  Motivation: Over the past decade, network-based approaches have proven useful
in identifying disease modules within the human interactome, often providing
insights into key mechanisms and guiding the quest for therapeutic targets.
This is all the more important, since experimental investigation of potential
gene candidates is an expensive task, thus not always a feasible option. On the
other hand, many sources of biological information exist beyond the interactome
and an important research direction is the design of effective techniques for
their integration. Results: In this work, we introduce the Biological Random
Walks (BRW) approach for disease gene prioritization in the human interactome.
The proposed framework leverages multiple biological sources within an
integrated framework. We perform an extensive, comparative study of BRW's
performance against well-established baselines. Availability and
implementation: All code is publicly available and can be downloaded at
\url{https://github.com/LeoM93/BiologicalRandomWalks}. We used publicly
available datasets, details on their retrieval and preprocessing are provided
in the supplementary material.
