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MetabolomicsDynamics of Plasma Lipidome in Progression to Islet Autoimmunity and Type 1 Diabetes: Type 1 Diabetes Prediction and Prevention Study (DIPP)
Type 1 diabetes (T1D) is one of the most prevalent autoimmune diseases among children in Western countries. Earlier metabolomics studies suggest that T1D is preceded by dysregulation of lipid metabolism. Here we used a lipidomics approach to analyze molecular lipids in a prospective series of 428 pl...
2018-07-16 | MTBLS620 | MetaboLights
GenomicsCombining transcription factor binding affinities with open chromatin data for accurate gene expression prediction
The binding and contribution of transcription factors (TF) to cell specific gene expression is often deduced from open-chromatin measurements to avoid cost and labour intensive TF ChIP-seq assays.It is important to develop reliable and fast computational methods for accurate TF binding prediction in...
EGAS00001002073 | EGA
ProteomicsDefining HLA-II ligand processing and binding rules with mass spectrometry enhances cancer epitope prediction
Increasing evidence indicates CD4+ T cells can recognize cancer-specific antigens and control tumor growth. However, it remains difficult to predict the antigens that will be presented by human leukocyte antigen class II molecules (HLA-II) - hindering efforts to optimally target them therapeutically...
ORGANISM(S): Homo Sapiens (ncbitaxon:9606) 
2019-06-18 | MSV000083991 | MassIVE
ModelsJiménez-Luna2021 - Coloring molecules for plasma protein binding prediction
By combining a Message-Passing Graph Neural Network (MPGNN) and a Forward fully connected Neural Network (FNN) with an integrated gradients explainable artificial intelligence (XAI) method, the authors developed MolGrad and tested it on a number of ADME predictive tasks such as plasma protein bindin...
2024-05-21 | MODEL2405210005 | BioModels
Machine Learning Model FAIR-AIML Ersilia 
GenomicsDNase I hypersensitivity and algorithmic prediction of TF binding in early pancreatic mES directed differentiation
This dataset uses DNase-seq to profile the genome-wide DNase I hypersensitivity of mES and mES-derived cells along an early pancreatic lineage and provides the locations of putative Transcription Factor (TF) binding sites using the PIQ algorithm. DNase-seq takes advantage of the preferential cutting...
ORGANISM(S): Mus musculus 
2014-01-19 | E-GEOD-53776 | biostudies-arrayexpress
GenomicsmaxATAC: genome-scale transcription-factor binding prediction from ATAC-seq with deep neural networks
maxATAC: genome-scale transcription-factor binding prediction from ATAC-seq with deep neural networks
PRJNA808389 | ENA
GenomicsProtein/DNA in vitro Binding seq (PB-seq) for Drosophila HSF and genomic Drosophila DNA
DNA sequence and local chromatin landscape act jointly to determine transcription factor (TF) binding intensity profiles. To disentangle these influences, we developed an experimental approach, called protein/DNA binding and high-throughput sequencing (PB-seq), that allows the binding energy landsca...
ORGANISM(S): Drosophila melanogaster 
2012-02-06 | E-GEOD-32570 | biostudies-arrayexpress
GenomicsPrediction of functional microRNA targets by integrative modeling of microRNA binding and target expression data
Prediction of functional microRNA targets by integrative modeling of microRNA binding and target expression data
PRJNA512378 | ENA
GenomicsGenome-wide prediction of topoisomerase IIB binding by architectural factors and chromatin accessibility
Genome-wide prediction of topoisomerase IIB binding by architectural factors and chromatin accessibility
PRJNA593812 | ENA
TranscriptomicsEnhancer Transcripts Mark Active Estrogen Receptor Binding Sites
In this study, we used Global Run-On sequencing (GRO-seq), a method that assays the genome-wide location and orientation of all active RNA polymerases. We generated a global profile of active transcription at ERM-NM-1 binding sites in MCF-7 human breast cancer cells in response to short time course ...
ORGANISM(S): Homo sapiens 
2013-05-08 | E-GEOD-43835 | biostudies-arrayexpress
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