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Interpretable predictive models of genome-wide aryl hydrocarbon receptor-DNA binding reveal tissue-specific binding determinants.

Toxicological sciences : an official journal of the Society of Toxicology
The aryl hydrocarbon receptor (AhR) is an inducible transcription factor whose ligands include the potent environmental contaminant 2,3,7,8-tetrachlorodibenzo-p-dioxin (TCDD). Ligand-activated AhR binds to DNA at dioxin response elements (DREs) conta...

Targeted design of synthetic enhancers for selected tissues in the Drosophila embryo.

Nature
Enhancers control gene expression and have crucial roles in development and homeostasis. However, the targeted de novo design of enhancers with tissue-specific activities has remained challenging. Here we combine deep learning and transfer learning t...

Tissue specific tumor-gene link prediction through sampling based GNN using a heterogeneous network.

Medical & biological engineering & computing
A tissue sample is a valuable resource for understanding a patient's symptoms and health status in relation to tumor growth. Recent research seeks to establish a connection between tissue-specific tumor samples and genetic markers (genes). This break...

Self-supervised learning for characterising histomorphological diversity and spatial RNA expression prediction across 23 human tissue types.

Nature communications
As vast histological archives are digitised, there is a pressing need to be able to associate specific tissue substructures and incident pathology to disease outcomes without arduous annotation. Here, we learn self-supervised representations using a ...

Tissue of origin detection for cancer tumor using low-depth cfDNA samples through combination of tumor-specific methylation atlas and genome-wide methylation density in graph convolutional neural networks.

Journal of translational medicine
BACKGROUND: Cell free DNA (cfDNA)-based assays hold great potential in detecting early cancer signals yet determining the tissue-of-origin (TOO) for cancer signals remains a challenging task. Here, we investigated the contribution of a methylation at...

Current genomic deep learning models display decreased performance in cell type-specific accessible regions.

Genome biology
BACKGROUND: A number of deep learning models have been developed to predict epigenetic features such as chromatin accessibility from DNA sequence. Model evaluations commonly report performance genome-wide; however, cis regulatory elements (CREs), whi...

Tissue-aware interpretation of genetic variants advances the etiology of rare diseases.

Molecular systems biology
Pathogenic variants underlying Mendelian diseases often disrupt the normal physiology of a few tissues and organs. However, variant effect prediction tools that aim to identify pathogenic variants are typically oblivious to tissue contexts. Here we r...

Tisslet tissues-based learning estimation for transcriptomics.

BMC bioinformatics
In the context of multi-omics data analytics for various diseases, transcriptome-wide association studies leveraging genetically predicted gene expression hold promise for identifying novel regions linked to complex traits. However, existing methods ...

A deep learning model trained on expressed transcripts across different tissue types reveals cell-type codon-optimization preferences.

Nucleic acids research
Species-specific differences in protein translation can affect the design of protein-based drugs. Consequently, efficient expression of recombinant proteins often requires codon optimization. Publicly available optimization tools do not always result...

Investigation of cell development and tissue structure network based on natural Language processing of scRNA-seq data.

Journal of translational medicine
BACKGROUND: Single-cell multi-omics technologies, particularly single-cell RNA sequencing (scRNA-seq), have revolutionized our understanding of cellular heterogeneity and development by providing insights into gene expression at the single-cell level...