iDASH statistics

Tool stats & trends

Looking to identify usage trends or leading experts?


iDASH specifications


Unique identifier OMICS_03398
Name iDASH
Alternative name integrating Data for Analysis, Anonymization, and SHaring
Software type Package/Module
Interface Graphical user interface
Restrictions to use None
Operating system Unix/Linux
License BSD 2-clause “Simplified” License
Computer skills Medium
Stability Stable
Maintained No


No version available

Publication for integrating Data for Analysis, Anonymization, and SHaring

iDASH citations


Finding relevant biomedical datasets: the UC San Diego solution for the bioCADDIE Retrieval Challenge

PMCID: 5861401
PMID: 29688374
DOI: 10.1093/database/bay017

[…] The experiments were completed on an iDASH () cloud virtual machine with 16 processors (Intel(R) Xeon(R) CPU E7-4870 v2) and 32 GB RAM. Indexing all datasets approximately took 3 hours. PSD-allwords and PSD-keywords each required ∼4 min […]


Simplifying research access to genomics and health data with Library Cards

Sci Data
PMCID: 5851345
PMID: 29537396
DOI: 10.1038/sdata.2018.39

[…] outs). The GA4GH Consent Codes have recently been incorporated into the HL7 Purpose of Use code system for research uses of data. Also, an ontology similar to DUO was developed by UCSD as part of the iDASH project.EGA is building and deploying systems that are actively working towards these goals. EGA is working with ELIXIR ( to use the ELIXIR AAI (https://www.elixir- […]


Medical subdomain classification of clinical notes using a machine learning based natural language processing approach

BMC Med Inform Decis Mak
PMCID: 5709846
PMID: 29191207
DOI: 10.1186/s12911-017-0556-8

[…] rvised shallow learning algorithms to generate medical subdomain classifiers for clinical notes. The baseline classifier used the bag-of-words, term frequency representation, and NB algorithm. In the iDASH dataset, combining the hybrid features of bag-of-words + UMLS concepts restricted to five semantic groups, with tf-idf weighting and linear SVM algorithm yielded the best performing classifier f […]


BLOOM: BLoom filter based oblivious outsourced matchings

BMC Med Genomics
PMCID: 5547447
PMID: 28786361
DOI: 10.1186/s12920-017-0277-y

[…] We compare the runtime and communication complexity of both approaches in Table . Following the evaluation criteria of the iDASH competition, we distinguish the following three phases: i) DB setup (Client) includes all steps required for pre-processing, encryption, and upload of the patient database; ii) Query (Cloud) com […]


Using uncertain data from body worn sensors to gain insight into type 1 diabetes

J Biomed Inform
PMCID: 5077631
PMID: 27580935
DOI: 10.1016/j.jbi.2016.08.022

[…] xtend causal inference methods to better handle uncertainty in observational data; (2) we present a novel set of free-living data from people with diabetes that is available for research use ( We demonstrate the utility of the method through rigorous comparison on simulated data and its successful application to the free-living data. […]


Integration and Visualization of Translational Medicine Data for Better Understanding of Human Diseases

PMCID: 4932659
PMID: 27441714
DOI: 10.1089/big.2015.0057

[…] linical and omics data can be divided into two groups: repositories with an existing infrastructure and solutions requiring deployment. The first group is represented by technologies, such as STRIDE, iDASH, caGRID, and its follow-up, TRIAD, or BDDS Center. Certain platforms of this group focus on a specific disease, such as cBioPortal or G-DOC for cancer or COPD Knowledge Base for pulmonary dysfun […]


Looking to check out a full list of citations?

iDASH institution(s)
Division of Biomedical Informatics, University of California San Diego, La Jolla, CA, USA

iDASH reviews

star_border star_border star_border star_border star_border
star star star star star

Be the first to review iDASH