Kernel Partial Least Research Articles

An In Silico Method for Screening Nicotine Derivatives as Cytochrome P450 2A6 Selective Inhibitors Based on Kernel Partial Least Squares

Nicotine and a variety of other drugs and toxins are metabolized by cytochromeP450 (CYP) 2A6. The aim of the present study was to build a quantitative structure-activityrelationship (QSAR) model to predict the activities of nicotine analogues on CYP2A6.Kernel partial least squares (K-PLS) regression was employed with the electro-topologicaldescriptors to build the computational models. Both the internal and external predictabilitiesof the models were evaluated with test sets to ensure their validity and reliability. As acomparison to K-PLS, a standard PLS algorithm was also applied on the same training andtest sets. Our results show that the K-PLS produced reasonable results that outperformed thePLS model on the datasets. The obtained K-PLS model will be helpful for the design ofnovel nicotine-like selective CYP2A6 inhibitors.

Classification of Metabolites with Kernel-Partial Least Squares (K-PLS)

Numerous experimental and computational approaches have been developed to predict human drug metabolism. Since databases of human drug metabolism information are widely available, these can be used to train computational algorithms and generate predictive approaches. In turn, they may be used to assist in the identification of possible metabolites from a large number of molecules in drug discovery based on molecular structure alone. In the current study we have used a commercially available database (MetaDrug) and extracted a fraction of the human drug metabolism data. These data were used along with augmented atom descriptors in a predictive machine learning model, kernel-partial least squares (K-PLS). A total of 317 molecules, including parent drugs and their primary and secondary (sequential) metabolites, were used to build these models corresponding to individual metabolism rules, representing the formation of discrete metabolites, e.g., N-dealkylation. Each model was internally validated to assess the capability to classify other molecules that were left out. Using receiver operator curve statistics models for N-dealkylation, O-dealkylation, aromatic hydroxylation, aliphatic hydroxylation, O-glucuronidation, and O-sulfation gave area under the curve values from 0.75 to 0.84 and were able to predict between 61 and 79% active molecules upon leave-one-out testing. This preliminary study indicates that K-PLS and possibly other similar machine learning methods (such as support vector machines) can be applied to predicting human drug metabolite formation in a classification manner. Improvements can be achieved using considerably larger datasets that contain more positive examples for the less frequently occurring metabolite rules, as well as the external evaluation of novel molecules.

Piecewise Kernel Partial Least Squares with bagging k-meansによるQSAR解析

Piecewise Kernel Partial Least Squares with bagging k-meansによるQSAR解析

Subset based least squares subspace regression in RKHS

Kernel based methods suffer from exceeding time and memory requirements when applied on large datasets since the involved optimization problems typically scale polynomially in the number of data samples. As a remedy, some least squares methods on one hand only reduce the number of parameters (for fast training), on the other hand only work on a reduced set (for fast evaluation). Departing from the Nyström based feature approximation, via the fixed-size LS-SVM model, we propose a general regression framework, based on restriction of the search space to a subspace and a particular choice of basis vectors in feature space. In the general model both reduction aspects are unified and become explicit model choices. This allows to accommodate kernel Partial Least Squares and kernel Canonical Correlation analysis for regression with a sparse representation, which makes them applicable to large data sets, with little loss in accuracy.

Introduction to the special issue on kernel methods

This special issue arose from a workshop held at NIPS 2000 on New Directions in Kernel Methods, though not all the submissions received were from talks at the workshop. With the great help of around forty referees we selected the following ten papers from some 28 submissions, an acceptance rate of 36%. The high number of submissions we received illustrates the vitality and popularity of the field of kernel methods in machine learning. We are pleased to be able to support the fledgling Journal of Machine Learning Research in this way and to provide a rapid but refereed route to publication for the papers presented at the workshop less than a year ago. The papers in the special issue cover a wide range of topics in kernel-based learning machines, but mostly reflect three of the main current research directions: exporting the design principles of standard Support Vector Machines to a variety of other algorithms, producing alternative and more efficient implementations, and deepening the theoretical understanding of kernel methods. The first five papers in the special issue describe extensions of the basic algorithms: Kernel Partial Least Squares Regression in RKHS by Roman Rosipal and Leonard J. Trejo describes the development of kernel partial least squares regression. This technique is similar to kernel PCA or latent semantic kernels, but the projection is chosen by modeling the relationship between input and output variables. The paper compares performance of a number of different projection methods and obtains encouraging results, particularly in terms of the number of dimensions required to obtain a certain level of performance. In Support Vector Clustering, Asa Ben-Hur, David Horn, Hava T. Siegelmann and Vladimir Vapnik present a novel clustering method using Support Vector Machines. Data points are mapped by means of a Gaussian kernel to a high dimensional feature space, where the minimal enclosing sphere can be calculated. When mapped back to data space, this sphere can separate into several components, each enclosing a separate cluster of points. A simple algorithm for identifying these clusters is discussed and evaluated experimentally. One-Class SVMs for Document Classification by Larry M. Manevitz and Malik Yousef provides extensive experimentation comparing the SVM approach to one-class classification of text documents with more traditional methods such as nearest neighbour, naive Bayes and one more advanced neural network method based on ‘bottleneck’ compression. The