Ontology highlight
ABSTRACT:
PROVIDER: PRJNA503076 | ENA |
REPOSITORIES: ENA
Dataset's files
| Action | DRS | |||
|---|---|---|---|---|
| SRR8138220.fastq.gz | Fastqsanger.gz | |||
| SRR8138230.fastq.gz | Fastqsanger.gz | |||
| SRR8138240.fastq.gz | Fastqsanger.gz | |||
| SRR8138250.fastq.gz | Fastqsanger.gz | |||
| SRR8138260.fastq.gz | Fastqsanger.gz |
Items per page: 5 1 - 5 of 196 |
Similar Datasets
Project description:The incorporation of machine learning methods into proteomics workflows improves the identification of disease-relevant biomarkers and biological pathways. However, machine learning models, such as deep neural networks, typically suffer from lack of interpretability. Here, we present a deep learning approach to combine biological pathway analysis and biomarker identification to increase the interpretability of proteomics experiments. Our approach integrates a priori knowledge of the relationships between proteins and biological pathways and biological processes into sparse neural networks to create biologically informed neural networks. We employ these networks to differentiate between clinical subphenotypes of septic acute kidney injury and COVID-19, as well as acute respiratory distress syndrome of different aetiologies. To gain biological insight into the complex syndromes, we utilize feature attribution-methods to introspect the networks for the identification of proteins and pathways important for distinguishing between subtypes. The algorithms are implemented in a freely available open source Python-package (https://github.com/InfectionMedicineProteomics/BINN).
2023-09-05 | PXD044264 | Pride
Project description:Interventions: Gold Standard:MMR status detection by immunohistochemistry;Index test:deep learning model predicting MMR status of patients with colorectal cancer
Primary outcome(s): AUROC
Study Design: Diagnostic test for accuracy
| 114063 | ecrin-mdr-crc
Project description:International guidelines recommend deciding the treatment of colorectal lesions based on the estimated histology by endoscopic optical diagnosis. However, the theoretical and practical knowledge on optical diagnosis is not widely expanded
The mail goal of this randomised controlled trial is to compare the pooled sensitivity of optical diagnosis for predicting deep submucosal invasion in large non-pedunculated polyps > 20 mm assessed in routine colonoscopies of gastroenterologists attending a e-learning module (intervention group) vs gastroenterologists who do not (control group)
The main questions the study aims to answer are:
* Is the pooled sensitivity of optical diagnosis for predicting deep submucosal invasion in large non-pedunculated polyps assessed in routine colonoscopies increased in those gastroenterologists participating in the e-learning module?
* Is the pooled diagnostic accuracy of optical diagnosis for predicting deep sm invasion in large non-pedunculated polyps ≥ 20 mm assessed in routine colonoscopies increased in those gastroenterologists participating in the e-learning module?
* In lesions with submucosal invasion, is the en bloc and complete resection rate (R0) increased in those gastroenterologists participating in the e-learning module?
* In lesions referred to surgery, is the pooled benign polyps rate decreased in those gastroenterologists participating in the e-learning module?
* In lesions treated with advanced en bloc procedures (ESD, TAMIS, fullthickness resection), is the pooled rate of histology with high-grade dysplasia, intramucosal cancer or submucosal invasion increased in those gastroenterologists participating in the e-learning module?
* In lesions treated with piecemeal endoscopic resection, is the pooled rate of histology with high-grade dysplasia, intramucosal cancer or submucosal invasion decreased in those gastroenterologists participating in the e-learning module?
* Is the diagnostic accuracy for predicting deep submucosal invasion in a test with pictures increased after participating in the e-learning module?
The participants (or subjects of study) are gastroenterologists. They will be randomised to do the e-learning course (intervention group) or not (control group).
Researchers will compare clinical outcomes of gastroenterologists participating in the e-learning module vs gastroenterologists not participating in the e-learning module to see if:
* the pooled sensitivity of optical diagnosis for predicting deep submucosal invasion in large non-pedunculated polyps > 20 mm assessed in routine colonoscopies is increased.
* the pooled diagnostic accuracy of optical diagnosis for predicting deep sm invasion in large non-pedunculated polyps > 20 mm is increased.
* the en bloc and complete resection rate (R0) is increased in lesions with submucosal invasion.
* the pooled benign polyps rate decreased in lesions referred to surgery.
* the pooled rate of histology with high-grade dysplasia, intramucosal cancer or submucosal invasion increased in lesions treated with advanced en bloc procedures (ESD, TAMIS, fullthickness resection).
* the pooled rate of histology with high-grade dysplasia, intramucosal cancer or submucosal invasion decreased in lesions treated with piecemeal endoscopic resection.
* the diagnostic accuracy for predicting deep submucosal invasion in a test with pictures after participating is increased.
| 3397 | ecrin-mdr-crc
Project description:Linking DNA sequence to genomic function remains one of the grand challenges in genetics and genomics. Here, we combine large-scale single-molecule transcriptome sequencing of diverse cancer cell lines with cutting-edge machine learning to build LoRNASH, an RNA foundation model that learns how the nucleotide sequence of unspliced pre-mRNA dictates transcriptome architecture—the relative abundances and molecular structures of mRNA isoforms. Owing to its use of the StripedHyena architecture, LoRNASH handles extremely long sequence inputs at base-pair resolution (~65 kilobase pairs), allowing for quantitative, zero-shot prediction of all aspects of transcriptome architecture, including isoform abundance, isoform structure, and the impact of DNA sequence variants on transcript structure and abundance. We anticipate that our public data release and the accompanying frontier model will accelerate many aspects of RNA biotechnology. More broadly, we envision the use of LoRNASH as a foundation for fine-tuning of any transcriptome-related downstream prediction task, including cell-type specific gene expression, splicing, and general RNA processing.
2024-10-22 | GSE280041 | GEO
Project description:Current clinical antibiotics are largely broad-spectrum agents that promote intestinal dysbiosis and colonisation of Enterobacteriaceae, which are often drug-resistant. Indeed, dysbiosis creates an ideal niche for adherent-invasive Escherichia coli (AIEC) in patients with inflammatory bowel disease (IBD). There is an urgent and unmet need for novel narrow-spectrum and microbiome-sparing antibiotics. Here, we screened >10,000 molecules for antibacterial activity against AIEC and discovered enterololin, an antibacterial compound with targeted activity against Enterobacteriaceae species. Molecular substructure- and deep learning-guided mechanism of action investigations revealed that enterololin perturbs lipoprotein trafficking through a mechanism involving the LolCDE complex. Moreover, enterololin can suppress an AIEC infection in mouse models, while largely preserving the overall microbiome composition. This work highlights the utility of deep learning methods for predicting molecular interactions, thereby accelerating mechanism of action elucidation of novel molecules, and identifies a promising Enterobacteriaceae-specific antibacterial candidate for further development to treat challenging infections in IBD patients.
2025-08-27 | GSE300731 | GEO
Project description:Comparison of relative transcript and protein abundance in Plasmodium falciparum schizont stage parasites
| PRJNA109533 | ENA
Project description:To be able to reliably generate theoretical libraries that can be used in SWATH experiments, we developed a prediction framework, deep-learning for SWATH analysis (dpSWATH), to improve the sensitivity and specificity of data generated by Q-TOF mass spectrometers. The theoretical library built by dpSWATH allowed us to increase the identification rate of proteins and peptides compared to traditional or library-free methods. Especially, the in-silico library built based on the transcriptome scale identified the most proteins while kept a similar FDR as DDA library. Based on our analysis we conclude that dpSWATH is superior in predicting libraries that can be used for SWATH-MS measurements compared to other algorithms that are based on Orbitrap data.
2023-01-16 | PXD038407 | Pride
Project description:The dual functional lncRNAs have been intensively studied and identified to be involved in various fundamental cellular processes recently. It is essential to understand in which context when a dual functional lncRNA serves as a non-coding RNA or a template for coding peptide, particularly in some pathological conditions. However, apart from time consuming and cell type specific experiments, there is virtually no in-silico method for predicting the identity of dual functional lncRNAs. Here, we developed a deep-learning model with multi-head self-attention mechanism, LncReader, to identify dual functional lncRNAs based on their sequence, physicochemical and secondary structural features. Our data demonstrated that LncReader showed multiple advantage compared to various classical machine learning methods. Moreover, to obtain independent in-house datasets for robust testing, mass spectrometry proteomics combined with RNA-seq were applied in four leukemia cell lines. Remarkably, LncReader achieved the best performance among all these datasets. Therefore, LncReader provides a sophisticated and practical tool that enables fast dual functional lncRNAs identification.
2023-04-26 | PXD030066 | Pride