AI Medical Compendium Topic

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

Gene Library

Showing 21 to 30 of 38 articles

Clear Filters

Machine Learning-driven Protein Library Design: A Path Toward Smarter Libraries.

Methods in molecular biology (Clifton, N.J.)
Proteins are small yet valuable biomolecules that play a versatile role in therapeutics and diagnostics. The intricate sequence-structure-function paradigm in the realm of proteins opens the possibility for directly mapping amino acid sequence to fun...

Predicting base editing outcomes with an attention-based deep learning algorithm trained on high-throughput target library screens.

Nature communications
Base editors are chimeric ribonucleoprotein complexes consisting of a DNA-targeting CRISPR-Cas module and a single-stranded DNA deaminase. They enable transition of C•G into T•A base pairs and vice versa on genomic DNA. While base editors have great ...

Optimization of C-to-G base editors with sequence context preference predictable by machine learning methods.

Nature communications
Efficient and precise base editors (BEs) for C-to-G transversion are highly desirable. However, the sequence context affecting editing outcome largely remains unclear. Here we report engineered C-to-G BEs of high efficiency and fidelity, with the seq...

A novel artificial intelligence-based approach for identification of deoxynucleotide aptamers.

PLoS computational biology
The selection of a DNA aptamer through the Systematic Evolution of Ligands by EXponential enrichment (SELEX) method involves multiple binding steps, in which a target and a library of randomized DNA sequences are mixed for selection of a single, nucl...

High-throughput developability assays enable library-scale identification of producible protein scaffold variants.

Proceedings of the National Academy of Sciences of the United States of America
Proteins require high developability-quantified by expression, solubility, and stability-for robust utility as therapeutics, diagnostics, and in other biotechnological applications. Measuring traditional developability metrics is low throughput in na...

Machine learning guided aptamer refinement and discovery.

Nature communications
Aptamers are single-stranded nucleic acid ligands that bind to target molecules with high affinity and specificity. They are typically discovered by searching large libraries for sequences with desirable binding properties. These libraries, however, ...

Machine learning based CRISPR gRNA design for therapeutic exon skipping.

PLoS computational biology
Restoring gene function by the induced skipping of deleterious exons has been shown to be effective for treating genetic disorders. However, many of the clinically successful therapies for exon skipping are transient oligonucleotide-based treatments ...

OPUS-Rota3: Improving Protein Side-Chain Modeling by Deep Neural Networks and Ensemble Methods.

Journal of chemical information and modeling
Side-chain modeling is critical for protein structure prediction since the uniqueness of the protein structure is largely determined by its side-chain packing conformation. In this paper, differing from most approaches that rely on rotamer library sa...

Denoising DNA Encoded Library Screens with Sparse Learning.

ACS combinatorial science
DNA-encoded libraries (DELs) are large, pooled collections of compounds in which every library member is attached to a stretch of DNA encoding its complete synthetic history. DEL-based hit discovery involves affinity selection of the library against ...

Determinants of Base Editing Outcomes from Target Library Analysis and Machine Learning.

Cell
Although base editors are widely used to install targeted point mutations, the factors that determine base editing outcomes are not well understood. We characterized sequence-activity relationships of 11 cytosine and adenine base editors (CBEs and AB...