AI Medical Compendium Journal:
Nucleic acids research

Showing 51 to 60 of 228 articles

Transfer learning for cross-context prediction of protein expression from 5'UTR sequence.

Nucleic acids research
Model-guided DNA sequence design can accelerate the reprogramming of living cells. It allows us to engineer more complex biological systems by removing the need to physically assemble and test each potential design. While mechanistic models of gene e...

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...

Generating, modeling and evaluating a large-scale set of CRISPR/Cas9 off-target sites with bulges.

Nucleic acids research
The CRISPR/Cas9 system is a highly accurate gene-editing technique, but it can also lead to unintended off-target sites (OTS). Consequently, many high-throughput assays have been developed to measure OTS in a genome-wide manner, and their data was us...

DDMut-PPI: predicting effects of mutations on protein-protein interactions using graph-based deep learning.

Nucleic acids research
Protein-protein interactions (PPIs) play a vital role in cellular functions and are essential for therapeutic development and understanding diseases. However, current predictive tools often struggle to balance efficiency and precision in predicting t...

Deep learning for the PSIPRED Protein Analysis Workbench.

Nucleic acids research
The PSIRED Workbench is a long established and popular bioinformatics web service offering a wide range of machine learning based analyses for characterizing protein structure and function. In this paper we provide an update of the recent additions a...

AIUPred: combining energy estimation with deep learning for the enhanced prediction of protein disorder.

Nucleic acids research
Intrinsically disordered proteins and protein regions (IDPs/IDRs) carry out important biological functions without relying on a single well-defined conformation. As these proteins are a challenge to study experimentally, computational methods play im...

iM-Seeker: a webserver for DNA i-motifs prediction and scoring via automated machine learning.

Nucleic acids research
DNA, beyond its canonical B-form double helix, adopts various alternative conformations, among which the i-motif, emerging in cytosine-rich sequences under acidic conditions, holds significant biological implications in transcription modulation and t...

ProTox 3.0: a webserver for the prediction of toxicity of chemicals.

Nucleic acids research
Interaction with chemicals, present in drugs, food, environments, and consumer goods, is an integral part of our everyday life. However, depending on the amount and duration, such interactions can also result in adverse effects. With the increase in ...

Deep-PK: deep learning for small molecule pharmacokinetic and toxicity prediction.

Nucleic acids research
Evaluating pharmacokinetic properties of small molecules is considered a key feature in most drug development and high-throughput screening processes. Generally, pharmacokinetics, which represent the fate of drugs in the human body, are described fro...

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...