Data-Driven Supervised Learning for Life Science Data

Data-Driven Supervised Learning for Life Science Data

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Life science data are often encoded in a non-standard way by means 15Amp Mains Plug Top of alpha-numeric sequences, graph representations, numerical vectors of variable length, or other formats.Domain-specific or data-driven similarity measures like alignment functions have been employed with great success.The vast majority of more complex data analysis algorithms require fixed-length vectorial input data, asking for substantial preprocessing of life science data.Data-driven measures are widely ignored in favor of simple encodings.

These preprocessing steps are not always easy to perform nor particularly effective, Tape Measurers with a potential loss of information and interpretability.We present some strategies and concepts of how to employ data-driven similarity measures in the life science context and other complex biological systems.In particular, we show how to use data-driven similarity measures effectively in standard learning algorithms.