Abstract
SummaryData 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.
Highlights
Over the past several years, due to the technological breakthroughs in genome sequencing,[1] high-dimensional flow cytometry,[2,3,4] mass cytometry,[5,6] and multiparameter microscopy,[7,8] the amount and complexity of biological data have become increasingly intractable and it is no longer feasible to extract knowledge without using sophisticated computer algorithms
We developed SIMON (Sequential Iterative Modeling ‘‘Over Night’’), a free and open-source software for application of Machine learning (ML) in life sciences that facilitates production of high-performing ML models and allows researchers to focus on the knowledge discovery process
SIMON provides a user-friendly, uniform interface for building and evaluating predictive models using a variety of ML algorithms
Summary
Over the past several years, due to the technological breakthroughs in genome sequencing,[1] high-dimensional flow cytometry,[2,3,4] mass cytometry,[5,6] and multiparameter microscopy,[7,8] the amount and complexity of biological data have become increasingly intractable and it is no longer feasible to extract knowledge without using sophisticated computer algorithms. The analysis of massive datasets and extraction of knowledge using ML require knowledge of many different computational libraries for data pre-processing and cleaning, data partitioning, model building and tuning, evaluation of the performance of the model, and minimizing overfitting.[11] Tools to achieve these tasks have been mainly developed in either R Since those libraries do not have a graphical user interface, usage requires extensive programming experience and general knowledge of R or Python, making them inaccessible for many life science researchers. There is an increased effort to harmonize those libraries and develop a software that will facilitate application of ML in life sciences
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