Building multiclass classifiers for remote homology detection and fold recognition

BACKGROUND: Protein remote homology detection and fold recognition are central problems in computational biology. Supervised learning algorithms based on support vector machines are currently one of the most effective methods for solving these problems. These methods are primarily used to solve binary classification problems and they have not been extensively used to solve the more general multiclass remote homology prediction and fold recognition problems. RESULTS: We present a comprehensive evaluation of a number of methods for building SVM-based multiclass classification schemes in the context of the SCOP protein classification. These methods include schemes that directly build an SVM-based multiclass model, schemes that employ a second-level learning approach to combine the predictions generated by a set of binary SVM-based classifiers, and schemes that build and combine binary classifiers for various levels of the SCOP hierarchy beyond those defining the target classes. CONCLUSION: Analyzing the performance achieved by the different approaches on four different datasets we show that most of the proposed multiclass SVM-based classification approaches are quite effective in solving the remote homology prediction and fold recognition problems and that the schemes that use predictions from binary models constructed for ancestral categories within the SCOP hierarchy tend to not only lead to lower error rates but also reduce the number of errors in which a superfamily is assigned to an entirely different fold and a fold is predicted as being from a different SCOP class. Our results also show that the limited size of the training data makes it hard to learn complex second-level models, and that models of moderate complexity lead to consistently better results

GOPred: GO Molecular Function Prediction by Combined Classifiers

Functional protein annotation is an important matter for in vivo and in silico biology. Several computational methods have been proposed that make use of a wide range of features such as motifs, domains, homology, structure and physicochemical properties. There is no single method that performs best in all functional classification problems because information obtained using any of these features depends on the function to be assigned to the protein. In this study, we portray a novel approach that combines different methods to better represent protein function. First, we formulated the function annotation problem as a classification problem defined on 300 different Gene Ontology (GO) terms from molecular function aspect. We presented a method to form positive and negative training examples while taking into account the directed acyclic graph (DAG) structure and evidence codes of GO. We applied three different methods and their combinations. Results show that combining different methods improves prediction accuracy in most cases. The proposed method, GOPred, is available as an online computational annotation tool (http://kinaz.fen.bilkent.edu.tr/gopred)

Near Infrared Spectra and Hyperspectral Imaging of Medieval Fortress Walls in Carcassonne: A Comprehensive Interdisciplinary Field Study

International audienc

Near infrared spectra and hyperspectral imaging of medieval fortress walls in carcassonne: a comprehensive interdisciplinary field study

International audienc

Characterization of building materials by means of spectral remote sensing: The example of Carcassonne's defensive wall (Aude, France)

Geological and archaeological analysis of stone masonries in standing structures helps reveal information about use of natural resources. At the same time, the study of historical materials is useful for conservators and cultural heritage management. Geochemical and petrographic analysis of building material types is usually done through destructive analysis on a few selected samples and can be problematic due to the costs of operations and the size of buildings themselves. This paper demonstrates that the combination of hyperspectral imaging portable Near Infrared (NIR) spectroscopy and Energy Dispersive X-ray Fluorescence (ED-XRF) spectroscopy was useful for analysing types of raw materials used in distinct construction phases of the inner defensive wall in the citadel of Carcassonne (Aude, France). Stratigraphic analysis of the architecture, short-range spectral remote sensing and portable ED-XRF measurements were combined in an interdisciplinary approach to classify sandstone elements. The experimental protocol for in situ non-destructive analysis and classification of the masonry types allows the investigation of the monument in a diachronic perspective, collecting information to delineate raw materials varieties and their use or re-use through time

Characterization of building materials by means of spectral remote sensing: The example of Carcassonne's defensive wall (Aude, France)

International audienceGeological and archaeological analysis of stone masonries in standing structures helps reveal information about use of natural resources. At the same time, the study of historical materials is useful for conservators and cultural heritage management. Geochemical and petrographic analysis of building material types is usually done through destructive analysis on a few selected samples and can be problematic due to the costs of operations and the size of buildings themselves. This paper demonstrates that the combination of hyperspectral imaging portable Near Infrared (NIR) spectroscopy and Energy Dispersive X-ray Fluorescence (ED-XRF) spectroscopy can be useful for analysing types of raw materials used in distinct construction phases of the inner defensive wall in the citadel of Carcassonne (Aude, France). Stratigraphic analysis of the architecture, short-range spectral remote sensing and portable ED-XRF measurements were combined in an interdisciplinary approach to classify sandstone elements. The experimental protocol for in situ non-destructive analysis and classification of the masonry types allows the investigation of the monument in a diachronic perspective, collecting information to delineate raw materials varieties and their use or re-use through time