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.     

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。     

Enhancing biomedical text summarization using semantic relation extraction

Automatic text summarization for a biomedical concept can help researchers to get the key points of a certain topic from large amount of biomedical literature efficiently. In this paper, we present a method for generating text summary for a given biomedical concept, e.g., H1N1 disease, from multiple documents based on semantic relation extraction. Our approach includes three stages: 1) We extract semantic relations in each sentence using the semantic knowledge representation tool SemRep. 2) We develop a relation-level retrieval method to select the relations most relevant to each query concept and visualize them in a graphic representation. 3) For relations in the relevant set, we extract informative sentences that can interpret them from the document collection to generate text summary using an information retrieval based method. Our major focus in this work is to investigate the contribution of semantic relation extraction to the task of biomedical text summarization. The experimental results on summarization for a set of diseases show that the introduction of semantic knowledge improves the performance and our results are better than the MEAD system, a well-known tool for text summarization.     

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.     

Large Scale Application of Neural Network Based Semantic Role Labeling for Automated Relation Extraction from Biomedical Texts

To reduce the increasing amount of time spent on literature search in the life sciences, several methods for automated knowledge extraction have been developed. Co-occurrence based approaches can deal with large text corpora like MEDLINE in an acceptable time but are not able to extract any specific type of semantic relation. Semantic relation extraction methods based on syntax trees, on the other hand, are computationally expensive and the interpretation of the generated trees is difficult. Several natural language processing (NLP) approaches for the biomedical domain exist focusing specifically on the detection of a limited set of relation types. For systems biology, generic approaches for the detection of a multitude of relation types which in addition are able to process large text corpora are needed but the number of systems meeting both requirements is very limited. We introduce the use of SENNA (“Semantic Extraction using a Neural Network Architecture”), a fast and accurate neural network based Semantic Role Labeling (SRL) program, for the large scale extraction of semantic relations from the biomedical literature. A comparison of processing times of SENNA and other SRL systems or syntactical parsers used in the biomedical domain revealed that SENNA is the fastest Proposition Bank (PropBank) conforming SRL program currently available. 89 million biomedical sentences were tagged with SENNA on a 100 node cluster within three days. The accuracy of the presented relation extraction approach was evaluated on two test sets of annotated sentences resulting in precision/recall values of 0.71/0.43. We show that the accuracy as well as processing speed of the proposed semantic relation extraction approach is sufficient for its large scale application on biomedical text. The proposed approach is highly generalizable regarding the supported relation types and appears to be especially suited for general-purpose, broad-scale text mining systems. The presented approach bridges the gap between fast, cooccurrence-based approaches lacking semantic relations and highly specialized and computationally demanding NLP approaches.     

Automated recognition of malignancy mentions in biomedical literature

Background  

The rapid proliferation of biomedical text makes it increasingly difficult for researchers to identify, synthesize, and utilize developed knowledge in their fields of interest. Automated information extraction procedures can assist in the acquisition and management of this knowledge. Previous efforts in biomedical text mining have focused primarily upon named entity recognition of well-defined molecular objects such as genes, but less work has been performed to identify disease-related objects and concepts. Furthermore, promise has been tempered by an inability to efficiently scale approaches in ways that minimize manual efforts and still perform with high accuracy. Here, we have applied a machine-learning approach previously successful for identifying molecular entities to a disease concept to determine if the underlying probabilistic model effectively generalizes to unrelated concepts with minimal manual intervention for model retraining.     

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.     

A realistic assessment of methods for extracting gene/protein interactions from free text

Background  

The automated extraction of gene and/or protein interactions from the literature is one of the most important targets of biomedical text mining research. In this paper we present a realistic evaluation of gene/protein interaction mining relevant to potential non-specialist users. Hence we have specifically avoided methods that are complex to install or require reimplementation, and we coupled our chosen extraction methods with a state-of-the-art biomedical named entity tagger.     

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.     

Multi-dimensional classification of biomedical text: toward automated, practical provision of high-utility text to diverse users

MOTIVATION: Much current research in biomedical text mining is concerned with serving biologists by extracting certain information from scientific text. We note that there is no 'average biologist' client; different users have distinct needs. For instance, as noted in past evaluation efforts (BioCreative, TREC, KDD) database curators are often interested in sentences showing experimental evidence and methods. Conversely, lab scientists searching for known information about a protein may seek facts, typically stated with high confidence. Text-mining systems can target specific end-users and become more effective, if the system can first identify text regions rich in the type of scientific content that is of interest to the user, retrieve documents that have many such regions, and focus on fact extraction from these regions. Here, we study the ability to characterize and classify such text automatically. We have recently introduced a multi-dimensional categorization and annotation scheme, developed to be applicable to a wide variety of biomedical documents and scientific statements, while intended to support specific biomedical retrieval and extraction tasks. RESULTS: The annotation scheme was applied to a large corpus in a controlled effort by eight independent annotators, where three individual annotators independently tagged each sentence. We then trained and tested machine learning classifiers to automatically categorize sentence fragments based on the annotation. We discuss here the issues involved in this task, and present an overview of the results. The latter strongly suggest that automatic annotation along most of the dimensions is highly feasible, and that this new framework for scientific sentence categorization is applicable in practice.     

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     

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.

     

Using co-occurrence network structure to extract synonymous gene and protein names from MEDLINE abstracts

Background  

Text-mining can assist biomedical researchers in reducing information overload by extracting useful knowledge from large collections of text. We developed a novel text-mining method based on analyzing the network structure created by symbol co-occurrences as a way to extend the capabilities of knowledge extraction. The method was applied to the task of automatic gene and protein name synonym extraction.     

Knowledge discovery in biology and biotechnology texts: a review of techniques, evaluation strategies, and applications

Arguably, the richest source of knowledge (as opposed to fact and data collections) about biology and biotechnology is captured in natural-language documents such as technical reports, conference proceedings and research articles. The automatic exploitation of this rich knowledge base for decision making, hypothesis management (generation and testing) and knowledge discovery constitutes a formidable challenge. Recently, a set of technologies collectively referred to as knowledge discovery in text (KDT) has been advocated as a promising approach to tackle this challenge. KDT comprises three main tasks: information retrieval, information extraction and text mining. These tasks are the focus of much recent scientific research and many algorithms have been developed and applied to documents and text in biology and biotechnology. This article introduces the basic concepts of KDT, provides an overview of some of these efforts in the field of bioscience and biotechnology, and presents a framework of commonly used techniques for evaluating KDT methods, tools and systems.     

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.     

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.     

Concept recognition for extracting protein interaction relations from biomedical text

Background:

Reliable information extraction applications have been a long sought goal of the biomedical text mining community, a goal that if reached would provide valuable tools to benchside biologists in their increasingly difficult task of assimilating the knowledge contained in the biomedical literature. We present an integrated approach to concept recognition in biomedical text. Concept recognition provides key information that has been largely missing from previous biomedical information extraction efforts, namely direct links to well defined knowledge resources that explicitly cement the concept's semantics. The BioCreative II tasks discussed in this special issue have provided a unique opportunity to demonstrate the effectiveness of concept recognition in the field of biomedical language processing.

Results:

Through the modular construction of a protein interaction relation extraction system, we present several use cases of concept recognition in biomedical text, and relate these use cases to potential uses by the benchside biologist.

Conclusion:

Current information extraction technologies are approaching performance standards at which concept recognition can begin to deliver high quality data to the benchside biologist. Our system is available as part of the BioCreative Meta-Server project and on the internet http://bionlp.sourceforge.net.

     

Towards a systematic evaluation of protein mutation extraction systems

The development of text analysis systems targeting the extraction of information about mutations from research publications is an emergent topic in biomedical research. Current systems differ in both scope and approach, thus preventing a meaningful comparison of their performance and therefore possible synergies. To overcome this evaluation bottleneck, we developed a comprehensive framework for the systematic analysis of mutation extraction systems, precisely defining tasks and corresponding evaluation metrics, that will allow a comparison of existing and future applications.     

Knowledge Mapping of Opuntia Milpa Alta Since 1998: A Scientometric Analysis

Opuntia Milpa Alta is a cactus cultivated, domesticated, hybridized and selected from the plant Opuntia ficus-indica by Mexican agricultural experts, which can be used as fruit and vegetable. Opuntia Milpa Alta leaves and fruit are superior to wild varieties and suitable for storage and transportation. In 1998, Opuntia Milpa Alta was introduced to China from Mexico by the Quality Product Development Center of the Ministry of Agriculture of China. Up to now, the Opuntia Milpa Alta has been cultivated on a certain scale in China. This study aims to identify the research progress and development trends of Opuntia Milpa Alta in China. Papers published between 1998 to 2019 from two major Chinese academic databases (CNKI and Wangfang) with a topic search related to Opuntia Milpa Alta were collected. The research progress and development trends were analyzed based on CiteSpace software of text mining and visualization. The analysis found that Opuntia Milpa Alta has gone through three obvious research phases after being introduced to China. In the first phase, the researchers paid attention to its cultivation method. Subsequently, researchers began to use extraction methods to extract some of its components, such as polysaccharides and flavonoids. Finally, these extracted ingredients began to be used in some biomedical research.