because of this, biomedical studies don't provide enough opportunity for medical experts to evaluate the relationship between values and result. In this study, decision trees that is one of data mining method was applied to anthropometric data and blood analysis values to complete the mentioned deficiencies in breast cancer diagnosis aiming studies. The determined value ranges were also presented visually to medical experts understand them easily. The proposed diagnostic system has accuracy rate up to 90.52% and provides value ranges indicating the breast cancer as well as mathematically presents the relations between the values and cancer.     

Text mining and its potential applications in systems biology

With biomedical literature increasing at a rate of several thousand papers per week, it is impossible to keep abreast of all developments; therefore, automated means to manage the information overload are required. Text mining techniques, which involve the processes of information retrieval, information extraction and data mining, provide a means of solving this. By adding meaning to text, these techniques produce a more structured analysis of textual knowledge than simple word searches, and can provide powerful tools for the production and analysis of systems biology models.