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UnknownSirtuin 5 is required for mouse survival in response to cardiac pressure overload.
Not available
S-EPMC5712617 | biostudies-literature
TranscriptomicsMicroarray analysis of genes impacted by 5 day knockdown of SLC24A5 in normal human melanocytes.
A major development in pigmentation research was the discovery of the involvement of SLC24A5 in natural variation in skin colour. This study used Agilent whole-genome microarrays to examine the impact of SLC24A5 knockdown on the transcriptome of normal human melanocytes derived from lightly and dark...
ORGANISM(S): Homo sapiens 
2012-04-25 | E-GEOD-21565 | biostudies-arrayexpress
UnknownA first-in-human study of the anti-LAG-3 antibody favezelimab plus pembrolizumab in previously treated, advanced microsatellite stable colorectal cancer.
Not available
S-EPMC9832734 | biostudies-literature
Genomicsena-DATASET-CRUK-CI-16-05-2016-12:25:56:672-139 - samples
Targeted sequencing of 173 genes in 2433 primary breast tumours. Data includes 2433 tumour samples, 523 adjacent normal (breast) samples and 127 blood samples. Libraries were prepared with Illumina's Nextera custom enrichment kit targetting all the exons of the most frequently mutated breast cance...
EGAD00001002115 | EGA
GenomicsMETABRIC: Data from Pereira et al (2016), The somatic mutation profiles of 2433 breast cancers refine their genomic and transcriptomic landscapes. Nat Comms 7.
The genomic landscape of breast cancer is complex, and inter- and intra-tumour heterogeneity are important challenges in treating the disease. In this study, we sequence 173 genes in 2433 primary breast tumours that have copy number aberration (CNA), gene expression, and long-term clinical follow-u...
EGAS00001001753 | EGA
ModelsSammut2022 - Multi-omic machine learning model to predict pathological complete response for breast cancer neoadjuvant therapy
In this publication, researchers investigated the intricate relationship between breast cancers and their microenvironment, specifically focusing on predicting treatment responses using multi-omic machine learning model. They collected diverse data types including clinical, genomic, transcriptomic, ...
2023-08-28 | BIOMD0000001075 | BioModels
Machine Learning Model FAIR-AIML 
UnknownSubsequent neoplasms and late mortality in children undergoing allogeneic transplantation for nonmalignant diseases.
Not available
S-EPMC7218429 | biostudies-literature
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