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。     

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.     

蛋白质组学技术及其在肺脏疾病研究中的应用

蛋白质组学是旨在研究蛋白质表达谱和蛋白质与蛋白质之间相互作用的新领域,其研究必须依赖高通量、高自动化的技术.简要介绍了蛋白质组分离技术(双向电泳、色谱),蛋白质组分析技术(质谱分析、氨基酸组成分析、蛋白质芯片,Edman降解法测N端序列),蛋白质相互作用技术(酵母双杂交系统、表面等离子共振)以及生物信息学.并从寻找差异表达的蛋白质,寻找用于诊断的疾病相关的标记分子,研究疾病的发病机制三方面介绍了蛋白质组技术在肺脏疾病研究中的应用.     

基于唐诗文本挖掘的关中地区人文景观格局研究

关中地区作为中国历史上最早的“天府之国”,拥有丰富的自然与人文资源和独特的人文景观格局。对近400首描写关中风貌的唐代诗词进行筛选梳理,利用文本挖掘技术,通过可视化分析将关中地区划分为长安故都、行宫别苑、山水形胜、田园意境和雄关遗迹5种景观空间类型,并进一步对人文景观情感特征、景观空间分布特征和空间语义网络特征展开分析,结果表明：不同类型景观空间的词条语义有明显的相似性特征,关中地区形成了以“绿”“素”“青”“朱”为主的景观色彩分布格局,人文景观情感在区域中体现出明显的空间分异。     

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。     

尿蛋白质组学在肾脏疾病研究中的应用

长期以来,肾脏病的治疗进展一直十分缓慢,这是因为目前一些肾脏病在诊断分型上存在很多缺陷,分型通常只能依靠细微的组织病理学差异,这使早期诊断、预后追踪以及疗效观察都十分困难。如果能发现像肌钙蛋白一样特异的生物学标志物,将有助于提高肾脏病的诊疗水平。由于尿液与泌尿系统之间存在着天然的联系,这使得尿液在反映泌尿系统功能方面具备"地理"优势。因此,尿蛋白质组学的兴起和发展为肾脏病及其他泌尿系统疾病的研究开启了一扇新的大门。该文综述了尿蛋白质组学技术的发展及其在各种泌尿系统疾病研究中的应用。     

文本分析技术在蛋白质生物信息学中应用的案例综述

海量数据时代考察文本分析技术在生物信息学领域的应用具有重要的理论和现实价值。本文讨论了文本分析在蛋白质计算分析中的几个应用实例以及核心技术内容。文本分析技术应用于生物信息学领域可发挥引领和导向作用,在生物信息学中的应用又进一步促进了文本分析技术的发展。文本分析技术虽然广泛在生物信息学中应用,但是其发展仍然有需要尚待解决的几个问题,本文对此也进行了讨论。     

microRNA与人类疾病关系研究中的生物信息学方法和资源

microRNA(miRNA)是一类基因调控分子,其成熟体长度大约为22个核苷酸,其在转录后水平通过碱基互补配对的方式特异性结合靶mRNA的3端非翻译区来发挥调控功能,造成靶mRNA的翻译抑制或降解。越来越多的研究表明miRNA具有十分重要的分子功能,几乎在所有的生命过程中均扮演着重要角色。因此,和miRNA有关的功能异常就可能和疾病的发生发展有密切关系。生物信息学旨在为解决生命科学问题提供信息学手段,目前已经有多种用于研究miRNA和人类疾病关系的生物信息学方法和网络资源,本文将综述这一主题的现状,并探讨将来的发展趋势。     

基因芯片技术及其在疾病基因诊断中的应用

基因芯片技术是伴随着人类基因组计划的实施而发展起来的生命科学领域里的前沿生物技术。它最显著的特点是高通量、高集成、微型化、平行化、多样化和自动化。经过短短十几年的发展,基因芯片技术现已在基因表达分析,基因突变及多态性分析,疾病基因诊断,药物及毒物基因组学等多个领域显示出重大的理论意义和实际应用价值,具有广阔的前景。本文专门介绍了基因芯片技术及其在疾病基因诊断上的应用。     

From phenotype to gene: Detecting disease-specific gene functional modules via a text-based human disease phenotype network construction

Currently, some efforts have been devoted to the text analysis of disease phenotype data, and their results indicated that similar disease phenotypes arise from functionally related genes. These related genes work together, as a functional module, to perform a desired cellular function. We constructed a text-based human disease phenotype network and detected 82 disease-specific gene functional modules, each corresponding to a different phenotype cluster, by means of graph-based clustering and mapping from disease phenotype to gene. Since genes in such gene functional modules are functionally related and cause clinically similar diseases, they may share common genetic origin of their associated disease phenotypes. We believe the investigation may facilitate the ultimate understanding of the common pathophysiologic basis of associated diseases.     

A network medicine approach to human disease

High-throughput interaction discovery initiatives are providing thousands of novel protein interactions which are unveiling many unexpected links between apparently unrelated biological processes. In particular, analyses of the first draft human interactomes highlight a strong association between protein network connectivity and disease. Indeed, recent exciting studies have exploited the information contained within protein networks to disclose some of the molecular mechanisms underlying complex pathological processes. These findings suggest that both protein-protein interactions and the networks themselves could emerge as a new class of targetable entities, boosting the quest for novel therapeutic strategies.     

Cutoff variation induces different topological properties: A new discovery of amino acid network within protein

An increasing attention has been dedicated to the characterization of complex networks within the protein world. Before now most investigations about protein structures were only considered where the interactive cutoff distance R_c=5 or 7 Å. It is noteworthy that the length of peptide bond is about 1.5 Å, the length of hydrogen bond is about 3 Å, the range of London-van der Waals force is about 5 Å and the range of hydrophobic effect can reach to 12 Å in protein molecule. Present work reports a study on the topological properties of the amino acid network constructed by different interactions above. The results indicate that the small-world property of amino acid network constructed by the peptide and hydrogen bond, London-van der Waals force and the hydrophobic effect is strong, very strong and relatively weak, respectively. Besides, there exists a precise exponential relation C∝k^−0.5 at R_c=12 Å. It means that the amino acid network constructed by the hydrophobic effect tend to be hierarchical. Functional modules could be the cause for hierarchical modularity architecture in protein structures. This study on amino acid interactive network for different interactions facilitates the identification of binding sites which is strongly linked with protein function, and furthermore provides reasonable understanding of the underlying laws of evolution in genomics and proteomics.     

Predicting disease associations via biological network analysis

Background

Understanding the relationship between diseases based on the underlying biological mechanisms is one of the greatest challenges in modern biology and medicine. Exploring disease-disease associations by using system-level biological data is expected to improve our current knowledge of disease relationships, which may lead to further improvements in disease diagnosis, prognosis and treatment.

Results

We took advantage of diverse biological data including disease-gene associations and a large-scale molecular network to gain novel insights into disease relationships. We analysed and compared four publicly available disease-gene association datasets, then applied three disease similarity measures, namely annotation-based measure, function-based measure and topology-based measure, to estimate the similarity scores between diseases. We systematically evaluated disease associations obtained by these measures against a statistical measure of comorbidity which was derived from a large number of medical patient records. Our results show that the correlation between our similarity measures and comorbidity scores is substantially higher than expected at random, confirming that our similarity measures are able to recover comorbidity associations. We also demonstrated that our predicted disease associations correlated with disease associations generated from genome-wide association studies significantly higher than expected at random. Furthermore, we evaluated our predicted disease associations via mining the literature on PubMed, and presented case studies to demonstrate how these novel disease associations can be used to enhance our current knowledge of disease relationships.

Conclusions

We present three similarity measures for predicting disease associations. The strong correlation between our predictions and known disease associations demonstrates the ability of our measures to provide novel insights into disease relationships.

Electronic supplementary material

The online version of this article (doi:10.1186/1471-2105-15-304) contains supplementary material, which is available to authorized users.     

Amyloid precursor protein interaction network in human testis: sentinel proteins for male reproduction

Background

Amyloid precursor protein (APP) is widely recognized for playing a central role in Alzheimer''s disease pathogenesis. Although APP is expressed in several tissues outside the human central nervous system, the functions of APP and its family members in other tissues are still poorly understood. APP is involved in several biological functions which might be potentially important for male fertility, such as cell adhesion, cell motility, signaling, and apoptosis. Furthermore, APP superfamily members are known to be associated with fertility. Knowledge on the protein networks of APP in human testis and spermatozoa will shed light on the function of APP in the male reproductive system.

Results

We performed a Yeast Two-Hybrid screen and a database search to study the interaction network of APP in human testis and sperm. To gain insights into the role of APP superfamily members in fertility, the study was extended to APP-like protein 2 (APLP2). We analyzed several topological properties of the APP interaction network and the biological and physiological properties of the proteins in the APP interaction network were also specified by gene ontologyand pathways analyses. We classified significant features related to the human male reproduction for the APP interacting proteins and identified modules of proteins with similar functional roles which may show cooperative behavior for male fertility.

Conclusions

The present work provides the first report on the APP interactome in human testis. Our approach allowed the identification of novel interactions and recognition of key APP interacting proteins for male reproduction, particularly in sperm-oocyte interaction.

Electronic supplementary material

The online version of this article (doi:10.1186/s12859-014-0432-9) contains supplementary material, which is available to authorized users.     

GAAD: A Gene and Autoimmiune Disease Association Database

Autoimmune diseases (ADs) arise from an abnormal immune response of the body against substances and tissues normally present in the body. More than a hundred of ADs have been described in the literature so far. Although their etiology remains largely unclear, various types of ADs tend to share more associated genes with other types of ADs than with non-AD types. Here we present GAAD, a gene and AD association database. In GAAD, we collected 44,762 associations between 49 ADs and 4249 genes from public databases and MEDLINE documents. We manually verified the associations to ensure the quality and credibility. We reconstructed and recapitulated the relationships among ADs using their shared genes, which further validated the quality of our data. We also provided a list of significantly co-occurring gene pairs among ADs; with embedded tools, users can query gene co-occurrences and construct customized co-occurrence network with genes of interest. To make GAAD more straightforward to experimental biologists and medical scientists, we extracted additional information describing the associations through text mining, including the putative diagnostic value of the associations, type and position of gene polymorphisms, expression changes of implicated genes, as well as the phenotypical consequences, and grouped the associations accordingly. GAAD is freely available at http://gaad.medgenius.info.     

Peeling the yeast protein network

A set of highly connected proteins (or hubs) plays an important role for the integrity of the protein interaction network of Saccharomyces cerevisae by connecting the network's intrinsic modules. The importance of the hubs' central placement is further confirmed by their propensity to be lethal. However, although highly emphasized, little is known about the topological coherence among the hubs. Applying a core decomposition method which allows us to identify the inherent layer structure of the protein interaction network, we find that the probability of nodes both being essential and evolutionary conserved successively increases toward the innermost cores. While connectivity alone is often not a sufficient criterion to assess a protein's functional, evolutionary and topological relevance, we classify nodes as globally and locally central depending on their appearance in the inner or outer cores. The observation that globally central proteins participate in a substantial number of protein complexes which display an elevated degree of evolutionary conservation allows us to hypothesize that globally central proteins serve as the evolutionary backbone of the proteome. Even though protein interaction data are extensively flawed, we find that our results are very robust against inaccurately determined protein interactions.     

The relationship of DNA base composition and individual protein composition in micro-organisms

Summary To investigate the dependence of protein composition on DNA base composition, a set of data on individual proteins with known amino acid compositions from a spectrum of bacterial species has been compiled. It is found that similar relationships of amino acid frequency to G + C content exist for these proteins as for the bulk proteins studied by Sueoka (1961). The data are analysed by linear and cubic regression, and a measure of the proportions of A + T-rich and G + C-rich codons in the underlying messenger RNAs is put forward. The theoretical limits on the G + C content of coding DNA are discussed, and inference are made about the various selective forces acting on DNAs of different G + C contents.     

Biophysical characterization of the complex between human papillomavirus E6 protein and synapse-associated protein 97

The E6 protein of human papillomavirus (HPV) exhibits complex interaction patterns with several host proteins, and their roles in HPV-mediated oncogenesis have proved challenging to study. Here we use several biophysical techniques to explore the binding of E6 to the three PDZ domains of the tumor suppressor protein synapse-associated protein 97 (SAP97). All of the potential binding sites in SAP97 bind E6 with micromolar affinity. The dissociation rate constants govern the different affinities of HPV16 and HPV18 E6 for SAP97. Unexpectedly, binding is not mutually exclusive, and all three PDZ domains can simultaneously bind E6. Intriguingly, this quaternary complex has the same apparent hydrodynamic volume as the unliganded PDZ region, suggesting that a conformational change occurs in the PDZ region upon binding, a conclusion supported by kinetic experiments. Using NMR, we discovered a new mode of interaction between E6 and PDZ: a subset of residues distal to the canonical binding pocket in the PDZ(2) domain exhibited noncanonical interactions with the E6 protein. This is consistent with a larger proportion of the protein surface defining binding specificity, as compared with that reported previously.