To exploit large-scale biomedical data, the application of common data models and the establishment of data networks are being actively carried out worldwide. However, due to the privacy issues, it is difficult to share data distributed among institutions. In this study, we developed and evaluated weight-based integrated Cox model (WICOX) as a privacy-protecting method without sharing patient-level information across institutions. WICOX generates a weight for each institutional model and builds an integrated model of multi-institutional data based on these weights. WICOX does not require iterative communication until the centralized parameter converges. We performed experiments to show the weight characteristic of our algorithm based on 10 hospitals (2910 intensive care unit (ICU) stays in total) from the electronic intensive care unit Collaborative Research Database to predict time to ICU mortality with eight risk factors. Compared with the centralized Cox model, WICOX showed biases from 0 to 0.68E-2, from 0.00E-2 to 4.98E-2, and from 0.74E-2 to 1.7E-2 for time-dependent AUC, log hazard ratio, and survival rate, respectively. In addition, through simulation results using real 10 hospitals, WICOX showed robust results in accuracy under any composition of hospitals. The results of the experiments highlight that WICOX has robust characteristics and provides predictive performance and statistical inference results nearly the same as those of the centralized model. WICOX is a non-iterative method using the weight of institutional model for implementing the Cox model across multiple institutions in a privacy-preserving manner.

OBJECTIVE: Cancer can involve gene dysregulation via multiple mechanisms, so no single level of genomic data fully elucidates tumor behavior due to the presence of numerous genomic variations within or between levels in a biological system. We have previously proposed a graph-based integration approach that combines multi-omics data including copy number alteration, methylation, miRNA, and gene expression data for predicting clinical outcome in cancer. However, genomic features likely interact with other genomic features in complex signaling or regulatory networks, since cancer is caused by alterations in pathways or complete processes. METHODS: Here we propose a new graph-based framework for integrating multi-omics data and genomic knowledge to improve power in predicting clinical outcomes and elucidate interplay between different levels. To highlight the validity of our proposed framework, we used an ovarian cancer dataset from The Cancer Genome Atlas for predicting stage, grade, and survival outcomes. RESULTS: Integrating multi-omics data with genomic knowledge to construct pre-defined features resulted in higher performance in clinical outcome prediction and higher stability. For the grade outcome, the model with gene expression data produced an area under the receiver operating characteristic curve (AUC) of 0.7866. However, models of the integration with pathway, Gene Ontology, chromosomal gene set, and motif gene set consistently outperformed the model with genomic data only, attaining AUCs of 0.7873, 0.8433, 0.8254, and 0.8179, respectively. CONCLUSIONS: Integrating multi-omics data and genomic knowledge to improve understanding of molecular pathogenesis and underlying biology in cancer should improve diagnostic and prognostic indicators and the effectiveness of therapies.

BACKGROUND: The Gene Ontology (GO) provides a controlled vocabulary for describing genes and gene products. In spite of the undoubted importance of GO, several drawbacks associated with GO and GO-based annotations have been introduced. We identified three types of semantic inconsistencies in GO-based annotations; semantically redundant, biological-domain inconsistent and taxonomy inconsistent annotations. METHODS: To determine the semantic inconsistencies in GO annotation, we used the hierarchical structure of GO graph and tree structure of NCBI taxonomy. Twenty seven biological databases were collected for finding semantic inconsistent annotation. RESULTS: The distributions and possible causes of the semantic inconsistencies were investigated using twenty seven biological databases with GO-based annotations. We found that some evidence codes of annotation were associated with the inconsistencies. The numbers of gene products and species in a database that are related to the complexity of database management are also in correlation with the inconsistencies. Consequently, numerous annotation errors arise and are propagated throughout biological databases and GO-based high-level analyses. GOChase-II is developed to detect and correct both syntactic and semantic errors in GO-based annotations. CONCLUSIONS: We identified some inconsistencies in GO-based annotation and provided software, GOChase-II, for correcting these semantic inconsistencies in addition to the previous corrections for the syntactic errors by GOChase-I.

The importance of tissue microarrays (TMA) as clinical validation tools for cDNA microarray results is increasing, whereas researchers are still suffering from TMA data management issues. After we developed a comprehensive data model for TMA data storage, exchange and analysis, TMA-OM, we focused our attention on the development of a user-friendly exchange format with high expressivity in order to promote data communication of TMA results and TMA-OM supportive database applications. We developed TMA-TAB, a spreadsheet-based data format for TMA data submission to the TMA-OM supportive TMA database system. TMA-TAB was developed by simplifying, modifying and reorganizing classes, attributes and templates of TMA-OM into five entities: experiment, block, slide, core_in_block, and core_in_slide. Five tab-delimited formats (investigation design format, block description format, slide description format, core clinicohistopathological data format, and core result data format) were made, each representing the entities of experiment, block, slide, core_in_block, and core_in_slide. We implemented TMA-TAB import and export modules on Xperanto-TMA, a TMA-OM supportive database application, to facilitate data submission. Development and implementation of TMA-TAB and TMA-OM provide a strong infrastructure for powerful and user-friendly TMA data management.

SUMMARY: The Gene Ontology (GO) is a controlled biological vocabulary that provides three structured networks of terms to describe biological processes, cellular components and molecular functions. Many databases of gene products are annotated using the GO vocabularies. We found that some GO-updating operations are not easily traceable by the current biological databases and GO browsers. Consequently, numerous annotation errors arise and are propagated throughout biological databases and GO-based high-level analyses. GOChase is a set of web-based utilities to detect and correct the errors in GO-based annotations.