AIMC Topic: 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...

Inferring fungal cis-regulatory networks from genome sequences via unsupervised and interpretable representation learning.

Genetics
Gene expression patterns are determined to a large extent by transcription factor (TF) binding to noncoding regulatory regions in the genome. However, gene expression cannot yet be systematically predicted from genome sequences, in part because nonfu...

[Synthetic promoters: theory, design, and prospects].

Sheng wu gong cheng xue bao = Chinese journal of biotechnology
Synthetic promoters are novel promoters artificially designed and do not exist in nature. They can initiate the expression of target genes with specific regulatory modes, offering advantages such as high expression efficiency, precise regulation, and...

Machine learning Reveals ATM and CNOT6L as critical factors in Cataract pathogenesis.

Experimental eye research
OBJECTIVE: Cataract, a common age-related blinding eye disease, has a complex pathogenesis. This study aims to identify key genes and potential mechanisms associated with cataracts, offering new targets and insights for its prevention and treatment.

SCIG: Machine learning uncovers cell identity genes in single cells by genetic sequence codes.

Nucleic acids research
Deciphering cell identity genes is pivotal to understanding cell differentiation, development, and cell identity dysregulation involving diseases. Here, we introduce SCIG, a machine-learning method to uncover cell identity genes in single cells. In a...

Model-to-crop conserved NUE Regulons enhance machine learning predictions of nitrogen use efficiency.

The Plant cell
Systems biology aims to uncover gene regulatory networks (GRNs) for agricultural traits, but validating them in crops is challenging. We addressed this challenge by learning and validating model-to-crop transcription factor (TF) regulons governing ni...