title: Using recurrence quantification analysis descriptors for protein sequence classification with support vector machines.
abstract: In this work, we integrate a non-linear signal analysis method, recurrence quantification analysis (RQA), with the well-known machine-learning algorithm, support vector machines for the binary classification of protein sequences. Two different classification problems were selected, discriminating between aggregating and non-aggregating proteins and mostly disordered and completely ordered proteins, respectively. It has also been shown that classification performance of SVM models improve on selection of the most informative RQA descriptors as SVM input features.
authors.affiliation: Chemical Engineering and Process Development Division, National Chemical Laboratory, Dr. Homi Bhabha Road, Pune 411008, India.
authors.forename: Joydeep
authors.initials: J
authors.lastname: Mitra
authors.forename: Piyushkumar
authors.initials: P
authors.lastname: Mundra
authors.forename: B D
authors.initials: BD
authors.lastname: Kulkarni
authors.forename: Valadi K
authors.initials: VK
authors.lastname: Jayaraman
mesh_terms: D000465:Algorithms; D000595:Amino Acid Sequence; D030562:Databases, Protein; D015233:Models, Statistical; D016571:Neural Networks, Computer; D011506:Proteins / Q000737:chemistry* / Q000145:classification*
chemical_list: D011506:Proteins