Biomathematics Oriented Machine Learning System for Reconstructing Temporal Profiles of Biological or Clinical Markers

Time series reconstruction algorithms are widely used to create temporal profiles from data series. However, in many clinical fields, e.g., viral kinetics, the data is noisy and sparse, making it difficult to use standard algorithms. We developed PROFILASE, which combines advanced multi-objective genetic algorithm search with machine learning architecture to harvest experts' decision-making considerations. Furthermore, PROFILASE implements additional scoring considerations, more biological in nature, thus further exploits domain expertise. We tested our system against a standard bottom-up algorithm by reconstruction of time series sparsely sampled with noise from simulated profiles. PROFILASE obtained RMS distance 2.5 fold lower (P