From Machine Learning to Deep Learning Methods in Biology
Tuesday, June 15 at 09:30am (PDT)Tuesday, June 15 at 05:30pm (BST)Wednesday, June 16 01:30am (KST)
Erica Rutter (University of California, Merced, United States), Suzanne Sindi (University of California, Merced, United States)
As biological data becomes more detailed and ubiquitous, statistical and machine learning methods are needed to process and understand relationships in big data or to incorporate this data into existing mechanistic modeling frameworks. Here we present recent advances for machine learning and deep learning methodologies applied to a variety of biological processes, from single cell genomic analysis to population-wide disease spread. Methods of interest include biomedical image analysis via convolutional neural networks (CNNs), learning equations from data, and many more. The methods developed and discussed in this minisymposium span the range from purely statistical and machine learning models to hybridized mechanistic/machine learning models to data-driven mechanistic modeling.