Gautam S. Sabnis

Genotype-dependent behavioral signatures of opioid withdrawal revealed by automated behavioral quantification

This study uses automated behavioral quantification to show that opioid withdrawal behaviors vary with genetic background, but severity can still be assessed within strains.

An automated machine vision-based index for Rett Syndrome provides a generalizable framework for modeling rare disease therapeutics

This paper develops an automated machine vision-based index for Rett Syndrome that improves preclinical phenotyping by detecting disease-relevant behaviors and reducing animal use.

Integrating supervised and unsupervised machine learning for behavior segmentation reveals latent frailty signatures and improves aging clocks in isogenic and outbred mice

The paper combines supervised and unsupervised machine learning to improve aging predictions in mice by identifying latent frailty signatures.

Visual detection of seizures in mice using supervised machine learning

This study uses supervised machine learning to automatically quantify seizure severity from video data in mice, enabling high-throughput and standardized seizure scoring.

Machine vision based frailty assessment for genetically diverse mice

The paper extends a machine vision-based frailty assessment tool to genetically diverse mice, enabling accurate prediction of frailty, age, and lifespan proportions using video data.

Highly Accurate and Precise Determination of Mouse Mass Using Computer Vision

This paper demonstrates a computer vision method to estimate mouse body mass from video data with 4.8% error, enabling noninvasive and continuous monitoring.

Action detection using a neural network elucidates the genetics of mouse grooming behavior

The paper develops a neural network for automated mouse grooming behavior detection and uses it to study the genetic basis of grooming and its links to psychiatric traits.