| Literature DB >> 33511368 |
Adriana Tomic1,2, Ivan Tomic3, Levi Waldron4,5, Ludwig Geistlinger4,5, Max Kuhn6, Rachel L Spreng7, Lindsay C Dahora7, Kelly E Seaton7, Georgia Tomaras7, Jennifer Hill1, Niharika A Duggal8, Ross D Pollock9, Norman R Lazarus9, Stephen D R Harridge9, Janet M Lord8,10, Purvesh Khatri2,11, Andrew J Pollard1, Mark M Davis2,12,13.
Abstract
Data analysis and knowledge discovery has become more and more important in biology and medicine with the increasing complexity of biological datasets, but the 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, and 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.Entities:
Keywords: artificial intelligence; autoML; bioinformatics; computational biology; data mining; data science; machine learning; software; systems biology
Year: 2021 PMID： 33511368 PMCID： PMC7815964 DOI： 10.1016/j.patter.2020.100178
Source DB: PubMed Journal: Patterns (N Y) ISSN： 2666-3899