AI Medical Compendium Journal:
eLife

Showing 31 to 40 of 136 articles

Digital wearable insole-based identification of knee arthropathies and gait signatures using machine learning.

eLife
Gait is impaired in musculoskeletal conditions, such as knee arthropathy. Gait analysis is used in clinical practice to inform diagnosis and monitor disease progression or intervention response. However, clinical gait analysis relies on subjective vi...

Emergence of brain-like mirror-symmetric viewpoint tuning in convolutional neural networks.

eLife
Primates can recognize objects despite 3D geometric variations such as in-depth rotations. The computational mechanisms that give rise to such invariances are yet to be fully understood. A curious case of partial invariance occurs in the macaque face...

Genome-scale annotation of protein binding sites via language model and geometric deep learning.

eLife
Revealing protein binding sites with other molecules, such as nucleic acids, peptides, or small ligands, sheds light on disease mechanism elucidation and novel drug design. With the explosive growth of proteins in sequence databases, how to accuratel...

Neural network emulation of the human ventricular cardiomyocyte action potential for more efficient computations in pharmacological studies.

eLife
Computer models of the human ventricular cardiomyocyte action potential (AP) have reached a level of detail and maturity that has led to an increasing number of applications in the pharmaceutical sector. However, interfacing the models with experimen...

Protein language model-embedded geometric graphs power inter-protein contact prediction.

eLife
Accurate prediction of contacting residue pairs between interacting proteins is very useful for structural characterization of protein-protein interactions. Although significant improvement has been made in inter-protein contact prediction recently, ...

Passive exposure to task-relevant stimuli enhances categorization learning.

eLife
Learning to perform a perceptual decision task is generally achieved through sessions of effortful practice with feedback. Here, we investigated how passive exposure to task-relevant stimuli, which is relatively effortless and does not require feedba...

Designing optimal behavioral experiments using machine learning.

eLife
Computational models are powerful tools for understanding human cognition and behavior. They let us express our theories clearly and precisely and offer predictions that can be subtle and often counter-intuitive. However, this same richness and abili...

identifies gender disparities in scientific peer review.

eLife
The peer review process is a critical step in ensuring the quality of scientific research. However, its subjectivity has raised concerns. To investigate this issue, I examined over 500 publicly available peer review reports from 200 published neurosc...

Model discovery to link neural activity to behavioral tasks.

eLife
Brains are not engineered solutions to a well-defined problem but arose through selective pressure acting on random variation. It is therefore unclear how well a model chosen by an experimenter can relate neural activity to experimental conditions. H...

Rapid protein stability prediction using deep learning representations.

eLife
Predicting the thermodynamic stability of proteins is a common and widely used step in protein engineering, and when elucidating the molecular mechanisms behind evolution and disease. Here, we present RaSP, a method for making rapid and accurate pred...