AIMC Topic: Gene Expression Regulation, Developmental

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Accurate identification of RNA editing sites from primitive sequence with deep neural networks.

Scientific reports
RNA editing is a post-transcriptional RNA sequence alteration. Current methods have identified editing sites and facilitated research but require sufficient genomic annotations and prior-knowledge-based filtering steps, resulting in a cumbersome, tim...

McEnhancer: predicting gene expression via semi-supervised assignment of enhancers to target genes.

Genome biology
Transcriptional enhancers regulate spatio-temporal gene expression. While genomic assays can identify putative enhancers en masse, assigning target genes is a complex challenge. We devised a machine learning approach, McEnhancer, which links target g...

Transcriptomes of lineage-specific Drosophila neuroblasts profiled by genetic targeting and robotic sorting.

Development (Cambridge, England)
A brain consists of numerous distinct neurons arising from a limited number of progenitors, called neuroblasts in Drosophila. Each neuroblast produces a specific neuronal lineage. To unravel the transcriptional networks that underlie the development ...

Codon bias and gene ontology in holometabolous and hemimetabolous insects.

Journal of experimental zoology. Part B, Molecular and developmental evolution
The relationship between preferred codon use (PCU), developmental mode, and gene ontology (GO) was investigated in a sample of nine insect species with sequenced genomes. These species were selected to represent two distinct modes of insect developme...

Putative synaptic genes defined from a Drosophila whole body developmental transcriptome by a machine learning approach.

BMC genomics
BACKGROUND: Assembly and function of neuronal synapses require the coordinated expression of a yet undetermined set of genes. Although roughly a thousand genes are expected to be important for this function in Drosophila melanogaster, just a few hund...

Deep convolutional neural networks for annotating gene expression patterns in the mouse brain.

BMC bioinformatics
BACKGROUND: Profiling gene expression in brain structures at various spatial and temporal scales is essential to understanding how genes regulate the development of brain structures. The Allen Developing Mouse Brain Atlas provides high-resolution 3-D...

The potential of AOP networks for reproductive and developmental toxicity assay development.

Reproductive toxicology (Elmsford, N.Y.)
Historically, the prediction of reproductive and developmental toxicity has largely relied on the use of animals. The adverse outcome pathway (AOP) framework forms a basis for the development of new non-animal test methods. It also provides biologica...

The Maternal Blood Transcriptome Reflects Changes in Fetal Growth and Is an Accurate Predictor of Birth Weight in Cattle.

FASEB journal : official publication of the Federation of American Societies for Experimental Biology
Harnessing information from maternal blood to predict fetal growth is an emerging area of research in livestock production, offering a noninvasive tool to monitor development. This study aimed to investigate temporal changes in blood gene expression ...

The developmental and evolutionary characteristics of transcription factor binding site clustered regions based on an explainable machine learning model.

Nucleic acids research
Gene expression is temporally and spatially regulated by the interaction of transcription factors (TFs) and cis-regulatory elements (CREs). The uneven distribution of TF binding sites across the genome poses challenges in understanding how this distr...

Uncovering tissue-specific binding features from differential deep learning.

Nucleic acids research
Transcription factors (TFs) can bind DNA in a cooperative manner, enabling a mutual increase in occupancy. Through this type of interaction, alternative binding sites can be preferentially bound in different tissues to regulate tissue-specific expres...