Abstract: Intervertebral disc degeneration (IDD) is a common musculoskeletal disorder that can cause back, or neck discomfort and chronic pain associated with aging. The degeneration of the nucleus pulposus (NP) cells, the central component of the intervertebral disc, leads to dehydration, loss of disc height, disc distortion, and segmental instability. Identifying biomarkers for IDD can aid in diagnosis, monitoring, and developing precise treatments for the condition. Gene expression data from young and old, male, and female rat intervertebral disc (IVD) tissue types, along with known extracellular matrix-related genes from related human tissues were analyzed to discover potential biomarkers. Machine learning techniques of Logistic Regression, Support Vector Machines, Random Forest, Naïve Bayes, and Rule Learner were utilized to analyze the genes and discriminate between the nucleus pulposus (NP) and annulus fibrosus (AF) tissue types. This study presents the feasibility of a knowledge Augmented Rule Learner (ARL) to provide accurate and interpretable models, which can be useful as an efficient integrative biomarker discovery tool for diagnosing and treating IDD in precision medicine. The dataset contains 16,378 genes and 38 samples distributed across tissue types, gender, and age. Further research is necessary to validate the identified biomarkers and understand their role in the disease process.

Bio: Bamidele Ajisogun is a PhD student in the Intelligent Systems Program.  He's interested in exploring deep learning and machine learning algorithms using symbolic, probabilistic, and hybrid approaches to solve bioinformatics problems including genomics, biomarker discovery and disease classification for pattern recognition and biomedical evidence in precision medicine.

RSVP for Zoom information: https://pitt.co1.qualtrics.com/jfe/form/SV_byggt3iZWgJ956S