Biomaterial Database

Structural SCOP superfamily level classification using unsupervised machine learning. 2012

Ulavappa B Angadi, and M Venkatesulu

Kalasalingam University, Srivilliputtur.

One of the major research directions in bioinformatics is that of assigning superfamily classification to a given set of proteins. The classification reflects the structural, evolutionary, and functional relatedness. These relationships are embodied in a hierarchical classification, such as the Structural Classification of Protein (SCOP), which is mostly manually curated. Such a classification is essential for the structural and functional analyses of proteins. Yet a large number of proteins remain unclassified. In this study, we have proposed an unsupervised machine learning approach to classify and assign a given set of proteins to SCOP superfamilies. In the method, we have constructed a database and similarity matrix using P-values obtained from an all-against-all BLAST run and trained the network with the ART2 unsupervised learning algorithm using the rows of the similarity matrix as input vectors, enabling the trained network to classify the proteins from 0.82 to 0.97 f-measure accuracy. The performance of ART2 has been compared with that of spectral clustering, Random forest, SVM, and HHpred. ART2 performs better than the others except HHpred. HHpred performs better than ART2 and the sum of errors is smaller than that of the other methods evaluated.

UI	MeSH Term	Description	Entries
D010363	Pattern Recognition, Automated	In INFORMATION RETRIEVAL, machine-sensing or identification of visible patterns (shapes, forms, and configurations). (Harrod's Librarians' Glossary, 7th ed)	Automated Pattern Recognition,Pattern Recognition System,Pattern Recognition Systems
D011506	Proteins	Linear POLYPEPTIDES that are synthesized on RIBOSOMES and may be further modified, crosslinked, cleaved, or assembled into complex proteins with several subunits. The specific sequence of AMINO ACIDS determines the shape the polypeptide will take, during PROTEIN FOLDING, and the function of the protein.	Gene Products, Protein,Gene Proteins,Protein,Protein Gene Products,Proteins, Gene
D000465	Algorithms	A procedure consisting of a sequence of algebraic formulas and/or logical steps to calculate or determine a given task.	Algorithm
D015233	Models, Statistical	Statistical formulations or analyses which, when applied to data and found to fit the data, are then used to verify the assumptions and parameters used in the analysis. Examples of statistical models are the linear model, binomial model, polynomial model, two-parameter model, etc.	Probabilistic Models,Statistical Models,Two-Parameter Models,Model, Statistical,Models, Binomial,Models, Polynomial,Statistical Model,Binomial Model,Binomial Models,Model, Binomial,Model, Polynomial,Model, Probabilistic,Model, Two-Parameter,Models, Probabilistic,Models, Two-Parameter,Polynomial Model,Polynomial Models,Probabilistic Model,Two Parameter Models,Two-Parameter Model
D016000	Cluster Analysis	A set of statistical methods used to group variables or observations into strongly inter-related subgroups. In epidemiology, it may be used to analyze a closely grouped series of events or cases of disease or other health-related phenomenon with well-defined distribution patterns in relation to time or place or both.	Clustering,Analyses, Cluster,Analysis, Cluster,Cluster Analyses,Clusterings
D016571	Neural Networks, Computer	A computer architecture, implementable in either hardware or software, modeled after biological neural networks. Like the biological system in which the processing capability is a result of the interconnection strengths between arrays of nonlinear processing nodes, computerized neural networks, often called perceptrons or multilayer connectionist models, consist of neuron-like units. A homogeneous group of units makes up a layer. These networks are good at pattern recognition. They are adaptive, performing tasks by example, and thus are better for decision-making than are linear learning machines or cluster analysis. They do not require explicit programming.	Computational Neural Networks,Connectionist Models,Models, Neural Network,Neural Network Models,Neural Networks (Computer),Perceptrons,Computational Neural Network,Computer Neural Network,Computer Neural Networks,Connectionist Model,Model, Connectionist,Model, Neural Network,Models, Connectionist,Network Model, Neural,Network Models, Neural,Network, Computational Neural,Network, Computer Neural,Network, Neural (Computer),Networks, Computational Neural,Networks, Computer Neural,Networks, Neural (Computer),Neural Network (Computer),Neural Network Model,Neural Network, Computational,Neural Network, Computer,Neural Networks, Computational,Perceptron
D017434	Protein Structure, Tertiary	The level of protein structure in which combinations of secondary protein structures (ALPHA HELICES; BETA SHEETS; loop regions, and AMINO ACID MOTIFS) pack together to form folded shapes. Disulfide bridges between cysteines in two different parts of the polypeptide chain along with other interactions between the chains play a role in the formation and stabilization of tertiary structure.	Tertiary Protein Structure,Protein Structures, Tertiary,Tertiary Protein Structures
D019295	Computational Biology	A field of biology concerned with the development of techniques for the collection and manipulation of biological data, and the use of such data to make biological discoveries or predictions. This field encompasses all computational methods and theories for solving biological problems including manipulation of models and datasets.	Bioinformatics,Molecular Biology, Computational,Bio-Informatics,Biology, Computational,Computational Molecular Biology,Bio Informatics,Bio-Informatic,Bioinformatic,Biologies, Computational Molecular,Biology, Computational Molecular,Computational Molecular Biologies,Molecular Biologies, Computational
D020539	Sequence Analysis, Protein	A process that includes the determination of AMINO ACID SEQUENCE of a protein (or peptide, oligopeptide or peptide fragment) and the information analysis of the sequence.	Amino Acid Sequence Analysis,Peptide Sequence Analysis,Protein Sequence Analysis,Sequence Determination, Protein,Amino Acid Sequence Analyses,Amino Acid Sequence Determination,Amino Acid Sequence Determinations,Amino Acid Sequencing,Peptide Sequence Determination,Protein Sequencing,Sequence Analyses, Amino Acid,Sequence Analysis, Amino Acid,Sequence Analysis, Peptide,Sequence Determination, Amino Acid,Sequence Determinations, Amino Acid,Acid Sequencing, Amino,Analyses, Peptide Sequence,Analyses, Protein Sequence,Analysis, Peptide Sequence,Analysis, Protein Sequence,Peptide Sequence Analyses,Peptide Sequence Determinations,Protein Sequence Analyses,Protein Sequence Determination,Protein Sequence Determinations,Sequence Analyses, Peptide,Sequence Analyses, Protein,Sequence Determination, Peptide,Sequence Determinations, Peptide,Sequence Determinations, Protein,Sequencing, Amino Acid,Sequencing, Protein