Browse
Submit Data
Databases
API
Help

Dataset Information

0 Views

0 Connections

0 Citations

0 Reanalyses

0 Downloads

Omics score: 0

E7c35241-4031-4d43-9685-c0ffe4181685 - samples

ABSTRACT: RNA-seq FASTQ files from 296 bulk pre-treatment tumors from IMblaze370

PROVIDER: EGAD00001008553 | EGA |

REPOSITORIES: EGA

ACCESS DATA

Json Xml

Similar Datasets

dc8e1dbd-3db1-470e-ac1a-6b8f5c5b4fee - samples

Project description:RNAseq FASTq files of 181 bulk pre-treatment and 14 post-treatment tumors from GO30140 Ph1b group A and F and 177 bulk pre-treatment tumors of IMbrave150 PhIII

| EGAD00001008128 | EGA

6c38dd8d-d0b9-4118-946a-63e4f4fd6f9c - samples

Project description:RNAseq FASTq files from 728 bulk pre-treatment tumors from IMvigor010.

| EGAD00001007575 | EGA

Transcriptomic profiling of aganglionic and ganglionic colon segments from Hirschsprung disease patients

Project description:This dataset contains bulk RNA sequencing data from paired aganglionic and ganglionic colonic tissue specimens obtained from three pediatric patients diagnosed with Hirschsprung disease (HSCR, OMIM 142623). RNA was extracted and sequenced to investigate transcriptomic alterations and signaling pathway dysregulation associated with HSCR pathogenesis. Raw paired-end FASTQ files generated by Illumina NovaSeq 6000 sequencing are provided for each sample, enabling downstream analyses of differential gene expression between diseased and unaffected intestinal segments.

2025-08-14 | GSE305450 | GEO

6532d6d6-61da-4665-b403-f5fe8aeb7dff - samples

Project description:WES FASTq files of 76 bulk pre-treatment tumors and 76 matched peripheral blood mononuclear cells from GO30140 group A

| EGAD00001008129 | EGA

238c55bf-b1ec-4f2a-b1c7-c35bc4c852c1 - samples

Project description:RNA-seq count matrix for 296 bulk pre-treatment tumors from IMblaze370

| EGAD00001008552 | EGA

Mus musculus

Project description:MOB, Mus musculus fastq-files for RNA-seq

| PRJNA288477 | ENA

Sammut2022 - Multi-omic machine learning model to predict pathological complete response for breast cancer neoadjuvant therapy

Project description: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, and digital pathology profiles from pre-treatment biopsies of breast tumors. Leveraging this comprehensive multi-omic dataset, the team developed ensemble machine learning models using different algorithms (Logistic Regression, SVM and Random Forest). These predictive models identifies patients likely to achieve a pathological complete response (pCR) to therapy, showcasing their potential to enhance treatment selection. Please note that the authors also have an interactive dashboard to apply the fully-integrated NAT response model on new (or any desired) data. The user can find its link in their GitHub repository: https://github.com/micrisor/NAT-ML For more information and clarification, please refer to the ReadMe_NAT-ML document in the files section.

2023-08-28 | BIOMD0000001075 | BioModels

4612f446-5e68-4aa3-b9b3-dc4620ae2004 - samples

Project description:RNAseq fastq files from 611 bulk pre-treatment tumors from two indications: metastatic urothelial bladder cancer patients (IMvigor210) and metastatic renal cell carcinoma (IMmotion150)

| EGAD00001006960 | EGA

LCLF1.0 fastq-Files

Project description:LCLF1.0 fastq-Files

| PRJNA928832 | ENA

LCLF1.0 fastq-Files

Project description:LCLF1.0 fastq-Files

| PRJNA928833 | ENA