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Information


Unique identifier OMICS_13936
Name PyML
Alternative name Python Machine Learning
Software type Package/Module
Interface Command line interface
Restrictions to use None
Operating system Unix/Linux, Mac OS
Programming languages C++, Python
License GNU General Public License version 2.0
Computer skills Advanced
Version 0.7.14
Stability Stable
Requirements
Numpy, Matplotlib
Maintained Yes

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Publication for Python Machine Learning

PyML citations

 (25)
library_books

A note on the analysis of two stage task results: How changes in task structure affect what model free and model based strategies predict about the effects of reward and transition on the stay probability

2018
PLoS One
PMCID: 5882146
PMID: 29614130
DOI: 10.1371/journal.pone.0195328

[…] re simulated for each task modification discussed. The regression models were fitted to the data using the regularized logistic regression classifier with the liblinear algorithm from scikit-learn, a Python machine learning package []. […]

library_books

BTNET : boosted tree based gene regulatory network inference algorithm using time course measurement data

2018
BMC Syst Biol
PMCID: 5861501
PMID: 29560827
DOI: 10.1186/s12918-018-0547-0

[…] modified the part of computing regulatory interaction scores from bagging based tree method to boosted tree method. We used AdaBoost and gradient boosting implementation provided in the scikit-learn Python machine learning package. The visualization of an inferred gene regulatory network was done by using the Graphviz Python package version 0.4.10. For a fair comparison with GENIE3-time, we used […]

library_books

Microbiome Data Accurately Predicts the Postmortem Interval Using Random Forest Regression Models

2018
Genes
PMCID: 5852600
PMID: 29462950
DOI: 10.3390/genes9020104

[…] sing only the first 25 days resulted in invariably lower MAEs (). Therefore, data subset to the first 25 days of decomposition were selected for the modeling in this study. The modeling was done with Python machine learning package scikit-learn v19.0 []. Data were analyzed and graphics were generated using R software, version 3.4.1, the ggplot2 package, and matplotlib 2.0.0 [,,]. We provide jupyte […]

library_books

Scenario Screen: A Dynamic and Context Dependent P300 Stimulator Screen Aimed at Wheelchair Navigation Control

2018
PMCID: 5832133
PMID: 29666663
DOI: 10.1155/2018/7108906

[…] distance to the regression hyperplane:(2)sx=wTx+b.A LASSO-LDA was trained for each subject using the data from their corresponding Blocks II and III as shows. No class balancing method was utilized (Python machine learning library: scikit–learn LASSO through LARS method and sparsity parameter λ was estimated by cross-validation []). Target labeling was performed in two stages: (1) to score the fe […]

library_books

Mordred: a molecular descriptor calculator

2018
J Cheminform
PMCID: 5801138
PMID: 29411163
DOI: 10.1186/s13321-018-0258-y

[…] emoPy, a free software environment that calculates both 2D and 3D descriptors, can calculate 1135 descriptors. ChemoPy is available as a Python package and is convenient for constructing models using Python machine-learning packages. However, it can be difficult to employ it by non-Python users who are not familiar with the construction of the Python interface. (As described later, this disadvanta […]

library_books

Discriminating between HuR and TTP binding sites using the k spectrum kernel method

2017
PLoS One
PMCID: 5363848
PMID: 28333956
DOI: 10.1371/journal.pone.0174052

[…] remote evolutionary relationship []. A brief primer on SVMs and the k-spectrum kernel are provided in the Section k-spectrum Kernel Method of . The k-spectrum kernel method was implemented using the PyML library (version PyML-0.7.13.3, Python version 2.7 [] http://pyml.sourceforge.net).For the k-spectrum kernel, the HuR, and TTP target sequences were each used independently to build k-spectrum ke […]

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PyML institution(s)
Department of Genome Sciences, University of Washington, Seattle, WA, USA; Department of Computer Science and Engineering, University of Washington, Seattle, WA, USA
PyML funding source(s)
This work is funded by NCRR NIH award P41 RR11823, by NHGRI NIH award R33 HG003070, and by NSF award BDI-0243257.

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