Diffusion enables integration of heterogeneous data and user-driven learning in a desktop knowledge-base.

ABSTRACT: Integrating reference datasets (e.g. from high-throughput experiments) with unstructured and manually-assembled information (e.g. notes or comments from individual researchers) has the potential to tailor bioinformatic analyses to specific needs and to lead to new insights. However, developing bespoke analysis pipelines from scratch is time-consuming, and general tools for exploring such heterogeneous data are not available. We argue that by treating all data as text, a knowledge-base can accommodate a range of bioinformatic data types and applications. We show that a database coupled to nearest-neighbor algorithms can address common tasks such as gene-set analysis as well as specific tasks such as ontology translation. We further show that a mathematical transformation motivated by diffus

SUBMITTER: Konopka T 

PROVIDER: S-EPMC8382188 | biostudies-literature | 2021 Aug

REPOSITORIES: biostudies-literature