Supplementary Material

Towards Automatic Classification of NeuronsRubén Armañanzas and Giorgio A. Ascoli

Krasnow Institute for Advanced Study, George Mason UniversityFairfax, VA 22030, USA

Correspondence: ascoli@gmu.edu

Figure S1. Major classification approaches with representative algorithms. References and links to available resources are provided in Table S1.

Family/Method Refs SoftwareProbabilisticBayesian Network Classifiers (BNCs)

[S1] Weka, R(bnclassify), PyMVPA

Bayesian Networks [S2] Weka, GeNIe, Bayesia, Hugin, R(bnlearn, deal)

Expectation Maximization (EM) [S3] Weka, R(mclust)Generative Mixture Models [S4] Mixmod, R(Rmixmod)Factor Analysis for Biclustering [S5] R(fabia)Math FunctionsLogistic Regression [S6] Weka, R(glm2), Matlab(Statistics),

PyMVPASupport Vector Machines [S7] Weka, Shogun, R(libsvm),

Matlab(Statistics), PyMVPAArtificial Neural Networks [S8] Weka, Matlab(nnet, neuralnet)Kernel Methods [S9] Matlab(Statistics), R(stats, ks), PyMVPASemi-Supervised SVM (S3VM) [S10] SVMlight, UniverSVMSelf-Organizing Map (SOM) [S11] Matlab(SOM), R(som, kohonen)Subspace Clustering [S12] Weka(OpenSubspace), R(orclus,

FisherEM)Principal Component Analysis [S13] Matlab(Statistics), R(stats)Distance-basedk-Nearest Neighbors [S14] Weka, Matlab(Statistics), R(knn),

PyMVPAHierarchical Clustering [S15] Weka, Matlab(Statistics), R(stats,

fastcluster)k-Means Clustering [S16] Weka, R(stats), Matlab(Statistics)k-Medians Clustering [S17] Weka, R(flexclust), Matlab(Statistics)SimilarityGraph-based [S18] R(spa), SemiLGaussian processes [S19] Weka, R(gptk), Matlab(GPML)Affinity Propagation [S20] Weka (APCluster), Matlab(apcluster),

R(APCluster)Variance Biclustering [S21] R(biclust), Matlab(MTBA)RulesRIPPER [S22] Weka, R(caret), MLC++PRISM [S23] WekaTreesID3 [S24] Weka, R(tree), Matlab(Statistics)C4.5 [S25] Weka, R(tree), Matlab(Statistics)Random Forest [S26] Weka, R(randomForest),

Matlab(randomforest-matlab)Wrapper approachesSelf-training [S27] R(DMwR)Co-training [S28] CoAL, Weka(collective-classification)

Table S1. Major classification algorithms from a subset of representative families with original references and examples of implementations. Names in parentheses refer to particular packages that need to be installed. If none are noted, the algorithm is included within the basic installation.

Software list

Non-comprehensive exemplars of available programs implementing the algorithms presented in Table S1.

Bayesia http://www.bayesia.com/CoAL http://labic.icmc.usp.br/?q=node/762GeNIe https://dslpitt.org/genie/Hugin http://www.hugin.com/Matlab http://www.mathworks.com/products/matlab/Mixmod http://www.mixmod.org/MLC++ http://www.sgi.com/tech/mlc/PyMVPA http://www.pymvpa.org/scikit-learn http://scikit-learn.orgR http://www.r-project.org/SemiL http://www.learning-from-data.com/te-ming/semil.htmSVMlight http://svmlight.joachims.org/UniverSVM

http://www.epagoge.de/software/universvm/

Weka http://www.cs.waikato.ac.nz/ml/weka/

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