Understand
mlpy is a Python Open Source Machine Learning library built on top of NumPy/SciPy and the GNU Scientific Libraries.
- mlpy provides a wide range of state-of-the-art machine learning methods for supervised and unsupervised problems and it is aimed at finding a reasonable compromise among modularity, maintainability, reproducibility, usability and efficiency.
- mlpy is multiplatform, it works with Python 2 and 3 and it is distributed under GPL3 at the website http://mlpy.fbk.eu.
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Selection bias in gene extraction on the basis of microarray gene-expression data
C. Ambroise and G.J. McLachlan · 2002
Earlier work this paper cites.
LIBLINEAR: A library for large linear classification
R.-E. Fan, K.-W. Chang, C.-J. Hsieh, X.-R. Wang, and C.-J. Lin · 2008
Earlier work this paper cites.
Algebraic stability indicators for ranked lists in molecular profiling
G. Jurman, S. Merler, A. Barla, S. Paoli, A. Galea, and C. Furlanello · 2008
Earlier work this paper cites.
PyMVPA: a Python Toolbox for Multivariate Pattern Analysis of fMRI Data
M. Hanke, Y. Halchenko, P. Sederberg, S. Hanson, J. Haxby, and S. Pollmann · 2009
Earlier work this paper cites.
Similar
Repeatability of published microarray gene expression analyses
J.P.A. Ioannidis, D.B. Allison, C.A. Ball, I. Coulibaly, X. Cui, Culhane. A.C., M. Falchi, C. Furlanello, L. Game, G. Jurman, T. Mehta, J. Mangion, M. Nitzberg, G.P. Page, E. Petretto, and V. van Noort · 2009
Cited alongside, same era.
Modular toolkit for Data Processing (MDP): a Python data processing frame work
T. Zito, N. Wilbert, L. Wiskott, and P. Berkes · 2009
Cited alongside, same era.
The SHOGUN Machine Learning Toolbox
S. Sonnenburg, G. Rätsch, S. Henschel, C. Widmer, J. Behr, A. Zien, F. de Bona, A. Binder, C. Gehl, and V. Franc · 2010
Cited alongside, same era.
The MAQC-II Project: A comprehensive study of common practices for the development and validation of microarray-based predictive models
The MicroArray Quality Control (MAQC) Consortium · 2010
Cited alongside, same era.
Then
LIBSVM: A library for support vector machines
C.-C. Chang and C.-J. Lin · 2011
Later among the works it cites.
Scikit-learn: Machine Learning in Python
F. Pedregosa, G. Varoquaux, A. Gramfort, V. Michel, B. Thirion, O. Grisel, M. Blondel, P. Prettenhofer, R. Weiss, V. Dubourg, J. Vanderplas, A. Passos, D. Cournapeau, M. Brucher, M. Perrot, and Duchesnay E · 2011
Later among the works it cites.
Python: An Ecosystem for Scientific Computing
F. Pérez, B.E. Granger, and J.D. Hunter · 2011
Later among the works it cites.
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