Integrative database management for mouse development: systems and concepts

Cells in the developing embryo must integrate complex signals from the genome and environment to make decisions about their behavior or fate. The ability to understand the fundamental biology of the decision-making process, and how these decisions may go awry during abnormal development, requires a systems biology paradigm. Presently, the ability to build models with predictive capability in birth defects research is constrained by an incomplete understanding of the fundamental parameters underlying embryonic susceptibility, sensitivity, and vulnerability. Key developmental milestones must be parameterized in terms of system structure and dynamics, the relevant control methods, and the overall design logic of metabolic and regulatory networks. High-content data from genome-based studies provide some comprehensive coverage of these operational processes but a key research challenge is data integration. Analysis can be facilitated by data management resources and software to reveal the structure and function of bionetwork motifs potentially associated with an altered developmental phenotype. Borrowing from applied mathematics and artificial intelligence, we conceptualize a system that can help address the new challenges posed by the transformation of birth defects research into a data-driven science.     

基于精神影像和人工智能的抑郁症客观生物学标志物研究进展

抑郁症是当今社会上造成首要危害且病因和病理机制最为复杂的精神疾病之一,寻找抑郁症的客观生物学标志物一直是精神医学研究和临床实践的重点和难点,而结合人工智能技术的磁共振影像(magnetic resonance imaging,MRI)技术被认为是目前抑郁症等精神疾病中最有可能率先取得突破进展的客观生物学标志物.然而,当前基于精神影像学的潜在抑郁症客观生物学标志物还未得到一致结论 .本文从精神影像学和以机器学习(machine learning,ML)与深度学习(deep learning, DL)等为代表的人工智能技术相结合的角度,首次从疾病诊断、预防和治疗等三大临床实践环节对抑郁症辅助诊疗的相关研究进行归纳分析,我们发现:a.具有诊断价值的脑区主要集中在楔前叶、扣带回、顶下缘角回、脑岛、丘脑以及海马等;b.具有预防价值的脑区主要集中在楔前叶、中央后回、背外侧前额叶、眶额叶、颞中回等;c.具有预测治疗反应价值的脑区主要集中在楔前叶、扣带回、顶下缘角回、额中回、枕中回、枕下回、舌回等.未来的研究可以通过多中心协作和数据变换提高样本量,同时将多元化的非影像学数据应用于数据挖掘,这将有利于提高人工智能模型的辅助分类能力,为探寻抑郁症的精神影像学客观生物学标志物及其临床应用提供科学证据和参考依据.     

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.     

From Data to Decisions: Helping Crop Producers Build Their Actionable Knowledge

Recent advances in distributed information technologies are providing the means to capture and process abundant data, and to reveal associations between variables describing the crop-environment-management interaction. This review describes the determinants and moderating factors influencing how much value a crop producer and his or her advisor can derive from data, and information derived from data. We describe the social, technological, and entrepreneurial processes needed to progress the nonlinear pathway from data to an on-farm decision, and explore the meaning of actionable knowledge; that is, knowledge that can be acted upon and applied to solve a real-world problem. We argue that effective decision support is also a system that supports the learning needs of crop producers and their transactions with trusted advisors. Crop protection, the sub-set of crop management used to mitigate crop loss, is used to illustrate current approaches and technologies to support farmers' decisions. We describe how situational awareness and actionable knowledge could be improved through use of emerging platform technologies, advances in artificial intelligence, consideration of farmer decision style, knowledge capture and maintenance, and embedding technology in human-centered services. Implications for the conduct of research and development are discussed.     

BioWeka--extending the Weka framework for bioinformatics

Given the growing amount of biological data, data mining methods have become an integral part of bioinformatics research. Unfortunately, standard data mining tools are often not sufficiently equipped for handling raw data such as e.g. amino acid sequences. One popular and freely available framework that contains many well-known data mining algorithms is the Waikato Environment for Knowledge Analysis (Weka). In the BioWeka project, we introduce various input formats for bioinformatics data and bioinformatics methods like alignments to Weka. This allows users to easily combine them with Weka's classification, clustering, validation and visualization facilities on a single platform and therefore reduces the overhead of converting data between different data formats as well as the need to write custom evaluation procedures that can deal with many different programs. We encourage users to participate in this project by adding their own components and data formats to BioWeka. Availability: The software, documentation and tutorial are available at http://www.bioweka.org.     

Using multiple ontologies to integrate complex biological data

The strength of the rat as a model organism lies in its utility in pharmacology, biochemistry and physiology research. Data resulting from such studies is difficult to represent in databases and the creation of user-friendly data mining tools has proved difficult. The Rat Genome Database has developed a comprehensive ontology-based data structure and annotation system to integrate physiological data along with environmental and experimental factors, as well as genetic and genomic information. RGD uses multiple ontologies to integrate complex biological information from the molecular level to the whole organism, and to develop data mining and presentation tools. This approach allows RGD to indicate not only the phenotypes seen in a strain but also the specific values under each diet and atmospheric condition, as well as gender differences. Harnessing the power of ontologies in this way allows the user to gather and filter data in a customized fashion, so that a researcher can retrieve all phenotype readings for which a high hypoxia is a factor. Utilizing the same data structure for expression data, pathways and biological processes, RGD will provide a comprehensive research platform which allows users to investigate the conditions under which biological processes are altered and to elucidate the mechanisms of disease.     

Data Mining Strategies to Improve Multiplex Microbead Immunoassay Tolerance in a Mouse Model of Infectious Diseases

Multiplex methodologies, especially those with high-throughput capabilities generate large volumes of data. Accumulation of such data (e.g., genomics, proteomics, metabolomics etc.) is fast becoming more common and thus requires the development and implementation of effective data mining strategies designed for biological and clinical applications. Multiplex microbead immunoassay (MMIA), on xMAP or MagPix platform (Luminex), which is amenable to automation, offers a major advantage over conventional methods such as Western blot or ELISA, for increasing the efficiencies in serodiagnosis of infectious diseases. MMIA allows detection of antibodies and/or antigens efficiently for a wide range of infectious agents simultaneously in host blood samples, in one reaction vessel. In the process, MMIA generates large volumes of data. In this report we demonstrate the application of data mining tools on how the inherent large volume data can improve the assay tolerance (measured in terms of sensitivity and specificity) by analysis of experimental data accumulated over a span of two years. The combination of prior knowledge with machine learning tools provides an efficient approach to improve the diagnostic power of the assay in a continuous basis. Furthermore, this study provides an in-depth knowledge base to study pathological trends of infectious agents in mouse colonies on a multivariate scale. Data mining techniques using serodetection of infections in mice, developed in this study, can be used as a general model for more complex applications in epidemiology and clinical translational research.     

基于云计算的农作物种质资源数据挖掘平台研究

针对作物种质数据量大、多维带来的挖掘效率偏低的问题,通过探讨云计算技术及其解决方案,提出了一种基于Hadoop的农作物种质资源数据挖掘平台,详述了平台的各功能模块并给出了具体的开发方案。通过改进经典的Apriori算法并在平台上对其进行效率测试,验证了平台的有效性、可行性。     

Integrated Genomic and Network-Based Analyses of Complex Diseases and Human Disease Network

A disease phenotype generally reflects various pathobiological processes that interact in a complex network. The highly interconnected nature of the human protein interaction network(interactome) indicates that, at the molecular level, it is difficult to consider diseases as being independent of one another. Recently, genome-wide molecular measurements, data mining and bioinformatics approaches have provided the means to explore human diseases from a molecular basis. The exploration of diseases and a system of disease relationships based on the integration of genome-wide molecular data with the human interactome could offer a powerful perspective for understanding the molecular architecture of diseases. Recently, subnetwork markers have proven to be more robust and reliable than individual biomarker genes selected based on gene expression profiles alone, and achieve higher accuracy in disease classification. We have applied one of these methodologies to idiopathic dilated cardiomyopathy(IDCM) data that we have generated using a microarray and identified significant subnetworks associated with the disease. In this paper, we review the recent endeavours in this direction, and summarize the existing methodologies and computational tools for network-based analysis of complex diseases and molecular relationships among apparently different disorders and human disease network. We also discuss the future research trends and topics of this promising field.     

基于能值分析的煤炭矿区复合生态系统评价

煤炭矿区复合生态系统评价是对其进行有效管理及调控的手段之一。研究了煤炭矿区复合生态系统内涵,剖析了煤炭矿区复合生态系统的典型特征,论述了运用能值分析方法评价复合生态系统的特殊性;进而,构建了煤炭矿区复合生态系统及其子系统的能值评价指标。选择山东省龙口矿区北皂煤矿为研究对象,进行了实证研究。应用实际数据,测算了2006—2012年复合生态系统及子系统各个能值评价指标。研究结果表明,能值理论与能值分析是评价煤炭矿区复合生态系统的有效方法,各项指标变化反映了复合生态系统的演进趋势;不可再生煤炭资源的过度消耗、可再资源利用率低是这一系统的典型特征;能值评价指标的测算结果表征环境子系统负荷较小。研究结果可为实施有效的煤炭矿区复合生态系统管理提供智力支持和决策参考依据。     

基于PC/Linux的核酸序列分析系统的构建及其应用

基于PC机和Linux操作系统, 利用Phred/Phrap/Consed软件和Blast软件, 构建了核酸序列大规模自动分析系统. 该套系统可自动完成从测序峰图向核酸序列的转化、载体序列去除、序列自动拼接、重复序列鉴定以及序列的相似性分析, 可加速对大规模测序数据的分析和利用.     

Advancing post-genome data and system integration through machine learning

Research on biological data integration has traditionally focused on the development of systems for the maintenance and interconnection of databases. In the next few years, public and private biotechnology organisations will expand their actions to promote the creation of a post-genome semantic web. It has commonly been accepted that artificial intelligence and data mining techniques may support the interpretation of huge amounts of integrated data. But at the same time, these research disciplines are contributing to the creation of content markup languages and sophisticated programs able to exploit the constraints and preferences of user domains. This paper discusses a number of issues on intelligent systems for the integration of bioinformatic resources.     

Bioinformatics applied to allergy: allergen databases, from collecting sequence information to data integration. The Allergome platform as a model

Allergens are proteins or glycoproteins that are recognized by IgE produced by the immune system of allergic individuals. Until now around 1,500 allergenic structures have been identified and this number seems not have reached a plateau after 3-4 decades of research and the advent of molecular biology. Several allergen databases are available on Internet. Different aims and philosophies lead to different products. Here we report about main feature of web sites dedicated to allergens and we describe in more details our current work on the Allergome platform. The web server Allergome (www.allergome.org) represent a free independent open resource whose goal is to provide an exhaustive repository of data related to all the IgE-binding compounds. The main purpose of Allergome is to collect a list of allergenic sources and molecules by using the widest selection criteria and sources. A further development of the Allergome platform has been represented by the Real Time Monitoring of IgE sensitization module (ReTiME) that allows uploading of raw data from both in vivo and in vitro testing, thus representing the first attempt to have IT applied to allergy data mining. More recently, a new module (RefArray) representing a tool for literature mining has been released.     

社会经济代谢研究:内涵、发现和展望

社会经济代谢是指由于人类活动导致的物质和能量在社会经济系统内部和边界上的迁移和转换。社会经济代谢研究已经成为产业生态学的核心领域。本文结合典型案例介绍了社会经济代谢研究的内涵和基本步骤,总结了社会经济代谢研究的主要发现:提供了追踪物质在社会经济系统的来源、去向和流动路径的方法与模型;揭示了物质材料及其社会经济代谢过程对现代化生产和生活方式的支撑作用;阐明了人类活动驱动与物质代谢相关的生态环境问题的机制;提供了评估资源利用效率、资源供需趋势和城市矿产开发潜力的模型与基础数据。最后指出了社会经济代谢研究的发展方向:增加研究对象并深化对每种对象的研究精度;拓展或细化研究系统的时空边界;引入并融合新的数据来源和研究方法;将社会经济代谢中的物质流对接到与其相关的生态环境影响;建立可共享、可拓展、可累积的数据平台。     

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.     

三江源红外相机社区监测平台概述

三江源红外相机社区监测平台以当地牧民为监测工作的主体, 开展该地区的生物多样性监测、野生动物生态学和行为学研究, 以及基于社区的自然资源管理与保护成效评估。三江源红外相机社区监测平台于2013年10月由北京大学自然保护与社会发展研究中心与山水自然保护中心联合三江源当地社区共同建立与管理。截至2019年6月, 该平台共有有效监测样区9个, 监测覆盖面积7,000多平方公里, 覆盖三江源区域的玉树州全境五县一市和果洛州班玛县, 培养了社区监测队员264名。已处理照片总数252.43万张, 动物独立探测总数12万次, 共识别出30种野生兽类和37种野生鸟类。该平台在调查野生动物多样性本底, 研究雪豹(Panthera uncia)种群密度与动态、雪豹与同域分布的其他食肉动物的关系, 总结社区监测的管理经验等方面取得部分成果。平台未来的工作重点包括总结与发表平台的研究结果、构建云端数据库实现红外相机照片数据的共享与公众参与、打造可互动数据库管理平台和相应监测队员手持客户端以及人工智能辅助下的物种与个体识别。     

Intelligence, Coalitional Killing, and the Antecedents of War

Advances in primatological research have recently led to a hypothesis that lethal coalitionary raiding in chimpanzees is the product of an evolutionarily adaptive "dominance drive" that disposes adult males to seek out low-cost opportunities for conspecific killing. This conclusion has been extended into a claim that human warfare and other forms of coalitional killing are outcomes of a hardwired, "demonic male" complex. Reversing this evidential approach, I argue from data on conspecific killing in humans that humans and chimpanzees have an aversion to killing conspecifics. Their lethal violence, I propose, is more parsimoniously explained as the result of a developed intelligence capable of envisioning the future and, when necessary, of disabling this aversion to achieve desired goals.     

基于PC/Linux的核酸序列电子延伸系统的构建及其应用

新基因全长cDNA序列的获得常常是分子生物学工作者面临的难题。人类基因组计划及其相关计划的实施导致了大量表达序列标签(EST)的产生。利用一定的生物信息学算法,这些EST序列往往可用来对新基因片段进行延伸。采用Linux操作系统,利用Blast软件和Phrap软件以及EST数据库在微机上构建了EST序列的电子延伸系统,并对来自于人胎肝的11386条EST序列和511条插入片段全长cDNA序列进行了电子延伸,结果显示8373条EST序列和389条插入片段全长cDNA序列得到了程度不等的延伸,部分结果通过RACE实验得到证实。该套系统可高效地、规模化进行EST序列的延伸,可为通过实验获得新基因全长cDNA序列提供重要线索。 Abstract:Normally it is difficult to obtain full-length cDNA sequence of novel genes.More and more expressed sequence tags(ESTs) have been obtained since the start-up of human genome project.Powerful system is badly needed for data mining on these EST sequences.Based on a personal computer coupled with Linux operating system and EST database,the Blast software and Phrap software were used to construct a platform for in silico elongation of ESTs in our lab.The performance was tested using 11386 EST sequences and 511 partial-length cDNA sequences.Results demonstrated that 8373 EST and 389 cDNA sequence were elongated using this system.Thus the platform seems to be a fast way for full-length cDNA sequence cloning of new genes.