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Biology subjects

Ashouri, S.

Publications and source records attributed to Ashouri, S..

2 recordsLinked to original sources

Phased genome assemblies and pangenome graphs of human populations of Japan and Saudi Arabia

The selection of a reference sequence in genome analysis is critical, as it serves as the foundation for all downstream analyses. Recently, the pangenome graph has been proposed as a data model that incorporates haplotypes from multiple individuals. Here we present JaSaPaGe, a pangenome graph reference for Saudi Arabian and Japanese populations, both of which have been significantly underrepresented in previous genomic studies. We constructed JaSaPaGe from high-quality phased diploid assemblies which were made utilizing PacBio high-fidelity long reads, Nanopore long reads, and Hi-C short reads of 9 Saudi and 10 Japanese individuals. Quality evaluation of the pangenome graph by variant calling showed that our pangenome outperformed earlier linear reference genomes (GRCh38 and T2T-CHM13) and showed comparable performance to the pangenome graph provided by the Human Pangenome Reference Consortium (HPRC), with more variants found in Japanese and Saudi samples using their population-specific pangenomes. This pangenome reference will serve as a valuable resource for both the research and clinical communities in Japan and Saudi Arabia.

genomics↗

Using a Motion Sensor to Categorize Low Back Pain Patients: A Machine Learning Approach

Low back pain (LBP) remains a critical health issue impacting literally millions of people worldwide. Currently, clinical practitioners rely on subjective measures such as the STarT Back Screening Tool to categorize LBP patients, which then informs specific treatment regimens. This study sought to develop a machine learning model to classify LBP patients into different groups according to kinematic data. Specifically, an inertial measurement unit (IMU) was attached to each patients chest while he performed trunk flexion/extension motions at a self-selected pace. Machine learning algorithms such as support vector machine (SVM) and multi-layer perceptron (MLP) were implemented to evaluate the efficiency of the models. The results showed that the kinematic data we obtained could be used to categorize the patients into two groups: high vs. low-medium risk. We achieved accuracy levels of ~75% and 60% for SVM and MLP, respectively. Additionally, among a range of variables detailed herein, we determined that time-scaled IMU signal resulted in the highest accuracy. Our findings support the use of body-motion measures in developing prognosis tools for healthcare applications. Our results could help overcome the need for objective clinic-based diagnosis approaches, which in turn would lead to assigning better treatment approaches and rehabilitation services for LBP sufferers.

bioengineering↗