蛋白质相互作用信息的文本挖掘研究进展

蛋白质相互作用是生命活动中一种极其重要的生物分子关系, 对此领域的研究不仅具有理论意义, 还具有较强的应用价值. 近年来, 随着研究的深入, 各种蛋白质相互作用的生物医学文献激增, 挖掘其中的蛋白质相互作用关系成为人们面临的一大挑战. 当前, 已提出了多种文本挖掘方法, 对分散于生物医学文献中的蛋白质相互作用信息进行结构化或半结构化处理. 对这些工作进行分析, 总结出基于生物文本挖掘蛋白质相互作用信息的一般流程, 从蛋白质命名实体的识别、蛋白质相互作用关系的提取和蛋白质相互作用注释信息的提取3个子任务进行阐述, 同时介绍了生物文本挖掘领域的评测会议和一些挖掘蛋白质相互作用相关信息的工具. 最后, 对该领域存在的一些重要问题进行分析, 并预测了未来可能的发展方向, 以期对该领域相关研究提供一定的参考.     

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.     

基于文献可视化分析的近30年肠道菌群研究趋势及展望

目的全面分析近30年国内外肠道菌群相关研究的发展现状、研究热点及前沿动态,为本领域研究方向提供可视化分析数据。方法以中国知网、Web of Science为来源数据库,检索肠道菌群相关文献,应用CiteSpace(5.5.R2)软件对作者、机构、关键词和被引文献进行共现分析、聚类分析和可视化表达,并对关键词进行突显分析。结果共纳入中文文献912篇,英文文献10 249篇。近30年肠道菌群相关研究年发文量整体呈上升趋势,中文文献发文量明显低于英文文献发文量;英文文献国家总体发文量中,美国排名第一,影响力也最高,中国发文量排名第二,但影响力较低。国外核心研究人员为Rob Knight、Patrice D Cani,国内核心研究人员有王军、赵立平,中文文献发文量较高的作者是段金廒、谭周进、余倩;国外主要机构有哥本哈根大学、法国农业科学研究院等,国内的主要研究机构是中国科学院,其英文文献发文量排名第一;中文文献发文量较多的是中医药类大学。主要研究内容涉及菌群结构、肠道菌群与疾病的关系以及菌群检测技术3个方面。近年来,肠道菌群相关研究多围绕与疾病的相关性及新技术、新方法展开。比较中英文热点关键词,英文关键词丰富,涉及生理、病理各个方面,其中肠道菌群与免疫相关研究成果颇丰。结论作为生物医学领域的研究热点,近30年来肠道菌群研究领域取得了突出的成果,提升肠道菌群检测技术、关注肠道菌群研究成果的应用价值是未来本领域研究的关键。     

太赫兹技术在生物医学中的应用

在人类可持续发展的道路上,生物医学研究始终处于核心地位。太赫兹(THz)技术依靠其自身的优势,在生物医学领域得到了广泛的应用。本文着重介绍了太赫兹表征技术以及太赫兹生物效应在生物医学方面的应用。太赫兹表征技术应用领域的介绍主要分为5个部分:氨基酸和多肽、DNA、蛋白质、癌症的检测和龋齿诊断等其他领域的应用。太赫兹生物效应的研究则集中于THz对机体组织和细胞的作用上。此外,本文简单介绍了化学计量学方法以及其与THz技术结合在生物医学方面的应用。最后总结了THz技术在生物医学领域的不足,并对进一步研究的方向进行了讨论。本文主要对太赫兹技术在生物医学方面的应用进行综述,为相关研究奠定了基础。     

MEDLINE摘要本地下载与更新及癌基因表达数据的文本挖掘(英文)

近年来对生物医学文献的文本挖掘在功能基因组学研究中得到了广泛开展。为了更好的检索MEDLINE摘要,本文介绍利用Unix文本过滤命令实现了对摘要的自动下载和更新。同时,对癌基因表达数据,如癌的种类,癌基因表达情况,及与p53基因的关联等进行了初步的文本挖掘分析。     

基于文献计量分析的土壤微生物群落构建研究态势

土壤微生物群落构建是当前的热点研究领域,中国学者对此开展了大量研究,发表了众多高水平论文。采用文献计量方法,对2003-2022年期间WOS （Web of Science）核心合集数据库收录的该研究领域论文的数量、被引频次、作者、国家（地区）、研究机构、期刊以及关键词进行统计。基于VOSviewer可视化软件分析贡献居前的作者、国家（地区）、研究机构、期刊以及重要关键词,借助Scimago Graphica软件将不同国家（地区）的发文数量可视化,利用bibliometrix包（R语言）分析研究热点的演变趋势。结果表明：在2003-2022年间,中国学者在本研究领域共发表论文374篇,排名世界第一;根据年度发文量,该领域的研究历程大体可分为三个阶段,分别为萌芽期（2003-2005年）、波动发展期（2006-2011年）和指数增长期（2012-2021年）;Zhou JZ （周集中）、Li XZ （李香真）和Chu HY （褚海燕）是本研究领域的核心作者,中国科学院是核心研究机构,Frontiers in Microbiology为WOS数据库文献的主要来源期刊;本领域研究热点词的演变与测序技术的发展紧密关联,测序技术从454焦磷酸测序发展到高通量测序和宏基因组测序,推动了该领域从优先效应和中性理论等概念的引入发展到通过模型确定确定性过程和随机性过程相对贡献的定量研究。在系统描述2003-2022年土壤微生物群落构建领域的研究现状及研究热点的基础上,提出了未来可能的一些重要研究方向,有助于相关学者加深对该研究领域的理解,对进一步聚焦土壤微生物群落构建领域的研究方向具有重要参考价值。     

基于机器学习的蛋白质相互作用文献挖掘方法研究进展

蛋白质相互作用是生物体内一类极其重要的分子活动.自动挖掘、整合生物文献中的蛋白质相互作用有助于生物学的研究,获得了人们的广泛关注,成为生物文献挖掘领域的重要任务之一.目前,基于机器学习的蛋白质相互作用挖掘方法已经取得了很大进步,对该领域的进展进行归纳总结将有助于方法的进一步优化和应用.本文在对机器学习方法构建流程介绍的基础上,进一步从机器学习的分类器、学习特征、方法评估以及挖掘系统4个方面对蛋白质相互作用文献挖掘进行系统总结,并探讨了其发展前景.     

基于Web of Science数据库扩张型心肌病研究的文献计量学分析

目的:对扩张型心肌病(Dilated Cardiomyopathy,DCM)的研究,目前仍是国际上对于原发性心肌病研究的热点问题。本文针对DCM相关领域的研究文献进行计量分析,从而进一步深入了解国际DCM研究进展,为该研究的相关领域提供参考。方法:基于(SCIE)引文数据库为检索对象,检索2003-2012年DCM的所有相关文献,分别对不同国家和地区、著者、机构、文献来源期刊及论文学科分布等进行统计分析。结果:共检索出DCM研究文献12728篇,研究论文发表共涉及了107个国家和地区,美国的发文数最多4500篇,占35.36%,其次为德国和日本。中国居第9位,504篇占总发文量的3.96%;主要刊登期刊涵盖了国际上心血管领域的15种知名期刊;研究热点涉及心血管系统及脏病学、分子生物学、基因遗传学等学科。结论:目前DCM研究仍是人们关注的一个热点,美国、德国、日本等发达国家在该领域的研究居领先水平,中国在这一领域也做出了贡献。与领先国家和机构相比,我国亟需进一步加强对DCM的研究。为我国进一步了解和深入研究DCM的方向提出参考。     

基于CiteSpace文献计量法的基因组学研究文献可视化图谱分析

本研究从文献计量学角度对比中外基因组学领域的研究情况,为我国该领域研究提供参考。通过检索Web of Science核心合集及CNKI数据库中于1985年1月1日至2016年4月30日国内外发表的基因组学相关文献,利用文献计量可视化软件Cite Space 4.0.R5 SE对文献进行发文量分析、作者合作网络分析、研究机构合作网络分析、国家合作网络分析、关键词共现分析及文献共被引分析。国内外基因组学研究的发文量总体呈现上升趋势。发文量最多的三个国家依次为美国、英国和中国。美国在基因组学领域已形成了颇具规模的核心机构,我国基因组学的主要研究机构为中科院。中外文文献发文量最多的研究者分别为刘家强、Zhang。国外各研究机构间、作者间的合作关系强于国内。文献中突现和共现的关键词主要有药物基因组学、功能基因组学、比较基因组学、蛋白质组学等。文献共被引分析中,中心性最高的文献为(Yang,2007),被引频次最高的文献为(Lander,2001),突现值最高的文献为(Altschul,1997)。通过文献计量分析和可视化分析可见,我国在基因组学方面的发文量较多,但研究者之间、研究机构之间的合作较少,因此我国在加强基因组学研究力度的同时,还应鼓励各研究机构间的合作与交流,紧紧把握该领域研究的前沿与热点,以促进我国基因组学研究向更多元化、更深层化的方向迈进。     

全球大数据与健康管理的研究热点聚类分析

揭示全球该领域的研究热点。采用文献计量学和双向聚类分析方法。发现全球大数据与健康管理现已达到年均发文量1000篇以上;全球有89个国家和地区都进行了该方面的研究,其中欧洲地区的国家合作交流频繁;该领域中重要出版物有Stud Health Technol Inform、PloS one等;目前研究热点主要聚焦为：蛋白质等生物大分子网络作用的信息挖掘、数据挖掘在药物数据库及电子健康档案的应用、基因组序列数据挖掘在疾病预测中的应用、药物生物信息学的数据挖掘、生物医学大型数据库的数据挖掘、系统生物学的数据挖掘和医疗卫生服务中的数据挖掘等7个方面。     

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.     

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.     

Chapter 16: Text Mining for Translational Bioinformatics

Text mining for translational bioinformatics is a new field with tremendous research potential. It is a subfield of biomedical natural language processing that concerns itself directly with the problem of relating basic biomedical research to clinical practice, and vice versa. Applications of text mining fall both into the category of T1 translational research—translating basic science results into new interventions—and T2 translational research, or translational research for public health. Potential use cases include better phenotyping of research subjects, and pharmacogenomic research. A variety of methods for evaluating text mining applications exist, including corpora, structured test suites, and post hoc judging. Two basic principles of linguistic structure are relevant for building text mining applications. One is that linguistic structure consists of multiple levels. The other is that every level of linguistic structure is characterized by ambiguity. There are two basic approaches to text mining: rule-based, also known as knowledge-based; and machine-learning-based, also known as statistical. Many systems are hybrids of the two approaches. Shared tasks have had a strong effect on the direction of the field. Like all translational bioinformatics software, text mining software for translational bioinformatics can be considered health-critical and should be subject to the strictest standards of quality assurance and software testing.

What to Learn in This Chapter

Text mining is an established field, but its application to translational bioinformatics is quite new and it presents myriad research opportunities. It is made difficult by the fact that natural (human) language, unlike computer language, is characterized at all levels by rampant ambiguity and variability. Important sub-tasks include gene name recognition, or finding mentions of gene names in text; gene normalization, or mapping mentions of genes in text to standard database identifiers; phenotype recognition, or finding mentions of phenotypes in text; and phenotype normalization, or mapping mentions of phenotypes to concepts in ontologies. Text mining for translational bioinformatics can necessitate dealing with two widely varying genres of text—published journal articles, and prose fields in electronic medical records. Research into the latter has been impeded for years by lack of public availability of data sets, but this has very recently changed and the field is poised for rapid advances. Like all translational bioinformatics software, text mining software for translational bioinformatics can be considered health-critical and should be subject to the strictest standards of quality assurance and software testing.

This article is part of the “Translational Bioinformatics” collection for PLOS Computational Biology.

     

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.     

Knowledge acquisition, semantic text mining, and security risks in health and biomedical informatics

Computational techniques have been adopted in medi-cal and biological systems for a long time. There is no doubt that the development and application of computational methods will render great help in better understanding biomedical and biological functions. Large amounts of datasets have been produced by biomedical and biological experiments and simulations. In order for researchers to gain knowledge from origi- nal data, nontrivial transformation is necessary, which is regarded as a critical link in the chain of knowledge acquisition, sharing, and reuse. Challenges that have been encountered include: how to efficiently and effectively represent human knowledge in formal computing models, how to take advantage of semantic text mining techniques rather than traditional syntactic text mining, and how to handle security issues during the knowledge sharing and reuse. This paper summarizes the state-of-the-art in these research directions. We aim to provide readers with an introduction of major computing themes to be applied to the medical and biological research.     

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.     

Literature mining for the biologist: from information retrieval to biological discovery

For the average biologist, hands-on literature mining currently means a keyword search in PubMed. However, methods for extracting biomedical facts from the scientific literature have improved considerably, and the associated tools will probably soon be used in many laboratories to automatically annotate and analyse the growing number of system-wide experimental data sets. Owing to the increasing body of text and the open-access policies of many journals, literature mining is also becoming useful for both hypothesis generation and biological discovery. However, the latter will require the integration of literature and high-throughput data, which should encourage close collaborations between biologists and computational linguists.     

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.     

Passage-Based Bibliographic Coupling: An Inter-Article Similarity Measure for Biomedical Articles

Biomedical literature is an essential source of biomedical evidence. To translate the evidence for biomedicine study, researchers often need to carefully read multiple articles about specific biomedical issues. These articles thus need to be highly related to each other. They should share similar core contents, including research goals, methods, and findings. However, given an article r, it is challenging for search engines to retrieve highly related articles for r. In this paper, we present a technique PBC (Passage-based Bibliographic Coupling) that estimates inter-article similarity by seamlessly integrating bibliographic coupling with the information collected from context passages around important out-link citations (references) in each article. Empirical evaluation shows that PBC can significantly improve the retrieval of those articles that biomedical experts believe to be highly related to specific articles about gene-disease associations. PBC can thus be used to improve search engines in retrieving the highly related articles for any given article r, even when r is cited by very few (or even no) articles. The contribution is essential for those researchers and text mining systems that aim at cross-validating the evidence about specific gene-disease associations.     

Academic and molecular matrices: a study of the transformations of connective tissue research at the University of Manchester (1947-1996)

This paper explores the different identities adopted by connective tissue research at the University of Manchester during the second half of the 20th century. By looking at the long-term redefinition of a research programme, it sheds new light on the interactions between different and conflicting levels in the study of biomedicine, such as the local and the global, or the medical and the biological. It also addresses the gap in the literature between the first biomedical complexes after World War II and the emergence of biotechnology. Connective tissue research in Manchester emerged as a field focused on new treatments for rheumatic diseases. During the 1950s and 60s, it absorbed a number of laboratory techniques from biology, namely cell culture and electron microscopy. The transformations in scientific policy during the late 70s and the migration of Manchester researchers to the US led them to adopt recombinant DNA methods, which were borrowed from human genetics. This resulted in the emergence of cell matrix biology, a new field which had one of its reference centres in Manchester. The Manchester story shows the potential of detailed and chronologically wide local studies of patterns of work to understand the mechanisms by which new biomedical tools and institutions interact with long-standing problems and existing affiliations.