Browse
Submit Data
Databases
API
Help

Dataset Information

0 Views

0 Connections

0 Citations

0 Reanalyses

0 Downloads

Omics score: 0

A Model for Predicting the Efficacy of Conversion Therapy for Patients With Colorectal Cancer Liver Metastases by the Detection of 5-hydroxymethylcytosine (5-hmC) in Plasma Cell-free DNA

ABSTRACT: In this study, the investigators establish a model for predicting the efficacy of conversion therapy for the patients with unresectable liver metastases of colorectal cancer based on the detection of 5-hydroxymethylcytosine (5-hmC) in training group and validate the effectiveness of the model using a validation group.

DISEASE(S): Neoplasm Metastasis,Colorectal Cancer,Colorectal Neoplasms,Colorectal Cancer With Liver Metastases,Liver Neoplasms

PROVIDER: 2284746 | ecrin-mdr-crc |

REPOSITORIES: ECRIN MDR

ACCESS DATA

Json Xml

Similar Datasets

A Model for the Early Diagnosis of Colorectal Cancer by the Detection of 5-hydroxymethylcytosine (5-hmC) in Plasma Cell-free DNA

Project description:In this study, the investigators establish a model for the early diagnosis of colorectal cancer based on detection of 5-hydroxymethylcytosine (5-hmC) in training group and validate the effectiveness of the model using a validation group.

| 2278673 | ecrin-mdr-crc

A Multi-center Trial to Establish a Model for the Early Diagnosis of Colorectal Cancer by the Detection of 5-hydroxymethylcytosine (5-hmC) in Plasma Cell-free DNA

Project description:In this study, the investigators establish a model for the early diagnosis of colorectal cancer based on the detection of 5-hydroxymethylcytosine (5-hmC) in training group and validate the effectiveness of the model using a validation group recruited from multiple centers.

| 2284518 | ecrin-mdr-crc

A long context RNA foundation model for predicting transcriptome architecture

Project description:A long context RNA foundation model for predicting transcriptome architecture

| PRJNA1176011 | ENA

Predicting Neuroblastoma Using Developmental Signals and a Logic-Based Model

Project description:Predicting Neuroblastoma Using Developmental Signals and a Logic-Based Model

| PRJNA473995 | ENA

Computational frameworks for predicting protein interactions via single-cell proximity sequencing

Project description:Proximity sequencing (Prox-seq) is a method that can simultaneously measure gene expression, protein abundance and protein complex information in single cells. By making use of proximity information from proteins, Prox-seq can infer surface protein complexes at the single-cell level. Previously, we proposed a statistical method for predicting protein complex abundance in single cells from Prox-seq data. However, it is difficult to experimentally validate the prediction results. Here, we first introduces a physical model which enables simulation of Prox-seq data. The simulated data is then used to validate the statistical method that we proposed previously, and to understand how protein abundance can influence protein complex prediction. Second, we propose a modification to Prox-seq, and use the new measurements from the modification to develop a second, independent method for predicting protein complexes. We demonstrate that on experimental data, the two methods generally agree with each other, with the second method generally producing more accurate protein complex abundance results. Thus, we present a simple way to investigate the behavior of Prox-seq data under various biological conditions, and independent methods for predicting and quantifying protein complexes.

2022-02-07 | GSE196130 | GEO

A five-microRNA-signature predicting recurrence in HNSCC

Project description:The risk of locoregional or distant failure in advanced HPV-negative head and neck squamous cell carcinoma (HNSCC) patients is high. However, no suitable markers for stratification are clinically available. Thus, we aimed to identify a microRNA(miRNA)-signature predicting disease recurrence. For this purpose the miRNA profiles from 162 HNSCC samples were analysed with regard to identification of a low-complex porgnostic signature. The data set consists of a discovery dataset (n=85) and a validation dataset (n=77). The study resulted in a prognostic 5-miRNA signature significantly predicting the relevant clinical endpoint freedom from recurrence.

2018-09-06 | E-MTAB-5793 | biostudies-arrayexpress

A model for predicting response to PD-1 inhibitors in NSCLC

Project description:A model for predicting response to PD-1 inhibitors in NSCLC

| PRJNA671397 | ENA

A model for predicting response to PD-1 inhibitors in NSCLC

Project description:A model for predicting response to PD-1 inhibitors in NSCLC

| PRJNA671398 | ENA

FinaleMe: Predicting DNA Methylation by the Fragmentation Patterns of Plasma Cell-free DNA

Project description:FinaleMe: Predicting DNA Methylation by the Fragmentation Patterns of Plasma Cell-free DNA

| PRJNA965896 | ENA

FinaleMe: Predicting DNA Methylation by the Fragmentation Patterns of Plasma Cell-free DNA

Project description:FinaleMe: Predicting DNA Methylation by the Fragmentation Patterns of Plasma Cell-free DNA