AI Medical Compendium Journal:
Nucleic acids research

Showing 81 to 90 of 228 articles

Fusang: a framework for phylogenetic tree inference via deep learning.

Nucleic acids research
Phylogenetic tree inference is a classic fundamental task in evolutionary biology that entails inferring the evolutionary relationship of targets based on multiple sequence alignment (MSA). Maximum likelihood (ML) and Bayesian inference (BI) methods ...

STAMarker: determining spatial domain-specific variable genes with saliency maps in deep learning.

Nucleic acids research
Spatial transcriptomics characterizes gene expression profiles while retaining the information of the spatial context, providing an unprecedented opportunity to understand cellular systems. One of the essential tasks in such data analysis is to deter...

A multi-scale expression and regulation knowledge base for Escherichia coli.

Nucleic acids research
Transcriptomic data is accumulating rapidly; thus, scalable methods for extracting knowledge from this data are critical. Here, we assembled a top-down expression and regulation knowledge base for Escherichia coli. The expression component is a 1035-...

Predicting functional consequences of SNPs on mRNA translation via machine learning.

Nucleic acids research
The functional impact of single nucleotide polymorphisms (SNPs) on translation has yet to be considered when prioritizing disease-causing SNPs from genome-wide association studies (GWAS). Here we apply machine learning models to genome-wide ribosome ...

Achieving pan-microbiome biological insights via the dbBact knowledge base.

Nucleic acids research
16S rRNA amplicon sequencing provides a relatively inexpensive culture-independent method for studying microbial communities. Although thousands of such studies have examined diverse habitats, it is difficult for researchers to use this vast trove of...

Design of synthetic promoters for cyanobacteria with generative deep-learning model.

Nucleic acids research
Deep generative models, which can approximate complex data distribution from large datasets, are widely used in biological dataset analysis. In particular, they can identify and unravel hidden traits encoded within a complicated nucleotide sequence, ...

DDMut: predicting effects of mutations on protein stability using deep learning.

Nucleic acids research
Understanding the effects of mutations on protein stability is crucial for variant interpretation and prioritisation, protein engineering, and biotechnology. Despite significant efforts, community assessments of predictive tools have highlighted ongo...

The LightDock Server: Artificial Intelligence-powered modeling of macromolecular interactions.

Nucleic acids research
Computational docking is an instrumental method of the structural biology toolbox. Specifically, integrative modeling software, such as LightDock, arise as complementary and synergetic methods to experimental structural biology techniques. Ubiquitous...

TCRmodel2: high-resolution modeling of T cell receptor recognition using deep learning.

Nucleic acids research
The cellular immune system, which is a critical component of human immunity, uses T cell receptors (TCRs) to recognize antigenic proteins in the form of peptides presented by major histocompatibility complex (MHC) proteins. Accurate definition of the...

DeepAlloDriver: a deep learning-based strategy to predict cancer driver mutations.

Nucleic acids research
Driver mutations can contribute to the initial processes of cancer, and their identification is crucial for understanding tumorigenesis as well as for molecular drug discovery and development. Allostery regulates protein function away from the functi...