AI Medical Compendium Journal:
Nature methods

Showing 11 to 20 of 183 articles

Analysis of behavioral flow resolves latent phenotypes.

Nature methods
The accurate detection and quantification of rodent behavior forms a cornerstone of basic biomedical research. Current data-driven approaches, which segment free exploratory behavior into clusters, suffer from low statistical power due to multiple te...

Brain-wide neural recordings in mice navigating physical spaces enabled by robotic neural recording headstages.

Nature methods
Technologies that can record neural activity at cellular resolution at multiple spatial and temporal scales are typically much larger than the animals that are being recorded from and are thus limited to recording from head-fixed subjects. Here we ha...

Effective genome editing with an enhanced ISDra2 TnpB system and deep learning-predicted ωRNAs.

Nature methods
Transposon (IS200/IS605)-encoded TnpB proteins are predecessors of class 2 type V CRISPR effectors and have emerged as one of the most compact genome editors identified thus far. Here, we optimized the design of Deinococcus radiodurans (ISDra2) TnpB ...

Guiding questions to avoid data leakage in biological machine learning applications.

Nature methods
Machine learning methods for extracting patterns from high-dimensional data are very important in the biological sciences. However, in certain cases, real-world applications cannot confirm the reported prediction performance. One of the main reasons ...

Language models for biological research: a primer.

Nature methods
Language models are playing an increasingly important role in many areas of artificial intelligence (AI) and computational biology. In this primer, we discuss the ways in which language models, both those based on natural language and those based on ...

Applying interpretable machine learning in computational biology-pitfalls, recommendations and opportunities for new developments.

Nature methods
Recent advances in machine learning have enabled the development of next-generation predictive models for complex computational biology problems, thereby spurring the use of interpretable machine learning (IML) to unveil biological insights. However,...

Smart parallel automated cryo-electron tomography.

Nature methods
In situ cryo-electron tomography enables investigation of macromolecules in their native cellular environment. Samples have become more readily available owing to recent software and hardware advancements. Data collection, however, still requires an ...

Geometric deep learning of protein-DNA binding specificity.

Nature methods
Predicting protein-DNA binding specificity is a challenging yet essential task for understanding gene regulation. Protein-DNA complexes usually exhibit binding to a selected DNA target site, whereas a protein binds, with varying degrees of binding sp...

Contextual AI models for single-cell protein biology.

Nature methods
Understanding protein function and developing molecular therapies require deciphering the cell types in which proteins act as well as the interactions between proteins. However, modeling protein interactions across biological contexts remains challen...

Keypoint-MoSeq: parsing behavior by linking point tracking to pose dynamics.

Nature methods
Keypoint tracking algorithms can flexibly quantify animal movement from videos obtained in a wide variety of settings. However, it remains unclear how to parse continuous keypoint data into discrete actions. This challenge is particularly acute becau...