Hello, this is Abdullah Al Marzan, a molecular biologist and bioinformatician whose research philosophy bridges bench-based science with computational precision. With a strong academic foundation in Biochemistry and Molecular Biology, complemented by extensive experience in clinical data management, diagnostics, and advanced bioinformatics, my journey has been one of deliberate integration—bringing together the rigor of molecular science with the scalability and predictive power of bioinformatics and deep learning. My goal has always been clear: to contribute to precision diagnostics and translational research that can respond swiftly to emerging public health threats and complex disease systems.

My scientific journey began during my undergraduate studies at Shahjalal University of Science and Technology (SUST), Bangladesh, where I received a merit scholarship in recognition of academic excellence. My early exposure to molecular biology, genetics, and immunology laid the conceptual groundwork for my fascination with viruses and host-pathogen interactions. For my BSc research project, I explored the variant-specific deleterious mutations of the SARS-CoV-2 genome and their potential role in immune system modulation. This project introduced me to the relevance of computational biology in understanding complex biological systems, especially during a global pandemic, and encouraged me to deepen my capabilities in this domain.

I completed my Master’s degree in Molecular Biology from the same department at SUST, focusing my thesis on a topic with both public health urgency and clinical complexity: “Identification of Convenient Diagnostic Methods of Premature Rupture of Membrane (PROM) for Rural Areas of Developing Countries.” This multicenter case-control study spanned multiple clinical environments and integrated wet-lab validation, epidemiological modeling, and contextual sensitivity. It not only helped improve PROM detection pathways in low-resource settings but also shaped my interest in translational diagnostics—research that crosses the boundary between academic findings and real-world health solutions.

Over the past few years, I’ve taken on multiple professional roles that collectively reflect the interdisciplinary nature of my expertise. Currently, I serve as a Clinical Data Manager at the Toxicology Society of Bangladesh, where I contribute to a Phase III randomized controlled trial (RCT). My responsibilities span from data management planning and quality assurance to complex statistical modeling using R, SPSS, Python, and SAS. I’ve developed automated systems for real-time data monitoring, optimized eCRFs for improved trial workflows, and created fully functional dashboards that integrate analytics with data integrity alerts. This role sharpened my ability to think not only as a researcher but as a systems architect for clinical-grade data pipelines.

In parallel, I have served as a Medical Research Officer at the Advanced Molecular Lab, President Abdul Hamid Medical College and Hospital in Kishoreganj. This hands-on clinical and molecular research environment has allowed me to lead and support projects ranging from cervical cancer screening and HPV vaccination assessment to PROM biomarker identification and microbiological resistance profiling. I regularly perform diagnostic molecular assays including qRT-PCR, ELISA, and cDNA library preparation, and manage bacterial culture and antibiotic resistance evaluation. Here, I learned that molecular diagnostics is not merely about data or detection—it is about precision, accessibility, and the lives they affect, particularly in under-resourced health systems.

Complementing my clinical and wet-lab responsibilities is my ongoing position as a Remote Bioinformatician at SUST’s Department of Biochemistry and Molecular Biology. My work here focuses on computationally intensive tasks such as deep learning applications in miRNA-RNA secondary structure prediction, molecular dynamics simulation, and high-resolution SARS-CoV-2 variant analysis. I’ve explored ligand binding affinity using YASARA and DESMOND, built Linux-based GROMACS pipelines for protein dynamics simulation, and contributed to multiple projects integrating immunoinformatics, structural biology, and computational drug design. This role has not only refined my technical toolset but reinforced the importance of algorithmic thinking in biological problem solving.

In addition to these formal positions, I’ve been fortunate to gain research experience through roles in several laboratories and centers. At the Covid-19 Testing Lab of Noakhali Science and Technology University, I participated in national-level SARS-CoV-2 sample screening, including wastewater-based epidemiological surveillance. We developed detection pipelines for environmental monitoring and designed public health tracking frameworks still referenced today. In another project at the Environment Lab at SUST, I worked with cyanobacteria-based bioremediation—culturing Blue-Green algae for the absorption of heavy metals from wastewater. These early experiences taught me the value of applied research and its immediate relevance to society and sustainability.

Throughout this journey, publication has been both an outcome and a motivation. I have co-authored over a dozen peer-reviewed journal articles across high-impact outlets such as Scientific Reports, Frontiers in Pharmacology, Environmental Pollution, and Advances in Biomarker Sciences and Technology. Topics have ranged from spike protein evolution, cross-continental mutation mapping, and miRNA profiling to opinion pieces on epidemic preparedness. These publications have enabled me to not only share findings but engage with a broader scientific community committed to molecular innovation and health equity.

In terms of computational fluency, I have consistently expanded my technical skills to meet the demands of my interdisciplinary roles. I am proficient in Python, R, SQL, and SPSS, and have experience with SAS and MATLAB for statistical modeling. I routinely use tools like RNAfold, IEDB, GROMACS, Adobe Illustrator, and GraphPad Prism. I’ve developed predictive models for gene expression and disease susceptibility, implemented dashboards for clinical trials, and trained deep learning models for RNA–protein interaction. My projects now increasingly focus on integrating machine learning with molecular diagnostics—a field I believe is critical for next-generation bioscience innovation.

What drives my work is not only a commitment to technical excellence but a vision for translational impact. As global health faces rising threats—from pandemics and antimicrobial resistance to underdiagnosed cancers and immunological disorders—the need for scalable, affordable, and context-sensitive diagnostic technologies has never been more urgent. I believe that the convergence of molecular biology, deep learning, and clinical data science can offer powerful solutions to these challenges.

My long-term goal is to pursue a fully funded PhD in computational biology, molecular diagnostics, or infectious disease modeling, where I can continue building on this research philosophy. I am especially interested in programs that foster interdisciplinary integration—combining wet-lab investigation with deep learning prediction and public health translation. I seek an academic environment that encourages curiosity, rigor, and collaboration across disciplines and borders.

In summary, my journey from molecules to models has been defined by a deep respect for data, a fascination with biological systems, and a desire to make science actionable. I believe that meaningful diagnostics can transform lives, not just in elite research labs, but in remote clinics, underserved communities, and across global public health systems. Through a doctoral program, I hope to further this mission by contributing to knowledge that is not only innovative but also impactful, precise, and accessible.