Unsupervised deep learning on biomedical data with BoltzmannMachines.jl

Lenz, Stefan; Hess, Moritz; Binder, Harald

doi:10.1101/578252

Cited by 8 publications

(4 citation statements)

References 3 publications

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“…VAEs and DBMs are implemented in Julia ( Bezanson et al , 2017 ), using the Flux package ( Innes, 2018 ) for VAEs and the BoltzmannMachines package ( Lenz et al , 2019 ) for DBMs.…”

Section: Methodsmentioning

confidence: 99%

Exploring generative deep learning for omics data using log-linear models

2020

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Abstract Motivation Following many successful applications to image data, deep learning is now also increasingly considered for omics data. In particular, generative deep learning not only provides competitive prediction performance, but also allows for uncovering structure by generating synthetic samples. However, exploration and visualization is not as straightforward as with image applications. Results We demonstrate how log-linear models, fitted to the generated, synthetic data can be employed to extract patterns from omics data, learned by deep generative techniques. Specifically, interactions between latent representations learned by the approaches and generated synthetic data are used to determine sets of joint patterns. Distances of patterns with respect to the distribution of latent representations are then visualized in low-dimensional coordinate systems, e.g., for monitoring training progress. This is illustrated with simulated data and subsequently with cortical single cell gene expression data. Using different kinds of deep generative techniques, specifically variational autoencoders and deep Boltzmann machines, the proposed approach highlights how the techniques uncover underlying structure. It facilitates the real world use of such generative deep learning techniques in order to gain biological insights from omics data. Availability The code for the approach as well as an accompanying Jupyter notebook, which illustrates the application of our approach, is available via the GitHub repository: https://github.com/ssehztirom/Exploring-generative-deep-learning-for-omics-data-by-using-log-linear-models Supplementary information Supplementary data are available at Bioinformatics online.

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“…VAEs and DBMs are implemented in Julia ( Bezanson et al , 2017 ), using the Flux package ( Innes, 2018 ) for VAEs and the BoltzmannMachines package ( Lenz et al , 2019 ) for DBMs.…”

Section: Methodsmentioning

confidence: 99%

Exploring generative deep learning for omics data using log-linear models

2020

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“…The scDBM implementation is based on the Julia package 'BoltzmannMachines.jl' 41 and extends the packages' scope to scRNA-seq data which is available at https://github.com/MTreppner/scDBM.jl.…”

Section: Data Description and Processingmentioning

confidence: 99%

Making many out of few: deep generative models for single-cell RNA-sequencing data

Treppner

Salas-Bastos

Hess

et al. 2020

Preprint

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Motivation: When designing experiments, it is advised to start with a small pilot study for determining the sample size of full-scale investigations. Deep learning techniques for single-cell RNA-sequencing data that can uncover low-dimensional representations of expression patterns within cells could be useful also with pilot data. Here, we examine the ability of these methods to learn the structure of data from a small pilot study and generate synthetic expression datasets useful for planning full-scale experiments. Results: We investigate two deep generative modeling approaches. First, we consider single-cell variational inference (scVI) in two variants, either generating samples from the posterior distribution, which is the standard approach, or from the prior distribution. Second, we propose single-cell deep Boltzmann machines (scDBM), which might be particularly suitable for small datasets. When considering the similarity of clustering results on synthetic data to ground-truth clustering, we find that scV Iposterior exhibits high variability. Expression patterns from scV Iprior and scDBM perform better. All approaches show mixed results for cell types with different abundance by sometimes overrepresenting highly abundant cell types and missing less abundant cell types. Taking such tradeoffs in performance into account, we conclude that for making inference from a small pilot study to a larger experiment, it is advantageous to use scV Iprior or scDBM, as scV Iposterior produces signals that are not justified by the original data. The proposed scDBM seems to have an advantage for small pilot datasets. Overall, the results show that generative deep learning approaches might be valuable for supporting the design of scRNA-seq experiments.

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“…The increased availability of healthcare data provides scientists with a fresh opportunity to improve existing approaches for further comprehensive clinical analysis (Sarwinda et al, 2020). In the medical profession, machine learning and deep learning are commonly utilised technologies for analyzing biomedical data (Park et al, 2018) (Das et al, 2020) (Baldi, 2018) (Lenz et al, 2019).…”

Section: Introductionmentioning

confidence: 99%

Machine Learning-based Lung and Colon Cancer Detection using Deep Feature Extraction and Ensemble Learning

Talukder¹,

Islam²,

Uddin³

et al. 2022

Preprint

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Cancer is a fatal disease caused by a combination of genetic diseases and a variety of biochemical abnormalities. Lung and colon cancer have emerged as two of the leading causes of death and disability in humans. The histopathological detection of such malignancies is usually the most important component in determining the best course of action.Early detection of the ailment on either front considerably decreases the likelihood of mortality. Machine learning and deep learning techniques can be utilized to speed up such cancer detection, allowing researchers to study a large number of patients in a much shorter amount of time and at a lower cost. In this research work, we introduced a hybrid ensemble feature extraction model to efficiently identify lung and colon cancer. It integrates deep feature extraction and ensemble learning with high-performance filtering for cancer image datasets. The model is evaluated on histopathological (LC25000) lung and colon datasets. According to the study findings, our hybrid model can detect lung, colon, and (lung and colon) cancer with accuracy rates of 99.05%, 100%, and 99.30%, respectively. The study's findings show that our proposed strategy outperforms existing models significantly. Thus, these models could be applicable in clinics to support the doctor in the diagnosis of cancers.

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Unsupervised deep learning on biomedical data with BoltzmannMachines.jl

Cited by 8 publications

References 3 publications

Exploring generative deep learning for omics data using log-linear models

Exploring generative deep learning for omics data using log-linear models

Making many out of few: deep generative models for single-cell RNA-sequencing data

Machine Learning-based Lung and Colon Cancer Detection using Deep Feature Extraction and Ensemble Learning

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