生物信息学在昆虫学研究中的应用

随着深度测序和基因芯片技术的不断发展,基因组、转录组、表达谱数据大量积累。目前,至少有10多个昆虫的基因组已被测序,30多个昆虫的转录组数据被报道。显然,传统的生物统计学方法无法处理如此海量的生物数据。量变引发质变,生物数据的大量积累催生了一门新兴学科,生物信息学。生物信息学融合了统计学、信息科学和生物学等各学科的理论和研究内容,在医学、基础生物学、农业科学以及昆虫学等方面获得了广泛的应用。生物信息学的目标是存储数据、管理数据和数据挖掘。因此,建立维护生物学数据库、设计开发基于模式识别、机器学习、数据挖掘等方法的生物软件,以及运用上述工具进行深度的数据挖掘,是生物信息学的重要研究内容。本文首先简要介绍了生物信息学的历史、研究现状及其在昆虫学科中的应用,然后综述了昆虫基因组学和转录组学的研究进展,最后对生物信息学在昆虫学研究中的应用前景进行了展望。     

A re-evaluation of biomedical named entity-term relations

Text mining can support the interpretation of the enormous quantity of textual data produced in biomedical field. Recent developments in biomedical text mining include advances in the reliability of the recognition of named entities (NEs) such as specific genes and proteins, as well as movement toward richer representations of the associations of NEs. We argue that this shift in representation should be accompanied by the adoption of a more detailed model of the relations holding between NEs and other relevant domain terms. As a step toward this goal, we study NE-term relations with the aim of defining a detailed, broadly applicable set of relation types based on accepted domain standard concepts for use in corpus annotation and domain information extraction approaches.     

Automatic individual recognition of wild Crested Ibis based on hybrid method of self-supervised learning and clustering

Automatic individual recognition of wild Crested Ibis is challenged by two factors: the lack of labeled data and an unpredictable number of individuals. In this paper, we propose a hybrid method of self-supervised learning and clustering to automatically recognize wild Crested Ibis based on vocalizations. To address the first challenge, we enhance the Bootstrap Your Own Latent for Audio (BYOL-A) model by using an improved augmentation module and Spatial Group-wise Enhance (SGE) attention module to create the self-supervised learning model BYOL-AIS. This model aims to extract a more discriminative representation of Crested Ibis vocalizations. To handle the second challenge, we introduce a clustering method that combines Uniform Manifold Approximation and Projection (UMAP) and Hierarchical Density-Based Spatial Clustering of Applications with Noise (HDBSCAN) to distinguish the representation of Crested Ibis vocalizations. We evaluate our proposed method using vocalizations collected from 10 Crested Ibis individuals and achieve a recognition accuracy of 0.864. This accuracy is comparable to the performance of commonly used supervised methods. Our results suggest that our proposed method is a feasible method for wild bird recognition in the absence of labeled data and has the potential to be an analytical tool for processing huge amounts of monitoring data.     

Discovering amino acid patterns on binding sites in protein complexes

Discovering amino acid (AA) patterns on protein binding sites has recently become popular. We propose a method to discover the association relationship among AAs on binding sites. Such knowledge of binding sites is very helpful in predicting protein-protein interactions. In this paper, we focus on protein complexes which have protein-protein recognition. The association rule mining technique is used to discover geographically adjacent amino acids on a binding site of a protein complex. When mining, instead of treating all AAs of binding sites as a transaction, we geographically partition AAs of binding sites in a protein complex. AAs in a partition are treated as a transaction. For the partition process, AAs on a binding site are projected from three-dimensional to two-dimensional. And then, assisted with a circular grid, AAs on the binding site are placed into grid cells. A circular grid has ten rings: a central ring, the second ring with 6 sectors, the third ring with 12 sectors, and later rings are added to four sectors in order. As for the radius of each ring, we examined the complexes and found that 10Å is a suitable range, which can be set by the user. After placing these recognition complexes on the circular grid, we obtain mining records (i.e. transactions) from each sector. A sector is regarded as a record. Finally, we use the association rule to mine these records for frequent AA patterns. If the support of an AA pattern is larger than the predetermined minimum support (i.e. threshold), it is called a frequent pattern. With these discovered patterns, we offer the biologists a novel point of view, which will improve the prediction accuracy of protein-protein recognition. In our experiments, we produced the AA patterns by data mining. As a result, we found that arginine (arg) most frequently appears on the binding sites of two proteins in the recognition protein complexes, while cysteine (cys) appears the fewest. In addition, if we discriminate the shape of binding sites between concave and convex further, we discover that patterns {arg, glu, asp} and {arg, ser, asp} on the concave shape of binding sites in a protein more frequently (i.e. higher probability) make contact with {lys} or {arg} on the convex shape of binding sites in another protein. Thus, we can confidently achieve a rate of at least 78%. On the other hand {val, gly, lys} on the convex surface of binding sites in proteins is more frequently in contact with {asp} on the concave site of another protein, and the confidence achieved is over 81%. Applying data mining in biology can reveal more facts that may otherwise be ignored or not easily discovered by the naked eye. Furthermore, we can discover more relationships among AAs on binding sites by appropriately rotating these residues on binding sites from a three-dimension to two-dimension perspective. We designed a circular grid to deposit the data, which total to 463 records consisting of AAs. Then we used the association rules to mine these records for discovering relationships. The proposed method in this paper provides an insight into the characteristics of binding sites for recognition complexes.     

Data Mining for Bioprocess Optimization

Although developed for completely different applications, the great technological potential of data analysis methods called “data mining” has increasingly been realized as a method for efficiently analyzing potentials for optimization and for troubleshooting within many application areas of process, technology. This paper presents the successful application of data mining methods for the optimization of a fermentation process, and discusses diverse characteristics of data mining for biological processes. For the optimization of biological processes a huge amount of possibly relevant process parameters exist. Those input variables can be parameters from devices as well as process control parameters. The main challenge of such optimizations is to robustly identify relevant combinations of parameters among a huge amount of process parameters. For the underlying process we found with the application of data mining methods, that the moment a special carbohydrate component is added has a strong impact on the formation of secondary components. The yield could also be increased by using 2 m³ fermentors instead of 1 m³ fermentors.     

基于数据挖掘的门诊信息资源分析

目的：随着各级医院信息化建设的不断加强,医院的信息化水平也日益提高。目前各医院都有自己完善的信息化系统,在日常的门诊中,信息化系统积累了大量的门诊就诊数据,但长久以来这部分数据只是处于低层次的应用。对数据的深层次分析、加工以及对医院管理层的决策支持能力较弱。面对着这些宝贵的数据,医院迫切需要数据挖掘和分析工具从积累的就诊数据中分析出更深层次的、高价值的信息,从而为医院的管理决策提供高价值的决策信息。方法：以聚类算法进行数据挖掘建模,对某院门诊信息资源中有用字段进行挖掘分析。结果：根据数据挖掘模型进行挖掘分析,对有价值字段进行聚类分析,得到相关字段数据挖掘结果。结论：将得到相关字段数据挖掘结果进行分析,并将所分析的结果在医院管理决策和医疗质量管理等方面的应用进行探讨。     

From Data towards Knowledge: Revealing the Architecture of Signaling Systems by Unifying Knowledge Mining and Data Mining of Systematic Perturbation Data

Genetic and pharmacological perturbation experiments, such as deleting a gene and monitoring gene expression responses, are powerful tools for studying cellular signal transduction pathways. However, it remains a challenge to automatically derive knowledge of a cellular signaling system at a conceptual level from systematic perturbation-response data. In this study, we explored a framework that unifies knowledge mining and data mining towards the goal. The framework consists of the following automated processes: 1) applying an ontology-driven knowledge mining approach to identify functional modules among the genes responding to a perturbation in order to reveal potential signals affected by the perturbation; 2) applying a graph-based data mining approach to search for perturbations that affect a common signal; and 3) revealing the architecture of a signaling system by organizing signaling units into a hierarchy based on their relationships. Applying this framework to a compendium of yeast perturbation-response data, we have successfully recovered many well-known signal transduction pathways; in addition, our analysis has led to many new hypotheses regarding the yeast signal transduction system; finally, our analysis automatically organized perturbed genes as a graph reflecting the architecture of the yeast signaling system. Importantly, this framework transformed molecular findings from a gene level to a conceptual level, which can be readily translated into computable knowledge in the form of rules regarding the yeast signaling system, such as “if genes involved in the MAPK signaling are perturbed, genes involved in pheromone responses will be differentially expressed.”     

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.     

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.     

Using ontologies to describe mouse phenotypes

The mouse is an important model of human genetic disease. Describing phenotypes of mutant mice in a standard, structured manner that will facilitate data mining is a major challenge for bioinformatics. Here we describe a novel, compositional approach to this problem which combines core ontologies from a variety of sources. This produces a framework with greater flexibility, power and economy than previous approaches. We discuss some of the issues this approach raises.     

Mining metabolites: extracting the yeast metabolome from the literature

Text mining methods have added considerably to our capacity to extract biological knowledge from the literature. Recently the field of systems biology has begun to model and simulate metabolic networks, requiring knowledge of the set of molecules involved. While genomics and proteomics technologies are able to supply the macromolecular parts list, the metabolites are less easily assembled. Most metabolites are known and reported through the scientific literature, rather than through large-scale experimental surveys. Thus it is important to recover them from the literature. Here we present a novel tool to automatically identify metabolite names in the literature, and associate structures where possible, to define the reported yeast metabolome. With ten-fold cross validation on a manually annotated corpus, our recognition tool generates an f-score of 78.49 (precision of 83.02) and demonstrates greater suitability in identifying metabolite names than other existing recognition tools for general chemical molecules. The metabolite recognition tool has been applied to the literature covering an important model organism, the yeast Saccharomyces cerevisiae, to define its reported metabolome. By coupling to ChemSpider, a major chemical database, we have identified structures for much of the reported metabolome and, where structure identification fails, been able to suggest extensions to ChemSpider. Our manually annotated gold-standard data on 296 abstracts are available as supplementary materials. Metabolite names and, where appropriate, structures are also available as supplementary materials.     

A Network Biology Approach Identifies Molecular Cross-Talk between Normal Prostate Epithelial and Prostate Carcinoma Cells

The advent of functional genomics has enabled the genome-wide characterization of the molecular state of cells and tissues, virtually at every level of biological organization. The difficulty in organizing and mining this unprecedented amount of information has stimulated the development of computational methods designed to infer the underlying structure of regulatory networks from observational data. These important developments had a profound impact in biological sciences since they triggered the development of a novel data-driven investigative approach. In cancer research, this strategy has been particularly successful. It has contributed to the identification of novel biomarkers, to a better characterization of disease heterogeneity and to a more in depth understanding of cancer pathophysiology. However, so far these approaches have not explicitly addressed the challenge of identifying networks representing the interaction of different cell types in a complex tissue. Since these interactions represent an essential part of the biology of both diseased and healthy tissues, it is of paramount importance that this challenge is addressed. Here we report the definition of a network reverse engineering strategy designed to infer directional signals linking adjacent cell types within a complex tissue. The application of this inference strategy to prostate cancer genome-wide expression profiling data validated the approach and revealed that normal epithelial cells exert an anti-tumour activity on prostate carcinoma cells. Moreover, by using a Bayesian hierarchical model integrating genetics and gene expression data and combining this with survival analysis, we show that the expression of putative cell communication genes related to focal adhesion and secretion is affected by epistatic gene copy number variation and it is predictive of patient survival. Ultimately, this study represents a generalizable approach to the challenge of deciphering cell communication networks in a wide spectrum of biological systems.     

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.     

癌症基因表达谱挖掘中的特征基因选择算法GA/WV

鉴定癌症表达谱的特征基因集合可以促进癌症类型分类的研究,这也可能使病人获得更好的临床诊断?虽然一些方法在基因表达谱分析上取得了成功,但是用基因表达谱数据进行癌症分类研究依然是一个巨大的挑战,其主要原因在于缺少通用而可靠的基因重要性评估方法。GA/WV是一种新的用复杂的生物表达数据评估基因分类重要性的方法,通过联合遗传算法(GA)和加权投票分类算法(WV)得到的特征基因集合不但适用于WV分类器,也适用于其它分类器?将GA/WV方法用癌症基因表达谱数据集的验证,结果表明本方法是一种成功可靠的特征基因选择方法。     

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.

     

Identifying gene-specific variations in biomedical text

The influence of genetic variations on diseases or cellular processes is the main focus of many investigations, and results of biomedical studies are often only accessible through scientific publications. Automatic extraction of this information requires recognition of the gene names and the accompanying allelic variant information. In a previous work, the OSIRIS system for the detection of allelic variation in text based on a query expansion approach was communicated. Challenges associated with this system are the relatively low recall for variation mentions and gene name recognition. To tackle this challenge, we integrate the ProMiner system developed for the recognition and normalization of gene and protein names with a conditional random field (CRF)-based recognition of variation terms in biomedical text. Following the newly developed normalization of variation entities, we can link textual entities to Single Nucleotide Polymorphism database (dbSNP) entries. The performance of this novel approach is evaluated, and improved results in comparison to state-of-the-art systems are reported.     

Bioinformatics tools and databases for analysis of next-generation sequence data

Genome sequencing has been revolutionized by next-generation technologies, which can rapidly produce vast quantities of data at relatively low cost. With data production now no longer being limited, there is a huge challenge to analyse the data flood and interpret biological meaning. Bioinformatics scientists have risen to the challenge and a large number of software tools and databases have been produced and these continue to evolve with this rapidly advancing field. Here, we outline some of the tools and databases commonly used for the analysis of next-generation sequence data with comment on their utility.     

数据挖掘在生物信息学中的应用——文献计量学视角

基因组和蛋白质结构与功能方面已积累了海量数据。如何从海量数据中获取有效信息成为生物信息学迫切要解决的问题。本文以相关主题词检索文献,分析了该领域历年文章数量、发文最多的机构和作者、被引用频次居前论文、期刊载文量,并对关键词和被引用频次居前论文的作者进行共现分析。我们发现,生物信息学中运用数据挖掘方法的文献逐年增多,该领域30.1%的文献发表在十个期刊上,分类、聚类、特征选择和支持向量机等数据挖掘方法使用较多。本研究描绘了生物信息学与数据挖掘这一交叉领域的研究概况,为后续数据挖掘方法与生物信息学研究相结合提供帮助。     

Terminology-driven mining of biomedical literature

MOTIVATION: With an overwhelming amount of textual information in molecular biology and biomedicine, there is a need for effective literature mining techniques that can help biologists to gather and make use of the knowledge encoded in text documents. Although the knowledge is organized around sets of domain-specific terms, few literature mining systems incorporate deep and dynamic terminology processing. RESULTS: In this paper, we present an overview of an integrated framework for terminology-driven mining from biomedical literature. The framework integrates the following components: automatic term recognition, term variation handling, acronym acquisition, automatic discovery of term similarities and term clustering. The term variant recognition is incorporated into terminology recognition process by taking into account orthographical, morphological, syntactic, lexico-semantic and pragmatic term variations. In particular, we address acronyms as a common way of introducing term variants in biomedical papers. Term clustering is based on the automatic discovery of term similarities. We use a hybrid similarity measure, where terms are compared by using both internal and external evidence. The measure combines lexical, syntactical and contextual similarity. Experiments on terminology recognition and clustering performed on a corpus of MEDLINE abstracts recorded the precision of 98 and 71% respectively. AVAILABILITY: software for the terminology management is available upon request.     

Evolutionary and ecological genomics of non-model plants

Dissecting evolutionary dynamics of ecologically important traits is a long-term challenge for biologists.Attempts to understand natural variation and molecular mechanisms have motivated a move from laboratory model systems to non-model systems in diverse natural environments.Next generation sequencing methods,along with an expansion of genomic resources and tools,have fostered new links between diverse disciplines,including molecular biology,evolution,ecology,and genomics.Great progress has been made in a few non-model wild plants,such as Arabidopsis relatives,monkey flowers,and wild sunflowers.Until recently,the lack of comprehensive genomic information has limited evolutionary and ecological studies to larger QTL (quantitative trait locus) regions rather than single gene resolution,and has hindered recognition of general patterns of natural variation and local adaptation.Further efforts in accumulating genomic data and developing bioinformatic and biostatistical tools are now poised to move this field forward.Integrative national and international collaborations and research communities are needed to facilitate development in the field of evolutionary and ecological genomics.