Biology subjects

Arab, M.

Publications and source records attributed to Arab, M..

2 recordsLinked to original sources

bioRxiv2024

ARBEL: A Machine Learning Tool with Light-Based Image Analysis for Automatic Classification of 3D Pain Behaviors

A detailed analysis of pain-related behaviors in rodents is essential for exploring both the mechanisms of pain and evaluating analgesic efficacy. With the advancement of pose-estimation tools, automatic single-camera video animal behavior pipelines are growing and integrating rapidly into quantitative behavioral research. However, current existing algorithms do not consider an animals body-part contact intensity with- and distance from- the surface, a critical nuance for measuring certain pain-related responses like paw withdrawals ( flinching) with high accuracy and interpretability. Quantifying these bouts demands a high degree of attention to body part movement and currently relies on laborious and subjective human visual assessment. Here, we introduce a supervised machine learning algorithm, ARBEL: Automated Recognition of Behavior Enhanced with Light, that utilizes a combination of pose estimation together with a novel light-based analysis of body part pressure and distance from the surface, to automatically score pain-related behaviors in freely moving mice in three dimensions. We show the utility and accuracy of this algorithm for capturing a range of pain-related behavioral bouts using a bottom-up animal behavior platform, and its application for robust drug-screening. It allows for rapid objective pain behavior scoring over extended periods with high precision. This open-source algorithm is adaptable for detecting diverse behaviors across species and experimental platforms.

neuroscience↗

bioRxiv2022

Integrative chromatin domain annotation throughgraph embedding of Hi-C data

MotivationThe organization of the genome into domains plays a central role in gene expression and other cellular activities. Researchers identify genomic domains mainly through two views: 1D functional assays such as ChIP-seq, and chromatin conformation assays such as Hi-C. Fully understanding domains requires integrative modeling that combines these two views. However, the predominant form of integrative modeling uses segmentation and genome annotation (SAGA) along with the rigid assumption that loci in contact are more likely to share the same domain type, which is not necessarily true for epigenomic domain types and genome-wide chromatin interactions. ResultsHere, we present an integrative approach that annotates domains using both 1D functional genomic signals and Hi-C measurements of genome-wide 3D interactions without the use of a pairwise prior. We do so by using a graph embedding to learn structural features corresponding to each genomic region, then inputting learned structural features along with functional genomic signals to a SAGA algorithm. We show that our domain types recapitulate well-known subcompartments with an additional granularity that distinguishes a combination of the spatial and functional states of the genomic regions. In particular, we identified a division of the previously-identified A2 subcompartment such that the divided domain types have significantly varying expression levels. Availabilityhttps://github.com/nedashokraneh/IChDA Contactmaxwl@sfu.ca Supplementary information

genomics↗