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Transcription Factors

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BERT-TFBS: a novel BERT-based model for predicting transcription factor binding sites by transfer learning.

Briefings in bioinformatics
Transcription factors (TFs) are proteins essential for regulating genetic transcriptions by binding to transcription factor binding sites (TFBSs) in DNA sequences. Accurate predictions of TFBSs can contribute to the design and construction of metabol...

Deep-Learning Uncovers certain CCM Isoforms as Transcription Factors.

Frontiers in bioscience (Landmark edition)
BACKGROUND: Cerebral Cavernous Malformations (CCMs) are brain vascular abnormalities associated with an increased risk of hemorrhagic strokes. Familial CCMs result from autosomal dominant inheritance involving three genes: (), (), and (). CCM1 and...

Deep Learning for Predicting Gene Regulatory Networks: A Step-by-Step Protocol in R.

Methods in molecular biology (Clifton, N.J.)
Deep learning has emerged as a powerful tool for solving complex problems, including reconstruction of gene regulatory networks within the realm of biology. These networks consist of transcription factors and their associations with genes they regula...

DeepSTF: predicting transcription factor binding sites by interpretable deep neural networks combining sequence and shape.

Briefings in bioinformatics
Precise targeting of transcription factor binding sites (TFBSs) is essential to comprehending transcriptional regulatory processes and investigating cellular function. Although several deep learning algorithms have been created to predict TFBSs, the ...

Cooperation of local features and global representations by a dual-branch network for transcription factor binding sites prediction.

Briefings in bioinformatics
Interactions between DNA and transcription factors (TFs) play an essential role in understanding transcriptional regulation mechanisms and gene expression. Due to the large accumulation of training data and low expense, deep learning methods have sho...

Interpretable deep learning for chromatin-informed inference of transcriptional programs driven by somatic alterations across cancers.

Nucleic acids research
Cancer is a disease of gene dysregulation, where cells acquire somatic and epigenetic alterations that drive aberrant cellular signaling. These alterations adversely impact transcriptional programs and cause profound changes in gene expression. Inter...

DNAffinity: a machine-learning approach to predict DNA binding affinities of transcription factors.

Nucleic acids research
We present a physics-based machine learning approach to predict in vitro transcription factor binding affinities from structural and mechanical DNA properties directly derived from atomistic molecular dynamics simulations. The method is able to predi...

Identifying modifications on DNA-bound histones with joint deep learning of multiple binding sites in DNA sequence.

Bioinformatics (Oxford, England)
MOTIVATION: Histone modifications are epigenetic markers that impact gene expression by altering the chromatin structure or recruiting histone modifiers. Their accurate identification is key to unraveling the mechanisms by which they regulate gene ex...

Genome-wide cis-decoding for expression design in tomato using cistrome data and explainable deep learning.

The Plant cell
In the evolutionary history of plants, variation in cis-regulatory elements (CREs) resulting in diversification of gene expression has played a central role in driving the evolution of lineage-specific traits. However, it is difficult to predict expr...

Machine Learning Prediction of Non-Coding Variant Impact in Human Retinal cis-Regulatory Elements.

Translational vision science & technology
PURPOSE: Prior studies have demonstrated the significance of specific cis-regulatory variants in retinal disease; however, determining the functional impact of regulatory variants remains a major challenge. In this study, we utilized a machine learni...