OmicsDI
Toggle navigation
Browse
Submit Data
Databases
API
Advanced
Search
12
Results
Show all
Save search
Copy query
Show results for
Unknown
(12)
Publication Date
Release Date
2023
(3)
2017
(3)
2016
(2)
2015
(2)
2024
(1)
2009
(1)
Previous
page
1 / 2
You're on page
1
page
2
Next
page
Sort
by:
Relevance
Page size
10
Implementation of Recommendations on the Use of Corticosteroids in Severe COVID-19.
Not available
S-EPMC10751594
|
biostudies-literature
Cite
Gene and pathway level analyses of germline DNA-repair gene variants and prostate cancer susceptibility using the iCOGS-genotyping array.
Not available
S-EPMC5379914
|
biostudies-literature
Cite
Global surveillance of cancer survival 1995-2009: analysis of individual data for 25,676,887 patients from 279 population-based registries in 67 countries (CONCORD-2).
Not available
S-EPMC4588097
|
biostudies-literature
Cite
The histology of ovarian cancer: worldwide distribution and implications for international survival comparisons (CONCORD-2).
Not available
S-EPMC6195192
|
biostudies-literature
Cite
Worldwide comparison of survival from childhood leukaemia for 1995-2009, by subtype, age, and sex (CONCORD-2): a population-based study of individual data for 89 828 children from 198 registries in 53 countries.
Not available
S-EPMC5418564
|
biostudies-literature
Cite
Worldwide comparison of ovarian cancer survival: Histological group and stage at diagnosis (CONCORD-2).
Not available
S-EPMC6195190
|
biostudies-literature
Cite
Identification of seven new prostate cancer susceptibility loci through a genome-wide association study.
Not available
S-EPMC2846760
|
biostudies-literature
Cite
Multiple novel prostate cancer susceptibility signals identified by fine-mapping of known risk loci among Europeans.
Not available
S-EPMC4572072
|
biostudies-literature
Cite
Association of Country Income Level With the Characteristics and Outcomes of Critically Ill Patients Hospitalized With Acute Kidney Injury and COVID-19.
Not available
S-EPMC10219675
|
biostudies-literature
Cite
At-admission prediction of mortality and pulmonary embolism in an international cohort of hospitalised patients with COVID-19 using statistical and machine learning methods.
Not available
S-EPMC11252333
|
biostudies-literature
Cite
Previous
page
1 / 2
You're on page
1
page
2
Next
page
Sort
by:
Relevance
Page size
10
OmicsDI
is part of the ELIXIR infrastructure
OmicsDI is an Elixir interoperability service.
Learn more ›
Tweets