Molecular bioactivity extrapolation to novel targets by support vector machines

Molecular bioactivity extrapolation to novel targets by support vector machines

The early phases of drug discovery use in silico models to rationalize structure activity relationships, and to predict the activity of novel compounds. However, the performance of these models is not always acceptable and the reliability of external predictions both to novel compounds and to related protein targets is often limited. Proteochemometric modeling [1] adds a target description, bas...

STAGE-DISCHARGE MODELING USING SUPPORT VECTOR MACHINES

Establishment of rating curves are often required by the hydrologists for flow estimates in the streams, rivers etc. Measurement of discharge in a river is a time-consuming, expensive, and difficult process and the conventional approach of regression analysis of stage-discharge relation does not provide encouraging results especially during the floods. P

Combining Support Vector Machines to Predict Novel Angiogenesis Genes

Introduction to Support Vector Machines

Support Vector Machines (SVM’s) are a relatively new learning method used for binary classification. The basic idea is to find a hyperplane which separates the d-dimensional data perfectly into its two classes. However, since example data is often not linearly separable, SVM’s introduce the notion of a “kernel induced feature space” which casts the data into a higher dimensional space where the...

Face Recognition by Support Vector Machines

Support Vector Machines (SVMs) have been recently proposed as a new technique for pattern recognition. In this paper, the SVMs with a binary tree recognition strategy are used to tackle the face recognition problem. We illustrate the potential of SVMs on the Cambridge ORL face database, which consists of 400 images of 40 individuals, containing quite a high degree of variability in expression, ...