Browse
Submit Data
Databases
API
Help

Dataset Information

0 Views

0 Connections

0 Citations

0 Reanalyses

0 Downloads

Omics score: 0

Artificial intelligence assisted endocytoscopy in real-time diagnosis of early colorectal cancer

ABSTRACT: Interventions: Gold Standard:Pathological test;Index test:1. Endoscopy; 2. Artificial intelligence real-time diagnosis system. Primary outcome(s): Diagnostic accuracy;Sensitivity;Specificity;Positive predictive value;Negative predictive value Study Design: Sequential

DISEASE(S): Early Colorectal Cancer

PROVIDER: 2725610 | ecrin-mdr-crc |

REPOSITORIES: ECRIN MDR

ACCESS DATA

Json Xml

Similar Datasets

Construction of colorectal cancer genomics artificial intelligence system basing on deep learning model

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

| 2694323 | 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-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

Accuracy of Real Time Characterization in Artificial Intelligence-assisted Colonoscopy

Project description:The goal of this substudy is to investigate the accuracy of a computer-aided polyp characterization (CADx) system. The main question[s] it aims to answer are: • How high is the specificity of the AI system when characterizing colorectal polyps Participants will receive a standard colonoscopy, assisted by the artificial intelligence (AI) assisted system GI Genius. Researchers will compare the AI system´s characterization with the histopathology to see how accurate the system is.

| 10350 | ecrin-mdr-crc

Application of Artificial Intelligence to Plasma Metabolomics Profiles to Predict Response to Neoadjuvant Chemotherapy in Triple-Negative Breast Cancer

Project description:Summary: There is a need for biomarkers predictive of response to neoadjuvant chemotherapy (NACT) in triple-negative breast cancer (TNBC). We previously obtained evidence that a polyamine signature in the blood is associated with TNBC development and progression. In this study, we evaluated whether plasma polyamines and other metabolites may identify TNBC patients who are unlikely to respond to NACT. Pre-treatment plasma levels of acetylated polyamines were elevated in TNBC patients that had moderate to extensive tumor burden (RCB-II/III) following NACT compared to those that achieved a complete pathological response (pCR/RCB-0) or had minimal residual disease (RCB-I). We further applied artificial intelligence to comprehensive metabolic profiles to identify additional metabolites associated with treatment response. A deep learning model (DLM) consisting of two polyamines as well as nine additional metabolites was developed for improved prediction of RCB-II/III. The DLM has potential clinical value for identifying TNBC patients who are unlikely to respond to NACT and who may benefit from other treatment modalities.

2022-05-26 | ST002235 | MetabolomicsWorkbench

Higher intelligence is genetically linked with longevity

Project description:We report the application of mRNA sequencing technology for identifying that the genetic linkage of higher intelligence with longevity in Drosophilla melanogaster. a transcriptome analysis was performed by sequencing the total mRNA. The differentially expressed gene analysis by a negative binomial test showed that a subset of genes displayed remarkably altered expression where 56, 152, and 26 genes were significantly upregulated (adjusted P value <0.01), and 30, 397, and 10 genes were downregulated (adjusted P value < 0.01) in INT compared to F0, NINT compared to F0, and INT compared to NINT each. The functions of the differentially expressed genes were analyzed at high levels by using Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment program. Compared to F0, the pathways of ribosome and autophagy were up-regulated in INT while the pathways related to metabolisms were down-regulated. Meanwhile, the pathways associated with genome stability and immune reaction was upregulated and the pathway of oxidative phosphorylation responsible for ATP generation was down regulated in NINT. The KEGG comparison of INT with NINT, which eliminates genes associated with the environmental factors during the selection process, visualized more precise pathways associated with higher intelligence and longevity. The pathways related to genome stability (Fanconi anemia pathway, Mismatch repair, DNA replication, Homologous recombination, and Nucleotide excision repair) and immune reaction (Toll and lmd signaling pathway) were up-regulated in NINT, and these pathways were down-regulated in INT.

2023-02-17 | GSE196981 | GEO

CT artificial intelligence based on tumor immune microenvironment predicting the prognosis of stage II colorectal cancer

Project description:Interventions: Gold Standard:Clinical outcome;Index test:Pathological immunofluorescence analysis was performed to determine the infiltration status of immune cells in postoperative rectal cancer specimens including CD8+, PDL1_1, CD45RO, PD1, CD3, CD8+CD45RO+, CD3+PD1+, CD8+PD1+, FOXP3, CD163, CD68?CD4, PDL1_2, CD68+CD163-, CD68+CD163+, PDL1+CD68+, PDL1+CD68-, CD4+FOXP3+, CD68+CD163+PDL1+, PDL1_A Primary outcome(s): the area under the receiver operating characteristic (AUC);accuracy;Sensitivity;Specificity;Positive predictive values;Negative predictive values Study Design: Factorial

| 2696146 | ecrin-mdr-crc

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