AI Medical Compendium Topic

Explore the latest research on artificial intelligence and machine learning in medicine.

Promoter Regions, Genetic

Showing 31 to 40 of 131 articles

Clear Filters

Comprehensive tissue deconvolution of cell-free DNA by deep learning for disease diagnosis and monitoring.

Proceedings of the National Academy of Sciences of the United States of America
Plasma cell-free DNA (cfDNA) is a noninvasive biomarker for cell death of all organs. Deciphering the tissue origin of cfDNA can reveal abnormal cell death because of diseases, which has great clinical potential in disease detection and monitoring. D...

TIMER is a Siamese neural network-based framework for identifying both general and species-specific bacterial promoters.

Briefings in bioinformatics
BACKGROUND: Promoters are DNA regions that initiate the transcription of specific genes near the transcription start sites. In bacteria, promoters are recognized by RNA polymerases and associated sigma factors. Effective promoter recognition is essen...

LegNet: a best-in-class deep learning model for short DNA regulatory regions.

Bioinformatics (Oxford, England)
MOTIVATION: The increasing volume of data from high-throughput experiments including parallel reporter assays facilitates the development of complex deep-learning approaches for modeling DNA regulatory grammar.

Design and deep learning of synthetic B-cell-specific promoters.

Nucleic acids research
Synthetic biology and deep learning synergistically revolutionize our ability for decoding and recoding DNA regulatory grammar. The B-cell-specific transcriptional regulation is intricate, and unlock the potential of B-cell-specific promoters as synt...

Enhancer-MDLF: a novel deep learning framework for identifying cell-specific enhancers.

Briefings in bioinformatics
Enhancers, noncoding DNA fragments, play a pivotal role in gene regulation, facilitating gene transcription. Identifying enhancers is crucial for understanding genomic regulatory mechanisms, pinpointing key elements and investigating networks governi...

AI and Knowledge-Based Method for Rational Design of Sigma70 Promoters.

ACS synthetic biology
Expanding sigma70 promoter libraries can support the engineering of metabolic pathways and enhance recombinant protein expression. Herein, we developed an artificial intelligence (AI) and knowledge-based method for the rational design of sigma70 prom...

promSEMBLE: Hard Pattern Mining and Ensemble Learning for Detecting DNA Promoter Sequences.

IEEE/ACM transactions on computational biology and bioinformatics
Accurate identification of DNA promoter sequences is of crucial importance in unraveling the underlying mechanisms that regulate gene transcription. Initiation of transcription is controlled through regulatory transcription factors binding to promote...

DeepCBA: A deep learning framework for gene expression prediction in maize based on DNA sequences and chromatin interactions.

Plant communications
Chromatin interactions create spatial proximity between distal regulatory elements and target genes in the genome, which has an important impact on gene expression, transcriptional regulation, and phenotypic traits. To date, several methods have been...

Species-specific design of artificial promoters by transfer-learning based generative deep-learning model.

Nucleic acids research
Native prokaryotic promoters share common sequence patterns, but are species dependent. For understudied species with limited data, it is challenging to predict the strength of existing promoters and generate novel promoters. Here, we developed Promo...

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...