Titlebook: Biomedical Data and Applications; Amandeep S. Sidhu,Tharam S. Dillon Book 2009 Springer-Verlag Berlin Heidelberg 2009 algorithm.bioinforma

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书目名称Biomedical Data and Applications被引频次学科排名




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书目名称Biomedical Data and Applications读者反馈学科排名

MAZE

Towards Bioinformatics Resourceomes bioinformatics as a very dynamic universe, continuously growing under our eyes. More and more scientists are approaching it and would like to easier explore it, discovering the resources that can be found in every space region (i.e. related to every bioinformatics topic). We propose to intuitively

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A Summary of Genomic Databases: Overview and Discussionoday, more than eight hundred can be counted thereof). In spite of the enormous amount of available resources, a user may be disoriented when he/she searches for specific data. Thus, the accurate analysis of biological data and repositories turn out to be useful to obtain a systematic view of biolog

stress-test

Protein Data Integration Problemas an integral part of their discovery process. Specifically, data integration, meta-data specification, data provenance and data quality, and ontology are discussed here. These are the fundamental problems that need to be solved by the bioinformatics community so that modern information technology

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Heretical

Bio-medical Ontologies Maintenance and Change Managementseveral techniques from areas such as knowledge representation, semantic web and databases. Many of these techniques require a formal description of a part of the real world. Ontologies can provide a set of shared and precisely defined terms in various degrees of formality to describe a particular d

MUMP

Extraction of Constraints from Biological Datacontext, a constraint can be defined as a set of predicates P. ∧ P. ∧ ... P.. Each predicate is in the form C..C., where C. is an attribute, . is a comparison operator and C. is either an attribute or a constant [15]. Constraints are assertions on permissible or consistent database states, and speci

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Substructure Analysis of Metabolic Pathways by Graph-Based Relational Learningips is a fundamental way to represent a bio-system. A graph consisting of vertices and edges between these vertices is a natural data structure to represent biological networks. Substructure analysis of metabolic pathways by graph-based relational learning provides us biologically meaningful substru