Unsupervised deep learning on biomedical data with BoltzmannMachines.jl
Stefan Lenz, Moritz Hess, Harald Binder · bioRxiv (Cold Spring Harbor Laboratory) · 2019
Abstract Deep Boltzmann machines (DBMs) are models for unsupervised learning in the field of artificial intelligence, promising to be useful for dimensionality reduction and pattern detection in clinical and genomic data. Multimodal and partitioned DBMs alleviate the problem of small sample sizes and make it possible to combine different input data types in one DBM model. We present the package “BoltzmannMachines” for the Julia programming language, which makes this model class available for practical use in working with biomedical data. Availability Notebook with example data: http://github.com/stefan-m-lenz/BMs4BInf2019 Julia package: http://github.com/stefan-m-lenz/BoltzmannMachines.jl