AI Medical Compendium Journal:
Nature methods

Showing 31 to 40 of 183 articles

Improving deep learning protein monomer and complex structure prediction using DeepMSA2 with huge metagenomics data.

Nature methods
Leveraging iterative alignment search through genomic and metagenome sequence databases, we report the DeepMSA2 pipeline for uniform protein single- and multichain multiple-sequence alignment (MSA) construction. Large-scale benchmarks show that DeepM...

DeepMainmast: integrated protocol of protein structure modeling for cryo-EM with deep learning and structure prediction.

Nature methods
Three-dimensional structure modeling from maps is an indispensable step for studying proteins and their complexes with cryogenic electron microscopy. Although the resolution of determined cryogenic electron microscopy maps has generally improved, the...

Automated neuron tracking inside moving and deforming C. elegans using deep learning and targeted augmentation.

Nature methods
Reading out neuronal activity from three-dimensional (3D) functional imaging requires segmenting and tracking individual neurons. This is challenging in behaving animals if the brain moves and deforms. The traditional approach is to train a convoluti...

AlphaFold predictions are valuable hypotheses and accelerate but do not replace experimental structure determination.

Nature methods
Artificial intelligence-based protein structure prediction methods such as AlphaFold have revolutionized structural biology. The accuracies of these predictions vary, however, and they do not take into account ligands, covalent modifications or other...

Uncovering developmental time and tempo using deep learning.

Nature methods
During animal development, embryos undergo complex morphological changes over time. Differences in developmental tempo between species are emerging as principal drivers of evolutionary novelty, but accurate description of these processes is very chal...

Genetically encoded multimeric tags for subcellular protein localization in cryo-EM.

Nature methods
Cryo-electron tomography (cryo-ET) allows for label-free high-resolution imaging of macromolecular assemblies in their native cellular context. However, the localization of macromolecules of interest in tomographic volumes can be challenging. Here we...

Artificial intelligence-enabled quantitative phase imaging methods for life sciences.

Nature methods
Quantitative phase imaging, integrated with artificial intelligence, allows for the rapid and label-free investigation of the physiology and pathology of biological systems. This review presents the principles of various two-dimensional and three-dim...

CryoREAD: de novo structure modeling for nucleic acids in cryo-EM maps using deep learning.

Nature methods
DNA and RNA play fundamental roles in various cellular processes, where their three-dimensional structures provide information critical to understanding the molecular mechanisms of their functions. Although an increasing number of nucleic acid struct...

Deep learning-driven adaptive optics for single-molecule localization microscopy.

Nature methods
The inhomogeneous refractive indices of biological tissues blur and distort single-molecule emission patterns generating image artifacts and decreasing the achievable resolution of single-molecule localization microscopy (SMLM). Conventional sensorle...

D-LMBmap: a fully automated deep-learning pipeline for whole-brain profiling of neural circuitry.

Nature methods
Recent proliferation and integration of tissue-clearing methods and light-sheet fluorescence microscopy has created new opportunities to achieve mesoscale three-dimensional whole-brain connectivity mapping with exceptionally high throughput. With the...