AIMC Topic: Transcription Factors

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Transcriptional regulation of hypoxic cancer cell metabolism and artificial intelligence.

Trends in cancer
Gene expression regulation in hypoxic tumor microenvironments is mediated by O responsive transcription factors (OR-TFs), fine-tuning cancer cell metabolic demand for O according to its availability. Here, we discuss key OR-TFs and emerging artificia...

Enhancing the Predictive Power of Machine Learning Models through a Chemical Space Complementary DEL Screening Strategy.

Journal of medicinal chemistry
DNA-encoded library (DEL) technology is an effective method for small molecule drug discovery, enabling high-throughput screening against target proteins. While DEL screening produces extensive data, it can reveal complex patterns not easily recogniz...

Identification of mitophagy-related genes and analysis of immune infiltration in the astrocytes based on machine learning in the pathogenesis of major depressive disorder.

Journal of affective disorders
BACKGROUNDS: Major depressive disorder (MDD) is a pervasive mental and mood disorder with complicated and heterogeneous etiology. Mitophagy, a selective autophagy of cells, specifically eliminates dysfunctional mitochondria. The mitochondria dysfunct...

Predicting splicing patterns from the transcription factor binding sites in the promoter with deep learning.

BMC genomics
BACKGROUND: Alternative splicing is a pivotal mechanism of post-transcriptional modification that contributes to the transcriptome plasticity and proteome diversity in metazoan cells. Although many splicing regulations around the exon/intron regions ...

Cross-Species Prediction of Transcription Factor Binding by Adversarial Training of a Novel Nucleotide-Level Deep Neural Network.

Advanced science (Weinheim, Baden-Wurttemberg, Germany)
Cross-species prediction of TF binding remains a major challenge due to the rapid evolutionary turnover of individual TF binding sites, resulting in cross-species predictive performance being consistently worse than within-species performance. In thi...

AI-Assisted Rational Design and Activity Prediction of Biological Elements for Optimizing Transcription-Factor-Based Biosensors.

Molecules (Basel, Switzerland)
The rational design, activity prediction, and adaptive application of biological elements (bio-elements) are crucial research fields in synthetic biology. Currently, a major challenge in the field is efficiently designing desired bio-elements and acc...

TFscope: systematic analysis of the sequence features involved in the binding preferences of transcription factors.

Genome biology
Characterizing the binding preferences of transcription factors (TFs) in different cell types and conditions is key to understand how they orchestrate gene expression. Here, we develop TFscope, a machine learning approach that identifies sequence fea...

Nonlinear classifiers for wet-neuromorphic computing using gene regulatory neural network.

Biophysical reports
The gene regulatory network (GRN) of biological cells governs a number of key functionalities that enable them to adapt and survive through different environmental conditions. Close observation of the GRN shows that the structure and operational prin...

PPRTGI: A Personalized PageRank Graph Neural Network for TF-Target Gene Interaction Detection.

IEEE/ACM transactions on computational biology and bioinformatics
Transcription factors (TFs) regulation is required for the vast majority of biological processes in living organisms. Some diseases may be caused by improper transcriptional regulation. Identifying the target genes of TFs is thus critical for underst...

Identification of drug responsive enhancers by predicting chromatin accessibility change from perturbed gene expression profiles.

NPJ systems biology and applications
Individual may response to drug treatment differently due to their genetic variants located in enhancers. These variants can alter transcription factor's (TF) binding strength, affect enhancer's chromatin activity or interaction, and eventually chang...