Browse
Submit Data
Databases
API
Help

Dataset Information

0 Views

0 Connections

0 Citations

0 Reanalyses

0 Downloads

Omics score: 0

Early Screening of Colorectal Cancer Based on Plasma Multi-omics Combining With Artificial Intelligence

ABSTRACT: To evaluate the accuracy and effectiveness of a novel screening method based on plasma multi-omics combining with artificial intelligence in a large prospective cohort for the detection of colorectal cancer and advanced adenomas.

DISEASE(S): Colorectal Adenoma,Colorectal Cancer,Colorectal Neoplasms,Adenoma

PROVIDER: 2751979 | ecrin-mdr-crc |

REPOSITORIES: ECRIN MDR

ACCESS DATA

Json Xml

Similar Datasets

Artificial Intelligence-powered Drug Discovery against CTLA-4 in Cancer

Project description:Artificial Intelligence-powered Drug Discovery against CTLA-4 in Cancer

| PRJNA950363 | ENA

RNA-seq of S. cerevisiae with DOPAC and 4-nitrocatechol to investigate how Artificial Intelligence uncovers human carcinogenic metabolites

Project description:We have performed paired-end RNA-sequencing on budding yeast (BY4741), exposed to 4-nitrocatechol and 3,4-Dihydroxyphenylacetic acid (DOPAC). These two human metabolites were predicted as potential carcinogens using Metabokiller, an artificial intelligence-driven carcinogenicity predictor. The dataset contains 3 biological conditions, i.e. untreated (NC), DOPAC treated (DP), and 4-nitrocatechol treated (4NC). Two rounds of selection procedures have been introduced. The first selection was for canavanine resistance, and the followed selection was for rapid cell division (inferred using growth assay). The experiment was performed in biological triplicates.

2022-06-09 | E-MTAB-11179 | biostudies-arrayexpress

Artificial Intelligence-powered Drug Discovery against CTLA-4 in Cancer

Project description:Cytotoxic T-lymphocyte-associated protein 4 (CTLA-4) plays a pivotal role in preventing autoimmunity and fostering anticancer immunity by interacting with B7 proteins CD80 and CD86. CTLA-4 is the first immune checkpoint targeted with a monoclonal antibody inhibitor. Checkpoint inhibitors have generated durable responses in many cancer patients, representing a revolutionary milestone in cancer immunotherapy. However, therapeutic efficacy is limited to a small portion of patients, and immune-related adverse events are noteworthy, especially for monoclonal antibodies directed against CTLA-4. Previously, small molecules have been developed to impair the CTLA-4: CD80 interaction; however, they directly targeted CD80 and not CTLA-4. In this study, we performed artificial intelligence (AI)-powered virtual screening of approximately ten million compounds to target CTLA-4. We validated primary hits with biochemical, biophysical, immunological, and experimental animal assays. We then optimized lead compounds and obtained inhibitors with an inhibitory concentration of 1 micromole in disrupting the interaction between CTLA-4 and CD80. Unlike ipilimumab, these small molecules did not degrade CTLA-4. Several compounds inhibited tumor development prophylactically and therapeutically in syngeneic and CTLA-4-humanized mice. This project supports an AI-based framework in designing small molecules targeting immune checkpoints for cancer therapy.

2024-02-15 | GSE228560 | GEO

Surveillance Modified by Artificial Intelligence in Endoscopy (SMARTIE)

Project description:Evaluation of an artificial intelligence system for polyp detection (CADe)

| 2386702 | ecrin-mdr-crc

Multisystem Inflammatory Syndrome in Children Disease characterization and diagnostics using mass-spectrometry-based proteomics and artificial intelligence

Project description:During the COVID-19 pandemic, Multisystem Inflammatory Syndrome in Children (MIS-C) emerged as a disease with unknown causes and no specific diagnostic test. We used artificial intelligence in a nationwide study from April 2020 to March 2022 to identify a 4-protein diagnostic signature for MIS-C. This signature had an AUC of 1.0 for distinguishing MIS-C from other febrile diseases. Our analysis also revealed intricate protein patterns, indicating that MIS-C is associated with immune dysregulation, lipid metabolism changes, increased coagulation activity, and tissue remodeling.

2024-05-14 | PXD045661 | Pride

Artificial Intelligence in Omics.

Project description: Not available

| S-EPMC10025753 | biostudies-literature

Construction of an accurate treatment system for colorectal cancer basing on omics research of intestinal microecosystem

Project description:Interventions: Gold Standard:Pathological diagnosis;Index test:Construct colorectal cancer virus omics artificial intelligence system basing on deep learning model Primary outcome(s): Intestinal flora;SEN, SPE, ACC, AUC of ROC Study Design: Factorial

| 2694487 | ecrin-mdr-crc

Artificial Intelligence in proteomic profiling of Cerebrospinal fluid from extraventricular drainage in child Medulloblastoma

Project description:Medulloblastoma (MB) is the most common pediatric malignant central nervous system tumor. Overall survival in MB depends on treatment tuning. There is the need for biomarkers of residual disease, and recurrence. We analysed the proteome of waste cerebrospinal fluid (CSF) from extraventricular drainage (EVD) from 6 children bearing various subtypes of MB and 6 controls needing EVD insertion for unrelated causes. Samples included total CSF, Microvesicles, Exosomes, and proteins captured by combinatorial peptide ligand library (CPLL). Liquid Chromatography-Coupled Tandem Mass Spectrometry proteomics identified 3560 proteins in CSF from control and MB patients, 2412 (67.7%) of which were overlapping, and 346 (9.7%) and 805 (22.6%) exclusive. Multidimensional scaling analysis discriminated samples. The weighted gene co-expression network analysis (WGCNA) identified those modules functionally associated with the samples. A ranked core of 192 proteins allowed distinguishing between control and MB samples. Machine learning highlighted long-chain fatty acid transport protein 4 (SLC27A4), and laminin B-type (LMNB1) as proteins that maximize the discrimination between control and MB samples, respectively. Artificial intelligence was able to distinguish between MB vs non-tumor/hemorrhagic controls. The two potential protein biomarkers for the discrimination between control and MB may guide therapy and predict recurrences, improving the MB patients quality of life.

2022-10-14 | PXD035292 | Pride

Early screening of colorectal cancer based on plasma multi-omics combining with artificial intelligence: a multi-center, prospective study (R-E-C-O-M-M-E-N-D)

Project description:Interventions: Healthy group:None;Advanced adenoma:None;Colorectal caner:None Primary outcome(s): Pathological stage Study Design: Cohort study

| 41497 | ecrin-mdr-crc

Artificial intelligence-based histopathology image analysis identifies a novel subset of endometrial cancers with distinct genomic features and unfavourable outcome

Project description:Artificial intelligence-based histopathology image analysis identifies a novel subset of endometrial cancers with distinct genomic features and unfavourable outcome

| PRJEB60600 | ENA

OmicsDI is part of the ELIXIR infrastructure

OmicsDI is an Elixir interoperability service. Learn more ›

Tweets

OmicsDI Databases

PRIDE
PeptideAtlas
MassIVE
JPOST Repository
Physiome Model Repository

EGA
EVA
ENA
LINCS
PAXDB
Cell Collective

MetaboLights
Metabolomics Workbench
MetabolomeExpress
GNPS
BioModels
FAIRDOMHub

ArrayExpress
dbGaP
ExpressionAtlas
GEO
NODE

Information

Databases
Help
API
Contact us
Code on GitHub
Terms of use
Submit Data