Via scMultiomeGRN, we identified Alzheimer's disease-relevant regulatory network of SPI1 and RUNX1 for microglia. In summary, scMultiomeGRN offers a deep learning framework to identify cell type-specific gene regulatory network from single-cell multiome data....
The binding specificities of RNA- and DNA-binding proteins are determined from experimental data using a ‘deep learning’ approach. Knowing the sequence specificities of DNA- and RNA-binding proteins is essential for developing models of the regulatory
69 developed a novel deep architecture that combines deep Boltzmann machine architecture70 with conditional and lateral connections derived from the gene regulatory network. The model provided insights about intermediate phenotypes and their connections to high-level phenotypes (disease traits). Laksshman ...
A hybrid deep learning framework for gene regulatory network inference from single-cell transcriptomic data Brief Bioinform, 23 (2022), p. bbab568 View in ScopusGoogle Scholar [94] Gene Ontology Consortium The Gene Ontology (GO) database and informatics resource Nucleic Acids Res, 32 (2004), ...
However, the training of this neural network was unsuccessful with our image set as it reached the futile training function (Figure 2). Eventually, if left running, VGG16 may train the images, but it would take much longer time and resources. Optimizers Learning rate (LR) is critical for ...
on biological networks by exploiting structural analogies between biological networks (such as the signaling pathways and gene-regulatory networks that regulate cell state) and the feed-forward neural networks used for deep learning. In KPNNs, each network node corresponds to a protein or a gene, ...
Cap analysis of gene expression ChIP-Seq: Chromatin ImmunoPrecipitation followed by sequencing CNN: Convolutional neural network DL: Deep learning GSP: Genomic signal processing ML: Machine learning NGS: Next generation sequencing NN: Neural network ReLU: Rectified Linear Unit TFBS: Transcri...
Here, we developed a deep learning model to predict hitherto unidentified degrons, allowing for a deeper characterization of the regulatory network involved in protein degradation, both in health and disease. Widely used motif-based methods are limited by few E3 motifs and high false-positive rate...
pythonbioinformaticsdeep-learningstructural-biologyprotein-data-bankprotein-structurecomputational-biologypytorchproteindrug-discoveryrnainteractomeprotein-designinteractomicsgeometric-deep-learningppi-networksgraph-neural-networkspytorch-geometricgene-regulatory-networksdgl ...
data sets, deep learning has transformed fields such as computer vision and natural language processing. Now, it is becoming the method of choice for many genomics modelling tasks, including predicting the impact of genetic variation on gene regulatory mechanisms such as DNA accessibility and splicing...