Secondary structure prediction with support vector machines
نویسندگان
چکیده
منابع مشابه
Secondary structure prediction with support vector machines
MOTIVATION A new method that uses support vector machines (SVMs) to predict protein secondary structure is described and evaluated. The study is designed to develop a reliable prediction method using an alternative technique and to investigate the applicability of SVMs to this type of bioinformatics problem. METHODS Binary SVMs are trained to discriminate between two structural classes. The b...
متن کاملProtein Secondary Structure Prediction with Support Vector Machines
In this paper, a method for secondary structure with support vector machines is presented. The system used two layers of support vector machines, with a weighted cost function to balance the uneven class memberships. Using this method, prediction accuracy reaches 71.5%, comparable to the best techniques avaliable.
متن کاملMulti-class support vector machines for protein secondary structure prediction.
The solution of binary classification problems using the Support Vector Machine (SVM) method has been well developed. Though multi-class classification is typically solved by combining several binary classifiers, recently, several multi-class methods that consider all classes at once have been proposed. However, these methods require resolving a much larger optimization problem and are applicab...
متن کاملSecondary Structure Prediction of All-Helical Proteins Using Hidden Markov Support Vector Machines
Our goal is to develop a state-of-the-art predictor with an intuitive and biophysically-motivated energy model through the use of Hidden Markov Support Vector Machines (HM-SVMs), a recent innovation in the field of machine learning. We focus on the prediction of alpha helices in proteins and show that using HM-SVMs, a simple 7-state HMM with 302 parameters can achieve a Qα value of 77.6% and a ...
متن کاملProtein Secondary Structure Prediction Using Support Vector Machines and a New Feature Representation
Knowledge of the secondary structure and solvent accessibility of a protein plays a vital role in the prediction of fold, and eventually the tertiary structure of the protein. A challenging issue of predicting protein secondary structure from sequence alone is addressed. Support vector machines (SVM) are employed for the classification and the SVM outputs are converted to posterior probabilitie...
متن کاملذخیره در منابع من
با ذخیره ی این منبع در منابع من، دسترسی به آن را برای استفاده های بعدی آسان تر کنید
ژورنال
عنوان ژورنال: Bioinformatics
سال: 2003
ISSN: 1367-4803,1460-2059
DOI: 10.1093/bioinformatics/btg223