SVMtm: Support vector machines to predict transmembrane segments

SVMtm: Support vector machines to predict transmembrane segments

A new method has been developed for prediction of transmembrane helices using support vector machines. Different coding schemes of protein sequences were explored, and their performances were assessed by crossvalidation tests. The best performance method can predict the transmembrane helices with sensitivity of 93.4% and precision of 92.0%. For each predicted transmembrane segment, a score is g...

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...

Predict Collagen Hydroxyproline Sites Using Support Vector Machines

Collagen hydroxyproline is an important posttranslational modification activity because of its close relationship with various diseases and signaling activities. However, there is no study to date for constructing models for predicting collagen hydroxyproline sites. Support vector machines with two kernel functions (the identity kernel function and the bio-kernel function) have been used for co...