Machine learning models

Machine learning models derived from biologically annotated databases (e.g. MDDR, WDI)... 

Schroeter, T, Schwaighofer, A., Mika, S., Ter Laak, A., Suelzle, D., Ganzer, U., Heinrich, N. and Muller, K.R. (2007) Machine learning models for lipophilicity and their domain of applicability. Molecular Pharmaceutics, 4, 524—538. 

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. 

David Cummins is Principal Research Scientist at Eli Lilly and Company. His interests are in nonparametric regression, exploratory data analysis, simulation, predictive inference, machine learning, model selection, cheminformatics, genomics, proteomics, and metabonomics. 

The most recent advance in machine-learning modeling to gamer widespread application by fields outside of artificial intelligence itself is the support vector machine (SVM). SVM s were first developed by Vapnik in 1992. ... 

Many different methods can be applied to virtual screening, and such methods are described in other chapters of this book and/or in the Handbooks of Che-minformatics Here we discuss the methods based on a probabilistic approach. Unfortunately, there are many publications in which the probabilistic or statistical approach items are farfetched. The Binary Kernel Discrimination and the Bayesian Machine Learning Models are actually special... 

Bahler D, Stone B, Wellington C, Bristol DW. Symbolic, neural, and Bayesian machine learning models for predicting carcinogenicity of chemical compounds. / Chem Inf Comput Sci 2000 40 906-14. 

Aliferis CF, Hardin D, Massion P. Machine learning models for lung cancer classification using array comparative genomic hybridization. Proc AMIA Symp. 2002 7-ll. 

The prediction performance of machine learning models can be evaluated by using sensitivity SE)... 

Practice Ititorial for Building Machine Learning Models in Rapid Miner... 

Two different types of in silico analysis are relevant for in silica target deconvolution (i) correlation analysis between phenotypic screening results and the in vitro biochemical profile of the screened compounds and (ii) machine learning models to predict the targets for the hits in the phenotypic screening. Two independent studies have been published very recently where the Fisher exact test... 

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