FAIRsharing record for: MalaCards

This FAIRsharing record describes: The MalaCards human disease database (http://www. malacards.org/) is an integrated compendium of annotated diseases mined from 68 data sources. MalaCards has a web card for each of ?20 000 disease entries, in six global categories. It portrays a broad array of annotation topics in 15 sections, including Summaries, Symptoms, Anatomical Context, Drugs, Genetic Tests, Variations and Publications. The Aliases and Classifications section reflects an algorithm for disease name integration across often-conflicting sources, providing effective annotation consolidation. A central feature is a balanced Genes section, with scores reflecting the strength of disease-gene associations. This is accompanied by other gene-related disease information such as pathways, mouse phenotypes and GO-terms, stemming from MalaCards? affiliation with the GeneCards Suite of databases. MalaCards? capacity to inter-link information from complementary sources, along with its elaborate search function, relational database infrastructure and convenient data dumps, allows it to tackle its rich disease annotation landscape, and facilitates systems analyses and genome sequence interpretation. MalaCards adopts a ?flat? disease-card approach, but each card is mapped to popular hierarchical ontologies (e.g. International Classification of Diseases, Human Phenotype Ontology and Unified Medical Language System) and also contains information about multi-level relations among diseases, thereby providing an optimal tool for disease representation and scrutiny.

本FAIRsharing记录介绍的是：MalaCards人类疾病数据库（http://www.malacards.org/）是一套整合汇编的注释化疾病数据库，其数据挖掘自68个不同数据源。该数据库为六大全球分类下的约20000条疾病条目均配备了专属网页卡片，在15个板块中涵盖了广泛的注释主题，包括疾病概述、症状、解剖背景、相关药物、基因检测、变异位点与文献等。别名与分类板块内置了一套可整合不同来源（常存在命名冲突）中疾病名称的算法，可实现注释内容的高效整合。其核心特色之一为均衡化的基因板块，其中的评分可反映疾病与基因关联的强度；该板块还附带了其他与基因相关的疾病信息，例如通路、小鼠表型与基因本体（Gene Ontology, GO）术语，这些数据源自MalaCards与GeneCards数据库套件的关联合作。MalaCards具备将互补数据源中的信息进行互联的能力，搭配其精细的检索功能、关系型数据库架构与便捷的数据导出包，可有效应对其丰富的疾病注释体系，同时助力系统分析与基因组序列解读。MalaCards采用扁平化疾病卡片模式，但每张卡片均映射至主流层级本体，例如国际疾病分类（International Classification of Diseases, ICD）、人类表型本体（Human Phenotype Ontology, HPO）与统一医学语言系统（Unified Medical Language System, UMLS），同时还包含疾病间的多层次关联信息，因此可成为疾病表征与深入研究的理想工具。