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Transcription Factors

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g:Profiler-a web server for functional interpretation of gene lists (2016 update).

Nucleic acids research
Functional enrichment analysis is a key step in interpreting gene lists discovered in diverse high-throughput experiments. g:Profiler studies flat and ranked gene lists and finds statistically significant Gene Ontology terms, pathways and other gene ...

Learning a hierarchical representation of the yeast transcriptomic machinery using an autoencoder model.

BMC bioinformatics
BACKGROUND: A living cell has a complex, hierarchically organized signaling system that encodes and assimilates diverse environmental and intracellular signals, and it further transmits signals that control cellular responses, including a tightly con...

Predicting transcription factor site occupancy using DNA sequence intrinsic and cell-type specific chromatin features.

BMC bioinformatics
BACKGROUND: Understanding the mechanisms by which transcription factors (TF) are recruited to their physiological target sites is crucial for understanding gene regulation. DNA sequence intrinsic features such as predicted binding affinity are often ...

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 ...

Effect of sprint training on resting serum irisin concentration - Sprint training once daily vs. twice every other day.

Metabolism: clinical and experimental
OBJECTIVE: Exercise twice every other day has been shown to lead to increasing peroxisome proliferator receptor γ coactivator-1α (PGC-1α) expression (up-stream factor of irisin) via lowered muscle glycogen level during second of exercise compared wit...

Deep learning-based cell-specific gene regulatory networks inferred from single-cell multiome data.

Nucleic acids research
Gene regulatory networks (GRNs) provide a global representation of how genetic/genomic information is transferred in living systems and are a key component in understanding genome regulation. Single-cell multiome data provide unprecedented opportunit...

miRStart 2.0: enhancing miRNA regulatory insights through deep learning-based TSS identification.

Nucleic acids research
MicroRNAs (miRNAs) are small non-coding RNAs that regulate gene expression by binding to the 3'-untranslated regions of target mRNAs, influencing various biological processes at the post-transcriptional level. Identifying miRNA transcription start si...

A self-attention-driven deep learning framework for inference of transcriptional gene regulatory networks.

Briefings in bioinformatics
The interactions between transcription factors (TFs) and the target genes could provide a basis for constructing gene regulatory networks (GRNs) for mechanistic understanding of various biological complex processes. From gene expression data, particu...

FunlncModel: integrating multi-omic features from upstream and downstream regulatory networks into a machine learning framework to identify functional lncRNAs.

Briefings in bioinformatics
Accumulating evidence indicates that long noncoding RNAs (lncRNAs) play important roles in molecular and cellular biology. Although many algorithms have been developed to reveal their associations with complex diseases by using downstream targets, th...

Predicting bacterial transcription factor binding sites through machine learning and structural characterization based on DNA duplex stability.

Briefings in bioinformatics
Transcriptional factors (TFs) in bacteria play a crucial role in gene regulation by binding to specific DNA sequences, thereby assisting in the activation or repression of genes. Despite their central role, deciphering shape recognition of bacterial ...