PyBrain statistics

To access cutting-edge analytics on consensus tools, life science contexts and associated fields, you will need to subscribe to our premium service.


Citations per year

Citations chart

Popular tool citations

chevron_left Libraries/frameworks chevron_right
Popular tools chart

Tool usage distribution map

Tool usage distribution map

Associated diseases

Associated diseases

PyBrain specifications


Unique identifier OMICS_23787
Name PyBrain
Software type Package/Module
Interface Command line interface
Restrictions to use None
Operating system Unix/Linux
Programming languages Python
License BSD 3-clause “New” or “Revised” License
Computer skills Advanced
Version 0.3.3
Stability Stable
Source code URL
Maintained Yes



Add your version



  • person_outline Tom Schaul <>
  • person_outline Justin Bayer <>
  • person_outline Daan Wierstra <>
  • person_outline Yi Sun <>
  • person_outline Martin Felder <>

PyBrain in publications

PMCID: 5769116
PMID: 29349278
DOI: 10.1016/j.ssmph.2017.11.008

[…] were done using cv.glmnet from the package glmnet and random forests were done using randomforest from the package randomforest (, ). neural networks were constructed using python version 2.7.5 and pybrain (, )., and present, for each model or machine learning algorithm, the mean and range of the rmse across the cross-validation folds.fig. 1fig. 1, the minimal and theory-based models perform […]

PMCID: 5539115
PMID: 28765603
DOI: 10.1038/s41598-017-07156-1

[…] one hidden layer and a single output unit was used. a set of alternative numbers of hidden units were tested: between 10 and 200 hidden units., the network was constructed and trained using the pybrain python library. because of the relatively small dataset size (200 proteins), the method was cross-validated, instead of creating separate training and test sets. to avoid overtraining, […]

PMCID: 5134511
PMID: 27827909
DOI: 10.3390/s16111852

[…] planner scheme., the design of adtp and an evaluation of its accuracy has been carried out by relying on the python-based reinforcement learning, artificial intelligence and neural network (pybrain [], dalle molle institute for artificial intelligence, idsia, switzerland and technische universitat munchen, germany) library. in it is possible to see the implementation adopted […]

PMCID: 4836660
PMID: 27093054
DOI: 10.1371/journal.pone.0153904

[…] the model trained by other sample sizes would also perform well. therefore, we decided to train the proposed models using 45 samples., for the fnn models, we used the feedforwardnetwork module of pybrain [] to build the proposed fnn model. each fnn model contains a linear input layer with 25 nodes, a linear output layer with 1 node, and a “tanh” hidden layer with an undetermined number […]

PMCID: 4803387
PMID: 26545824
DOI: 10.1093/bioinformatics/btv593

[…] residue). in the current design, three machine learning algorithms: svm, nn and rf can be used for prediction. we employed the python-based machine learning packages scikit-learn and libsvm (svm), pybrain (nn) and randomforestclassifier (rf), we also used numpy and scipy as numerical computing packages in python (; ; ; )., the positive training dataset is the one produced […]

To access a full list of publications, you will need to upgrade to our premium service.

PyBrain institution(s)
IDSIA, University of Lugano Manno-Lugano, Switzerland; Technische Universitat Munchen, Garching, Germany
PyBrain funding source(s)
Supported by SNF grants 200021-111968/1, 200021-113364/1 and 200020- 116674/1, the Excellence Cluster Cognition For Technical Systems (CoTeSys) of the German Research Foundation (DFG) and the EU FP6 project #033287.

PyBrain reviews

star_border star_border star_border star_border star_border
star star star star star

Be the first to review PyBrain