Machine nearest neighbors

In this study, a machine learning model system was developed to classify cell line chemosensitivity exclusively based on proteomic profiling. Using reverse-phase protein lysate microarrays, protein expression levels were measured by 52 antibodies in a panel of 60 human cancer cell (NCI-60) lines. The model system combined several well-known algorithms, including Random forests, Relief, and the nearest neighbor methods, to construct the protein expression-based chemosensitivity classifiers. 

Histone deacetylases (HDACs) play a critical role in transcription regulation. Small molecule HDAC inhibitors have become an emerging target for the treatment of cancer and other cell proliferation diseases. We have employed variable selection k nearest neighbor approach (iNN)and support vector machines (SVM) approach to generate QSAR models for 59 chemically diverse... 

Supervised learning methods - multivariate analysis of variance and discriminant analysis (MVDA) - k nearest neighbors (kNN) - linear learning machine (LLM) - BAYES classification - soft independent modeling of class analogy (SIMCA) - UNEQ classification Quantitative demarcation of a priori classes, relationships between class properties and variables... 

The most popular classification methods are Linear Discriminant Analysis (LDA), Quadratic Discriminant Analysis (QDA), Regularized Discriminant Analysis (RDA), Kth Nearest Neighbors (KNN), classification tree methods (such as CART), Soft-Independent Modeling of Class Analogy (SIMCA), potential function classifiers (PFC), Nearest Mean Classifier (NMC), Weighted Nearest Mean Classifier (WNMC), Support Vector Machine (SVM), and Classification And Influence Matrix Analysis (CAIMAN). 

Recursive partitioning, Bayesian classifier, logisi-tic regression, k-nearest neighbor, support vector machine... 

There are a number of classification methods for analyzing data, including artificial neural (ANNs see Beale and Jackson, 1990) networks, -nearest-neighbor (fe-NN) methods, decision trees, support vector machines (SVMs), and Fisher s linear discriminant analysis (LDA). Among these methods, a decision tree is a flow-chart-like tree stmcture. An intermediate node denotes a test on a predictive attribute, and a branch represents an outcome of the test. A terminal node denotes class distribution. 

Wassermann, A. M., Geppert, H., and Bajorath, J. 2009. Ligand prediction for orphan targets using support vector machines and various target-hgand kernels is dominated by nearest neighbor effects. J. Chem. Inf. Model. 49 2155-2167. 

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