The spectral map framework, as described in the context, currently operates on unbiased molecular dynamics simulations. To extend its applicability to biased data obtained through enhanced sampling techniques, a reweighting algorithm can be integrated into the spectral map framework. This would involve adapting the anisotropic diffusion kernels used in the construction of the Markov transition matrices to account for the bias introduced by enhanced sampling methods. By applying reweighting, the transition probabilities can be adjusted to reflect the true equilibrium distribution, allowing the spectral map to learn slow collective variables (CVs) from biased datasets effectively. This approach would enable the spectral map to capture the dynamics of complex molecular processes even when the data is not uniformly sampled, thus enhancing its utility in studying rare events and transitions in protein folding and other biochemical systems.