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
Footnotes
↵* lenz{at}imbi.uni-freiburg.de
Copyright
The copyright holder for this preprint is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. All rights reserved. No reuse allowed without permission.
Posted March 20, 2019.
