Perceptual haptic representation of materials emerges from unsupervised learning as a consequence of efficient encoding of the physical signals at the input of tactile sensory system.

A new automated and unsupervised algorithm, Risk Assessment Population IDentification, identifies risk-stratifying cells in single cell datasets with robust statistical and biological validation.

Multidimensional analysis of data from high-throughput behavioral assays reveals many independent axes of behavioral variation among inbred animals reared in the same environment.

Single-cell analysis of skeletal mouse reveals highly diverse macrophage subgroups associated with diverse functions and age-related changes.

DeepEthogram automatically classifies animal behavior videos into researcher-defined behaviors of interest, saving researcher time and enabling more detailed downstream analysis of behavior.

A novel automated behavior analysis method for Hydra identifies pre-defined and new behavior types, and reveals a stable behavior repertoire.

Image3C is a new image-based analysis of cell population composition for research organisms in which detailed cellular phenotypes are unknown or for which species-specific reagents are not available.

Selfee, a self-supervised learning approach, is designed to extract comprehensive and discriminative features directly from raw videos of animal behaviors which can be used for in-depth analysis.

The proposed Footprinting framework enables approximation of missing cervical cancer epidemiological data and derivation of context-specific impact projection of cervical cancer prevention measures, assisting public health decisions on cervical cancer prevention in India and other countries.

DeepFly3D, a deep learning-based software, measures limb and appendage movements in tethered, behaving Drosophila and enables precise behavioral measurements during neural recordings, stimulation, and other biological experiments.