Biomarkers improve the way we treat disease. It is a step in the direction of precision medicine, where treatments are tailored to an individual’s unique biological profile, improving efficacy and reducing side effects. With the help of these biological markers, we can perform early disease detection, precise diagnoses, and develop targeted therapies. However, biomarker discovery remains a complex process, filled with challenges in data management and analysis.
Taking a closer look at this, a significant challenge in the biomarker discovery process lies in the processing of raw data into a format suitable for statistical analysis. Another hurdle is working in silos while using workflows that do not integrate all data types. In an ideal case, this process involves multiple interconnected steps, each shaped by the experimental design and data type of the study. However, many available software solutions are rigid, offering static workflows that cannot easily adapt to the unique requirements of diverse datasets. This limitation becomes particularly problematic in complex experiments where systematically customized approaches are required. The lack of modular and flexible tools often leads to inefficiencies and can hinder the accurate interpretation of data, emphasizing the need for innovative software infrastructures capable of dynamic adaptation.[1]