mlpy: Machine Learning Python

We introduce here mlpy, a library providing access to a wide spectrum of machine learning methods implemented in Python, which has proven to be an effective environment for building scientific oriented tools (Pérez et al., 2011). Although planned for general purpose applications, mlpy has the computational biology in general, and the functional genomics modeling in particular, as the elective application fields. As a major applications example, we use mlpy methods to implement molecular profiling experiments that need to warrant study reproducibility(Ioannidis et al., 2009) and flawless results(Ambroise and McLachlan, 2002). This task requires the availability of highly modular tools allowing the practioners to build an adequate workflow for the task at hand following authoritative guidelines (The MicroArray Quality Control (MAQC) Consortium, 2010). Such workflow involves a complex sequence of steps, both in the development and in the validation phases, starting from the upstream preprocessing algorithms to the downstream predictive analysis, repeated several times to accommodate the resampling schema. The dimension of highthroughtput data involved (thousands of samples described by millions of features) and the large number of replicates needed to control bias effects make also efficiency an essential requirement.