Inroads to Predict in Vivo Toxicology-An Introduction to the eTOX Project

There is a widespread awareness that the wealth of preclinical toxicity data that the pharmaceutical industry has generated in recent decades is not exploited as efficiently as it could be. Enhanced data availability for compound comparison ("read-across"), or for data mining to build predictive tools, should lead to a more efficient drug development process and contribute to the reduction of animal use (3Rs principle). In order to achieve these goals, a consortium approach, grouping numbers of relevant partners, is required. The eTOX ("electronic toxicity") consortium represents such a project and is a public-private partnership within the framework of the European Innovative Medicines Initiative (IMI). The project aims at the development of in silico prediction systems for organ and in vivo toxicity. The backbone of the project will be a database consisting of preclinical toxicity data for drug compounds or candidates extracted from previously unpublished, legacy reports from thirteen European and European operation-based pharmaceutical companies. The database will be enhanced by incorporation of publically available, high quality toxicology data. Seven academic institutes and five small-to-medium size enterprises (SMEs) contribute with their expertise in data gathering, database curation, data mining, chemoinformatics and predictive systems development. The outcome of the project will be a predictive system contributing to early potential hazard identification and risk assessment during the drug development process. The concept and strategy of the eTOX project is described here, together with current achievements and future deliverables.     

基于知识本体的过程安全分析信息标准化

过程危险分析的主要目标是识别危险剧情。危险剧情能够表达团队“头脑风暴”安全评价过程也能表达评价结论。危险剧情的知识本体是标准化过程安全分析信息的准确描述。知识本体是概念表达的明确规范。依据设计知识本体所遵循的规则提出了一种过程安全分析信息标准化方法,称为剧情对象模型（scenario object model,SOM）。SOM能够表达安全分析信息的内容和结构,能够实施计算机自动推理和半定量计算。应用知识本体SOM有效实现了计算机辅助自动安全评价和安全信息的传递、复查和共享。     

轴承锈蚀领域知识的本体描述

以本体为核心来构造知识系统，则可达到知识共享与方法重用等目的。本文在分析领域本体概念基础上，对轴承锈蚀领域知识进行描述。首先描述了轴承锈蚀领域本体库的建立过程，并进一步从类，属性，分面，关系等方面更加深入地分析轴承锈蚀领域的概念与关系；最后采用Protese，对轴承锈蚀领域本体进行计算机实现。本文为轴承锈蚀分析与诊断系统开发奠定基础。     

基于本体元模型的锻造知识管理系统建模与工艺推理

知识管理系统的首要问题是知识建模,知识模型直接影响着整个系统知识的获取、共享、重用和推理。文章运用领域本体思想对锻造知识进行分类、总结,提出一种基于本体元模型驱动的锻造知识管理模型,初步构建了锻造知识领域本体,采用框架概念图表示锻造知识。文章还结合锻造工艺知识,给出了本体元模型驱动和概念图框架知识表示模型在锻造知识管理中的具体应用,并提出一种适合于本体元模型的推理方法,并将该方法用于锻造工艺知识推理。本体元模型驱动的知识库构建方法在组织锻造知识时,具有构建速度快、良好的互操作和知识共享等优点。     

基于BBS的知识获取方法及其在质量管理中的应用

提出在BBS环境下综合运用语义网络、本体论和专家评分等技术来获取和整理员工中的质量管理知识的构架，包括借助于语义模型指导BBS版面设置，采用九元组描述语义对象的内部结构和外部联系，通过结构映射建立语义对象之间的关系等，从而为散乱信息的收集、知识整理、知识的结构化与同构化、知识共享等提供新的思路。     

A Survey of Autoencoder Algorithms to Pave the Diagnosis of Rare Diseases

Rare diseases (RDs) concern a broad range of disorders and can result from various origins. For a long time, the scientific community was unaware of RDs. Impressive progress has already been made for certain RDs; however, due to the lack of sufficient knowledge, many patients are not diagnosed. Nowadays, the advances in high-throughput sequencing technologies such as whole genome sequencing, single-cell and others, have boosted the understanding of RDs. To extract biological meaning using the data generated by these methods, different analysis techniques have been proposed, including machine learning algorithms. These methods have recently proven to be valuable in the medical field. Among such approaches, unsupervised learning methods via neural networks including autoencoders (AEs) or variational autoencoders (VAEs) have shown promising performances with applications on various type of data and in different contexts, from cancer to healthy patient tissues. In this review, we discuss how AEs and VAEs have been used in biomedical settings. Specifically, we discuss their current applications and the improvements achieved in diagnostic and survival of patients. We focus on the applications in the field of RDs, and we discuss how the employment of AEs and VAEs would enhance RD understanding and diagnosis.     

个体化用药基因电子身份应用程序的设计与实现

目的：为跨过药物基因组学基础研究至临床便捷应用的鸿沟,作者提出了基因电子身份（形象称之为“基因脸谱”）概念,并基于此开发了一款集“基因-药物”信息解读、用药指导等于一体的个体化用药基因电子身份应用程序（即“基因脸谱”）。方法：“基因脸谱”以自主开发的“精准用药知识库”为数据基础,采用Java平台上的开源应用框架Spring Boot及Uni-APP跨平台技术构建APP,该知识库内容取自目前证据级别高的药品说明书、指南或重要文献等。结果：“基因脸谱”APP具有基因检测预约、基因知识介绍、个体化用药建议、用药记录、基因脸谱解读和精准用药知识库六大功能。个人通过一次检测300多个基因位点的基因型,结果导入形成个人用药基因“身份证”。用药时,通过“基因脸谱”扫描或输入药物名称,自动给出针对个人的用药建议,包括：可能出现的药物不良反应风险、可能疗效降低甚至无效的风险、可能需要调整剂量等,为安全用药增加一道基因防线;通过“基因脸谱”还可获取药物基因组学知识和药品基本信息。结论：基因脸谱是一款可快速实现药物基因组学到临床应用、可提高用药安全度的数字疗法产品,将开启促进个体化医学加速落地临床的新模式。     

基于LBS的个性化手机菜品推荐系统设计与实现

为满足用户"随身、随时、随地"的个性化服务需求,设计一款基于位置服务(LBS)的个性化菜品推荐系统。在系统中,首先通过对用户的位置感知,记录每个用户的用餐信息;然后通过现有的选择过程或相似关系来获知用户的潜在兴趣;再对当地餐馆和菜肴的信息进行过滤,将用户可能感兴趣的餐厅和菜品推荐给他们。     

一种基于用户投资偏好与产品特征的P2P网贷个性化推荐算法

互联网金融的蓬勃发展,在将普惠金融的实惠带给投资者的同时,也因理财产品的多样化,给投资者带来了挑选适合自己的网贷产品的困扰。鉴于此,系统结合P2P网贷平台投资者的个人投资偏好和平台产品的特征,设计了一种基于用户账户持续时间、活跃度及投资偏好的P2P网贷个性化推荐算法,以节约投资者决策时间,加快平台资金筹集进度。     

Genetic Dissection of Phosphorus Use Efficiency in a Maize Association Population under Two P Levels in the Field

Phosphorus (P) deficiency is an important challenge the world faces while having to increase crop yields. It is therefore necessary to select maize (Zea may L.) genotypes with high phosphorus use efficiency (PUE). Here, we extensively analyzed the biomass, grain yield, and PUE-related traits of 359 maize inbred lines grown under both low-P and normal-P conditions. A significant decrease in grain yield per plant and biomass, an increase in PUE under low-P condition, as well as significant correlations between the two treatments were observed. In a genome-wide association study, 49, 53, and 48 candidate genes were identified for eleven traits under low-P, normal-P conditions, and in low-P tolerance index (phenotype under low-P divided by phenotype under normal-P condition) datasets, respectively. Several gene ontology pathways were enriched for the genes identified under low-P condition. In addition, seven key genes related to phosphate transporter or stress response were molecularly characterized. Further analyses uncovered the favorable haplotype for several core genes, which is less prevalent in modern lines but often enriched in a specific subpopulation. Collectively, our research provides progress in the genetic dissection and molecular characterization of PUE in maize.