PhD student Sugam Budhraja and the interdisciplinary team at KEDRI and NTU Singapore jointly published in Briefings in Bioinformatics (Impact factor 13.9 updated in 2023)

In the rapidly evolving domain of bioinformatics, breakthroughs in sequencing technologies are heralding an era of genetic revelations. But with this deluge of genetic data comes a pressing challenge: navigating the large amount of information to pinpoint precise markers of health and disease - biomarkers

AUT PhD student Sugam Budhraja and his interdisciplinary team at KEDRI and NTU Singapore have published a paper in the reputable journal of Briefings in Bioinformatics which has an impact factor of 13.9 (updated in 2023). The paper is led by Sugam as the first author, titled "Filter and Wrapper Stacking Ensemble (FWSE): A Robust Approach for Reliable Biomarker Discovery in High-Dimensional Omics Data". The research introduced an innovative approach to biomarker discovery in high-dimensional omics datasets. Biomarkers are indicators that play a key role in diagnosing diseases, assessing efficacy of treatments, and predicting future health risks. The set of potential markers identified by the proposed novel FWSE method demonstrated high accuracy, reproducibility, and biological significance on datasets related to mental health and cancer. FWSE employs a two-tiered strategy, it first filters out irrelevant/noisy features using an ensemble of filter feature selection methods, and then strategically ranks the most promising features using an ensemble of wrapper feature selection methods.

This research represents an important step forward in the application of AI to solve pressing challenges in medicine and biology. In an age where precision medicine is emerging as a cornerstone for medical interventions, methodologies that enhance the reliability and significance of biomarker discovery not only drive scientific advancements but also promise improved patient outcomes. Enhanced tools for biomarker discovery are paving the way for patient care that is personalized to the unique genetic makeup of individuals.