AI Medical Compendium Journal:
Nature biotechnology

Showing 11 to 20 of 74 articles

A universal deep-learning model for zinc finger design enables transcription factor reprogramming.

Nature biotechnology
CysHis zinc finger (ZF) domains engineered to bind specific target sequences in the genome provide an effective strategy for programmable regulation of gene expression, with many potential therapeutic applications. However, the structurally intricate...

Predicting prime editing efficiency and product purity by deep learning.

Nature biotechnology
Prime editing is a versatile genome editing tool but requires experimental optimization of the prime editing guide RNA (pegRNA) to achieve high editing efficiency. Here we conducted a high-throughput screen to analyze prime editing outcomes of 92,423...

Discovery of drug-omics associations in type 2 diabetes with generative deep-learning models.

Nature biotechnology
The application of multiple omics technologies in biomedical cohorts has the potential to reveal patient-level disease characteristics and individualized response to treatment. However, the scale and heterogeneous nature of multi-modal data makes int...

Rationalized deep learning super-resolution microscopy for sustained live imaging of rapid subcellular processes.

Nature biotechnology
The goal when imaging bioprocesses with optical microscopy is to acquire the most spatiotemporal information with the least invasiveness. Deep neural networks have substantially improved optical microscopy, including image super-resolution and restor...

Single-sequence protein structure prediction using a language model and deep learning.

Nature biotechnology
AlphaFold2 and related computational systems predict protein structure using deep learning and co-evolutionary relationships encoded in multiple sequence alignments (MSAs). Despite high prediction accuracy achieved by these systems, challenges remain...

Prediction of protein-ligand binding affinity from sequencing data with interpretable machine learning.

Nature biotechnology
Protein-ligand interactions are increasingly profiled at high throughput using affinity selection and massively parallel sequencing. However, these assays do not provide the biophysical parameters that most rigorously quantify molecular interactions....

Designing sensitive viral diagnostics with machine learning.

Nature biotechnology
Design of nucleic acid-based viral diagnostics typically follows heuristic rules and, to contend with viral variation, focuses on a genome's conserved regions. A design process could, instead, directly optimize diagnostic effectiveness using a learne...

Identification of antimicrobial peptides from the human gut microbiome using deep learning.

Nature biotechnology
The human gut microbiome encodes a large variety of antimicrobial peptides (AMPs), but the short lengths of AMPs pose a challenge for computational prediction. Here we combined multiple natural language processing neural network models, including LST...

Using deep learning to annotate the protein universe.

Nature biotechnology
Understanding the relationship between amino acid sequence and protein function is a long-standing challenge with far-reaching scientific and translational implications. State-of-the-art alignment-based techniques cannot predict function for one-thir...

A knowledge graph to interpret clinical proteomics data.

Nature biotechnology
Implementing precision medicine hinges on the integration of omics data, such as proteomics, into the clinical decision-making process, but the quantity and diversity of biomedical data, and the spread of clinically relevant knowledge across multiple...