Ontology-based instance data validation for high-quality curated biological pathways

<p>Abstract</p> <p>Background</p> <p>Modeling in systems biology is vital for understanding the complexity of biological systems across scales and predicting system-level behaviors. To obtain high-quality pathway databases, it is essential to improve the efficiency of model validation and model update based on appropriate feedback.</p> <p>Results</p> <p>We have developed a new method to guide creating novel high-quality biological pathways, using a rule-based validation. Rules are defined to correct models against biological semantics and improve models for dynamic simulation. In this work, we have defined 40 rules which constrain event-specific participants and the related features and adding missing processes based on biological events. This approach is applied to data in Cell System Ontology which is a comprehensive ontology that represents complex biological pathways with dynamics and visualization. The experimental results show that the relatively simple rules can efficiently detect errors made during curation, such as misassignment and misuse of ontology concepts and terms in curated models.</p> <p>Conclusions</p> <p>A new rule-based approach has been developed to facilitate model validation and model complementation. Our rule-based validation embedding biological semantics enables us to provide high-quality curated biological pathways. This approach can serve as a preprocessing step for model integration, exchange and extraction data, and simulation.</p

Multi-label multi-instance transfer learning for simultaneous reconstruction and cross-talk modeling of multiple human signaling pathways

Text file contains the predicted cross-talk signaling components between human signaling pathways (homolog instance). (ZIP 36 KB

Protein ontology: Vocabulary for protein data

These Huge amounts of Protein Structure Data make it difficult to create explanatory and predictive models that are consistent with huge volume of data. Difficulty increase when large variety of heterogeneous approaches gathers data from multiple perspectives. In order to facilitate computational processing data, it is especially critical to develop standardized structured data representation model formats for proteomics data. In this paper we describe a Protein Ontology Model for integrating protein databases and deduce a structured vocabulary for understanding process of protein synthesis completely. Proposed Protein Ontology Model provides biologists and scientists with a description of sequence, structure and functions of protein and also provides interpretation of various factors on final protein structure conformation. The Structured Vocabulary for Protein Data, describing Protein Ontology is composed of various Type Definitions for Protein Entry Details, Sequence and Structural Information of Proteins, Structural Domain Family of Protein, Cellular Function of Protein, Chemical Bonds present in the Protein, and External Constraints deciding final protein conformation. The Proposed Ontology Model will provide easier ways to predict and understand proteins

TargetMine, an Integrated Data Warehouse for Candidate Gene Prioritisation and Target Discovery

Prioritising candidate genes for further experimental characterisation is a non-trivial challenge in drug discovery and biomedical research in general. An integrated approach that combines results from multiple data types is best suited for optimal target selection. We developed TargetMine, a data warehouse for efficient target prioritisation. TargetMine utilises the InterMine framework, with new data models such as protein-DNA interactions integrated in a novel way. It enables complicated searches that are difficult to perform with existing tools and it also offers integration of custom annotations and in-house experimental data. We proposed an objective protocol for target prioritisation using TargetMine and set up a benchmarking procedure to evaluate its performance. The results show that the protocol can identify known disease-associated genes with high precision and coverage. A demonstration version of TargetMine is available at http://targetmine.nibio.go.jp/