SIMON: open-source knowledge discovery platform

Adriana Tomić; Ivan Tomic; Levi Waldron; Ludwig Geistlinger; Max Kühn; Rachel L. Spreng; Lindsay C. Dahora; Kelly E. Seaton; Georgia D. Tomaras; Jennifer W Hill; Niharika Arora Duggal; Ross D. Pollock; Norman R. Lazarus; Stephen D. R. Harridge; Janet M. Lord; Purvesh Khatri; Andrew John Pollard; Mark M. Davis

doi:10.1016/j.patter.2020.100178

Systems BiologyPatternsJan 1, 2021

Peer-reviewed journal article

SIMON: open-source knowledge discovery platform

Adriana Tomić iD, Ivan Tomic iD, Levi Waldron iD, Ludwig Geistlinger iD, Max Kühn iD, Rachel L. Spreng iD, Lindsay C. Dahora iD, Kelly E. Seaton iD, Georgia D. Tomaras iD, Jennifer W Hill iD, Niharika Arora Duggal iD, Ross D. Pollock iD, Norman R. Lazarus iD, Stephen D. R. Harridge iD, Janet M. Lord iD, Purvesh Khatri iD, Andrew John Pollard iD, Mark M. Davis iD

Abstract

Data analysis and knowledge discovery has become more and more important in biology and medicine with the increasing complexity of the biological datasets, but necessarily sophisticated programming skills and in-depth understanding of algorithms needed pose barriers to most biologists and clinicians to perform such research. We have developed a modular open-source software SIMON to facilitate the application of 180+ state-of-the-art machine learning algorithms to high-dimensional biomedical data. With an easy to use graphical user interface, standardized pipelines, automated approach for machine learning and other statistical analysis methods, SIMON helps to identify optimal algorithms and provides a resource that empowers non-technical and technical researchers to identify crucial patterns in biomedical data.

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