Literature DB >> 25199789

MalaCards: A Comprehensive Automatically-Mined Database of Human Diseases.

Noa Rappaport1, Michal Twik, Noam Nativ, Gil Stelzer, Iris Bahir, Tsippi Iny Stein, Marilyn Safran, Doron Lancet.   

Abstract

Systems medicine provides insights into mechanisms of human diseases, and expedites the development of better diagnostics and drugs. To facilitate such strategies, we initiated MalaCards, a compendium of human diseases and their annotations, integrating and often remodeling information from 64 data sources. MalaCards employs, among others, the proven automatic data-mining strategies established in the construction of GeneCards, our widely used compendium of human genes. The development of MalaCards poses many algorithmic challenges, such as disease name unification, integrated classification, gene-disease association, and disease-targeted expression analysis. MalaCards displays a Web card for each of >19,000 human diseases, with 17 sections, including textual summaries, related diseases, related genes, genetic variations and tests, and relevant publications. Also included are a powerful search engine and a variety of categorized disease lists. This unit describes two basic protocols to search and browse MalaCards effectively.
Copyright © 2014 John Wiley & Sons, Inc.

Entities:  

Keywords:  GeneCards; bioinformatics; biological database; disease database; integrated information retrieval; medical database; network medicine; symptoms

Mesh:

Year:  2014        PMID: 25199789     DOI: 10.1002/0471250953.bi0124s47

Source DB:  PubMed          Journal:  Curr Protoc Bioinformatics        ISSN: 1934-3396


  35 in total

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Authors:  Amy Makler; Ramaswamy Narayanan
Journal:  Cancer Genomics Proteomics       Date:  2017 May-Jun       Impact factor: 4.069

3.  Toward a gold standard for benchmarking gene set enrichment analysis.

Authors:  Ludwig Geistlinger; Gergely Csaba; Mara Santarelli; Marcel Ramos; Lucas Schiffer; Nitesh Turaga; Charity Law; Sean Davis; Vincent Carey; Martin Morgan; Ralf Zimmer; Levi Waldron
Journal:  Brief Bioinform       Date:  2021-01-18       Impact factor: 11.622

4.  Biomarker identification of hepatocellular carcinoma using a methodical literature mining strategy.

Authors:  Nai-Wen Chang; Hong-Jie Dai; Yung-Yu Shih; Chi-Yang Wu; Mira Anne C Dela Rosa; Rofeamor P Obena; Yu-Ju Chen; Wen-Lian Hsu; Yen-Jen Oyang
Journal:  Database (Oxford)       Date:  2017-01-01       Impact factor: 3.451

5.  A genetic database can be utilized to identify potential biomarkers for biphenotypic hepatocellular carcinoma-cholangiocarcinoma.

Authors:  Shaffer R S Mok; Sachin Mohan; Navjot Grewal; Adam B Elfant; Thomas A Judge
Journal:  J Gastrointest Oncol       Date:  2016-08

6.  The human disease network in terms of dysfunctional regulatory mechanisms.

Authors:  Jing Yang; Su-Juan Wu; Wen-Tao Dai; Yi-Xue Li; Yuan-Yuan Li
Journal:  Biol Direct       Date:  2015-10-08       Impact factor: 4.540

7.  Deep learning model reveals potential risk genes for ADHD, especially Ephrin receptor gene EPHA5.

Authors:  Lu Liu; Xikang Feng; Haimei Li; Shuai Cheng Li; Qiujin Qian; Yufeng Wang
Journal:  Brief Bioinform       Date:  2021-11-05       Impact factor: 11.622

8.  MicroRNA-derived network analysis of differentially methylated genes in schizophrenia, implicating GABA receptor B1 [GABBR1] and protein kinase B [AKT1].

Authors:  Vadim Gumerov; Hedi Hegyi
Journal:  Biol Direct       Date:  2015-10-08       Impact factor: 4.540

9.  An Integrated Data Driven Approach to Drug Repositioning Using Gene-Disease Associations.

Authors:  Joseph Mullen; Simon J Cockell; Peter Woollard; Anil Wipat
Journal:  PLoS One       Date:  2016-05-19       Impact factor: 3.240

10.  Advancing clinical genomics and precision medicine with GVViZ: FAIR bioinformatics platform for variable gene-disease annotation, visualization, and expression analysis.

Authors:  Zeeshan Ahmed; Eduard Gibert Renart; Saman Zeeshan; XinQi Dong
Journal:  Hum Genomics       Date:  2021-06-26       Impact factor: 4.639

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