Machine learning predictive studies

One way to develop an in silica tool to predictive promiscuity is to apply a NB classifier for modeling, a technique that compares the frequencies of features between selective and promiscuous sets of compounds. Bayesian classification was applied in many studies and was recently compared to other machine-learning techniques [26, 27, 43, 51, 52]. 

Krishnan, V.G., Westhead, D.R. A comparative study of machine-learning methods to predict the effects of single nucleotide polymorphisms on protein function. Bioinformatics 2003,19,2199-209. 

This case study described model building for the prediction of metabolic lability of novel compounds. The analysis of different descriptors and machine learning algorithms shows that the chosen descriptor set, as well as the machine learning algorithm influence the predictivity of the model. Committee models, as implemented within Cubist, include an inherent error correction mechanism, which improves predictivity. 

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... 

ML is a branch of artificial intelligence, which is concerned with the constraction and study of computational systems that can learn from data [9]. A ML system could be trained based on properties and features and on the basis of that information, predictions can be done. The aim of ML is to teach a machine to learn from experiences, i.e. to feed it with a set of example objects and, based on the information content thereof, to build a classifier or a predictive model [10] (Fig. 3.3). 

Abe et. at. C23 reported another study on the verification of correlations between mass spectra and biological activity. Several pattern recognition methods have been applied to a set of 17 analgesics and 16 antispasmodics. Predictive abilities of more than 90 % have been obtained by the KNN-method and by the learning machine. A set of 30 features and the leave-one-out-procedure was employed. 

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