AI Medical Compendium Topic

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

Evolution, Molecular

Showing 91 to 100 of 108 articles

Clear Filters

Deep learning revealed the distribution and evolution patterns for invertible promoters across bacterial lineages.

Nucleic acids research
Invertible promoters (invertons) are crucial regulatory elements in bacteria, facilitating gene expression changes under stress. Despite their importance, their prevalence and the range of regulated gene functions are largely unknown. We introduced D...

Do protein language models learn phylogeny?

Briefings in bioinformatics
Deep machine learning demonstrates a capacity to uncover evolutionary relationships directly from protein sequences, in effect internalising notions inherent to classical phylogenetic tree inference. We connect these two paradigms by assessing the ca...

CAPE: a deep learning framework with Chaos-Attention net for Promoter Evolution.

Briefings in bioinformatics
Predicting the strength of promoters and guiding their directed evolution is a crucial task in synthetic biology. This approach significantly reduces the experimental costs in conventional promoter engineering. Previous studies employing machine lear...

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...

Inpactor2: a software based on deep learning to identify and classify LTR-retrotransposons in plant genomes.

Briefings in bioinformatics
LTR-retrotransposons are the most abundant repeat sequences in plant genomes and play an important role in evolution and biodiversity. Their characterization is of great importance to understand their dynamics. However, the identification and classif...

ATSE: a peptide toxicity predictor by exploiting structural and evolutionary information based on graph neural network and attention mechanism.

Briefings in bioinformatics
MOTIVATION: Peptides have recently emerged as promising therapeutic agents against various diseases. For both research and safety regulation purposes, it is of high importance to develop computational methods to accurately predict the potential toxic...

Learning Retention Mechanisms and Evolutionary Parameters of Duplicate Genes from Their Expression Data.

Molecular biology and evolution
Learning about the roles that duplicate genes play in the origins of novel phenotypes requires an understanding of how their functions evolve. A previous method for achieving this goal, CDROM, employs gene expression distances as proxies for function...

Machine learning-based chemical binding similarity using evolutionary relationships of target genes.

Nucleic acids research
Chemical similarity searching is a basic research tool that can be used to find small molecules which are similar in shape to known active molecules. Despite its popularity, the retrieval of local molecular features that are critical to functional ac...