Hairpins in bookstacks: information retrieval from biomedical text

Current advances in high-throughput biology are accompanied by a tremendous increase in the number of related publications. Much biomedical information is reported in the vast amount of literature. The ability to rapidly and effectively survey the literature is necessary for both the design and the interpretation of large-scale experiments, and for curation of structured biomedical knowledge in public databases. Given the millions of published documents, the field of information retrieval, which is concerned with the automatic identification of relevant documents from large text collections, has much to offer. This paper introduces the basics of information retrieval, discusses its applications in biomedicine, and presents traditional and non-traditional ways in which it can be used.     

A text-mining perspective on the requirements for electronically annotated abstracts

We propose that the combination of human expertise and automatic text-mining systems can be used to create a first generation of electronically annotated information (EAI) that can be added to journal abstracts and that is directly related to the information in the corresponding text. The first experiments have concentrated on the annotation of gene/protein names and those of organisms, as these are the best resolved problems. A second generation of systems could then attempt to address the problems of annotating protein interactions and protein/gene functions, a more difficult task for text-mining systems. EAI will permit easier categorization of this information, it will help in the evaluation of papers for their curation in databases, and it will be invaluable for maintaining the links between the information in databases and the facts described in text. Additionally, it will contribute to the efforts towards completing database information and creating collections of annotated text that can be used to train new generations of text-mining systems. The recent introduction of the first meta-server for the annotation of biological text, with the possibility of collecting annotations from available text-mining systems, adds credibility to the technical feasibility of this proposal.     

基于文本挖掘的城市园林绿化信息化管理的需求分析方法

利用信息化的手段辅助园林绿化管理的日常工作能够大幅提高管理效率和管理的精准化水平。准确、充分和前沿的用户需求分析,是建立城市园林绿化信息化管理系统和平台顶层架构、功能组成、模块设计等的基础。但由于信息化建设和园林绿化行业管理在专业上存在偏差,使得信息化的需求分析和行业的预测工作沟通壁垒较高。结合大数据的特点,提出一套充分结合用户使用评价、网络新闻、会议记录等文本型大数据,利用文本挖掘方法进行城市园林绿化信息化管理的需求分析和预测的科学方法。分析了三类文本挖掘得出的高频词特点,结合前期对城市园林绿化信息化行业的实际调研,得出了当前中国园林绿化的管理目标在层级转变,办公自动化、物联网感知等前端管理逐步突显的需求特点,为推动园林绿化行业信息化工作提出了建设方向。     

昆虫学多媒体信息获取与处理方法的初步研究

该文介绍了多媒体信息包括文字信息、音频信息、视频信息和图象信息的获取及其数字化制作技术。较详细地介绍了制作这些媒体资料所需的软硬件环境及具体的操作步骤。这里包括了文字的录入和处理;声音的录制和处理;数字视频的制作,包括昆虫摄像技术、各种来源的视频信息捕获、视频信息的处理等;数字图象的制作,包括昆虫的摄影技术、图象扫描、图象处理等。     

Knowledge Discovery in Biology and Biotechnology Texts: A Review of Techniques,Evaluation Strategies,and Applications

Abstract

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.     

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.     

Which gene did you mean?

Computational Biology needs computer-readable information records. Increasingly, meta-analysed and pre-digested information is being used in the follow up of high throughput experiments and other investigations that yield massive data sets. Semantic enrichment of plain text is crucial for computer aided analysis. In general people will think about semantic tagging as just another form of text mining, and that term has quite a negative connotation in the minds of some biologists who have been disappointed by classical approaches of text mining. Efforts so far have tried to develop tools and technologies that retrospectively extract the correct information from text, which is usually full of ambiguities. Although remarkable results have been obtained in experimental circumstances, the wide spread use of information mining tools is lagging behind earlier expectations. This commentary proposes to make semantic tagging an integral process to electronic publishing.     

美国生物工程食品信息披露制度的新变化及启示 *

In December 2018, the United States Department of Agriculture published the national bioengineered food information disclosure standard (detailed implementation rules), which detailed and improved the specific content of the bioengineered food information disclosure system in the United States, marking the basic establishment of the bioengineered food information disclosure system in the United States. The text mainly includes the following aspects: add the conditions of detectability, unify the exemption conditions of bioengineering food information disclosure, improve the form of bioengineering food information disclosure, set up voluntary labels, and the obligations of the subjects of bioengineering food information disclosure. Through analysis, it is found that under the premise of mandatory labeling, the United States still adheres to the principle of substantive equivalence to ensure the smooth transition of labeling system while protecting consumers’ right to know. In view of the chaos of GM food labeling in China, this paper draws lessons from the experience of the United States and puts forward Suggestions on how to improve GM food labeling system in China in terms of concept, labeling object, management institution and exemption.     

The effect of costly information in diet choice

Summary We distinguish three cases which consider the effect of information on animal behaviour: static information, obligate information and facultative information. Static information deals with the case in which the animal does not acquire additional information; it starts with enough information to discriminate options. Obligate information deals with the case in which the animal acquires information at no additional cost. Facultative information is when the animal may choose to pay a cost in order to acquire information. We illustrate the differences among these three situations by analysing the optimal diet problem subject to the different information regimes. Compared to the case with static information, obligate recognition time narrows the range of prey densities over which an optimal forager feeds selectively, and facultative recognition time reduces it further still. The three models yield qualitatively different predictions regarding how the optimal diet varies with relative abundances of alternative resources. In the space of resource densities, the line separating the optimal behaviours of selectivity and opportunism is straight for both the perfect and obligate information cases. In the case of facultative recognition time this line or isoleg is part of a quadratic curve. This non-linearity yields two completely new predictions: a less profitable resource may be lost from the diet after becoming more abundant and the poor resource may be included in the diet as a result of the rich resource becoming more common.     

A web service for biomedical term look-up

Recent years have seen a huge increase in the amount of biomedical information that is available in electronic format. Consequently, for biomedical researchers wishing to relate their experimental results to relevant data lurking somewhere within this expanding universe of on-line information, the ability to access and navigate biomedical information sources in an efficient manner has become increasingly important. Natural language and text processing techniques can facilitate this task by making the information contained in textual resources such as MEDLINE more readily accessible and amenable to computational processing. Names of biological entities such as genes and proteins provide critical links between different biomedical information sources and researchers' experimental data. Therefore, automatic identification and classification of these terms in text is an essential capability of any natural language processing system aimed at managing the wealth of biomedical information that is available electronically. To support term recognition in the biomedical domain, we have developed Termino, a large-scale terminological resource for text processing applications, which has two main components: first, a database into which very large numbers of terms can be loaded from resources such as UMLS, and stored together with various kinds of relevant information; second, a finite state recognizer, for fast and efficient identification and mark-up of terms within text. Since many biomedical applications require this functionality, we have made Termino available to the community as a web service, which allows for its integration into larger applications as a remotely located component, accessed through a standardized interface over the web.