Browse
Submit Data
Databases
API
Help

Dataset Information

0 Views

0 Connections

0 Citations

0 Reanalyses

0 Downloads

Omics score: 0

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

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

PROVIDER: EGAD00001007575 | EGA |

REPOSITORIES: EGA

ACCESS DATA

Json Xml

Similar Datasets

289a561c-a53e-41af-a150-43a78fd6ce74 - samples

Project description:TPM matrices of counts from RNAseq data for IMvigor010 from bulk pre-treatment tumors.

| EGAD00001007653 | EGA

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

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

e7c35241-4031-4d43-9685-c0ffe4181685 - samples

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

| EGAD00001008553 | EGA

a0f5c8f5-32cb-481a-86ed-aa64c793dc80 - samples

Project description:RNAseq FASTq files from 817 bulk pre-treatment tumors from three indications (mUC, NSCLC and RCC) across three phase II (IMvigor210, POPLAR, IMmotion150) and a phase I (PCD4989g) clinical trials.

| EGAD00001006631 | EGA

0bfe651d-ef4e-4908-abb5-d2b6944052ac - samples

Project description:RNAseq FASTq files from 82 pre-treatment tumors from PURE01

| EGAD00001008003 | EGA

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

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

LCLF1.0 fastq-Files

Project description:LCLF1.0 fastq-Files

| PRJNA928832 | ENA

LCLF1.0 fastq-Files

Project description:LCLF1.0 fastq-Files

| PRJNA928833 | ENA