BACKGROUND: The recent advances in human disease network have provided insights into establishing the relationships between the genotypes and phenotypes of diseases. In spite of the great progress, it yet remains as only a map of topologies between diseases, but not being able to be a pragmatic diagnostic/prognostic tool in medicine. It can further evolve from a map to a translational tool if it equips with a function of scoring that measures the likelihoods of the association between diseases. Then, a physician, when practicing on a patient, can suggest several diseases that are highly likely to co-occur with a primary disease according to the scores. In this study, we propose a method of implementing 'n-of-1 utility' (n potential diseases of one patient) to human disease network-the translational disease network. RESULTS: We first construct a disease network by introducing the notion of walk in graph theory to protein-protein interaction network, and then provide a scoring algorithm quantifying the likelihoods of disease co-occurrence given a primary disease. Metabolic diseases, that are highly prevalent but have found only a few associations in previous studies, are chosen as entries of the network. CONCLUSIONS: The proposed method substantially increased connectivity between metabolic diseases and provided scores of co-occurring diseases. The increase in connectivity turned the disease network info-richer. The result lifted the AUC of random guessing up to 0.72 and appeared to be concordant with the existing literatures on disease comorbidity.

This study aimed to investigate whether a touch-screen tablet device can be used to avoid underestimation of cognitive function due to hand disability in patients with stroke. Thirty patients with stroke and hemiplegia were divided according to whether the paretic side corresponded to their dominant or non-dominant hand. They were given the cube-copying task twice, once via paper and once via tablet. The results between paper- and tablet-based tasks were more likely to be concordant in participants with a non-paretic dominant hand (z = 2.80, p = 0.005) when the stroke type, education years, and sex were included in the model. All participants with discordant test results had a paretic dominant hand and showed lower muscle power in the wrist and hand muscles. In conclusion, cognitive function in patients with a paretic dominant hand may be underestimated in the cube-copying task, because it requires handwriting ability. This could be prevented by using a touch-screen device.

OBJECTIVE: We propose 2 Medical Dictionary for Regulatory Activities-enabled pharmacovigilance algorithms, MetaLAB and MetaNurse, powered by a per-year meta-analysis technique and improved subject sampling strategy. MATRIALS AND METHODS: This study developed 2 novel algorithms, MetaLAB for laboratory abnormalities and MetaNurse for standard nursing statements, as significantly improved versions of our previous electronic health record (EHR)-based pharmacovigilance method, called CLEAR. Adverse drug reaction (ADR) signals from 117 laboratory abnormalities and 1357 standard nursing statements for all precautionary drugs ( n = 101) were comprehensively detected and validated against SIDER (Side Effect Resource) by MetaLAB and MetaNurse against 11 817 and 76 457 drug-ADR pairs, respectively. RESULTS: We demonstrate that MetaLAB (area under the curve, AUC = 0.61 ± 0.18) outperformed CLEAR (AUC = 0.55 ± 0.06) when we applied the same 470 drug-event pairs as the gold standard, as in our previous research. Receiver operating characteristic curves for 101 precautionary terms in the Medical Dictionary for Regulatory Activities Preferred Terms were obtained for MetaLAB and MetaNurse (0.69 ± 0.11; 0.62 ± 0.07), which complemented each other in terms of ADR signal coverage. Novel ADR signals discovered by MetaLAB and MetaNurse were successfully validated against spontaneous reports in the US Food and Drug Administration Adverse Event Reporting System database. DISCUSSION: The present study demonstrates the symbiosis of laboratory test results and nursing statements for ADR signal detection in terms of their system organ class coverage and performance profiles. CONCLUSION: Systematic discovery and evaluation of the wide spectrum of ADR signals using standard-based observational electronic health record data across many institutions will affect drug development and use, as well as postmarketing surveillance and regulation.

MOTIVATION: Genome-wide mapping of chromatin states is essential for defining regulatory elements and inferring their activities in eukaryotic genomes. A number of hidden Markov model (HMM)-based methods have been developed to infer chromatin state maps from genome-wide histone modification data for an individual genome. To perform a principled comparison of evolutionarily distant epigenomes, we must consider species-specific biases such as differences in genome size, strength of signal enrichment and co-occurrence patterns of histone modifications. RESULTS: Here, we present a new Bayesian non-parametric method called hierarchically linked infinite HMM (hiHMM) to jointly infer chromatin state maps in multiple genomes (different species, cell types and developmental stages) using genome-wide histone modification data. This flexible framework provides a new way to learn a consistent definition of chromatin states across multiple genomes, thus facilitating a direct comparison among them. We demonstrate the utility of this method using synthetic data as well as multiple modENCODE ChIP-seq datasets. CONCLUSION: The hierarchical and Bayesian non-parametric formulation in our approach is an important extension to the current set of methodologies for comparative chromatin landscape analysis. AVAILABILITY AND IMPLEMENTATION: Source codes are available at https://github.com/kasohn/hiHMM. Chromatin data are available at http://encode-x.med.harvard.edu/data_sets/chromatin/.

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.

Summary form only given. Biological networks often show a scale-free power-law distribution. Furthermore, leathal genes tend to form functional hubs whereas non-leathal disease genes are located at the periphery. Uni-dimensional analyses, however, are flawed. Here we report two distinct scale-free networks; a protein-protein interaction (PPI) and a perturbation-sensitivity (PSN) network. Hubs of both networks demonstrate a low molecular evolutionary rate and a high codon adaptation index, indicating that both hubs have been shaped under high evolutionary selective pressure. Moreover, the topologies of PPI and PSN are inversely proportional: hubs of PPI tend to be located at the periphery of PSN and vice versa. PPI hubs are highly enriched with lethal genes whereas PSN hubs with disease genes and drug targets. PPI network hubs are enriched with essential cellular processes whereas PSN hubs with environmental interactions like TATA boxes and transcription factor binding sites. It is concluded that biological systems may balance internal growth signaling and external stress signaling by unifying the two opposite scale-free networks that are reciprocal to each other but work in concert between death and disease.

OBJECTIVE: DNA methylation, a regulator of gene expression, plays an important role in diverse biological processes including developmental process, carcinogenesis and aging. In particular, aberrant DNA methylation has been largely observed in several types of cancers. Currently, it is important to extract disease-specific gene sets associated with the regulation of DNA methylation. MATERIALS AND METHODS: Here we propose a novel approach to find the minimum regulatory units of genes, co-methylated and co-expressed gene pairs (MEGP) that are highly correlated gene pairs between DNA methylation and gene expression showing the co-regulatory relationship. To evaluate whether our method is applicable to extract disease-associated genes, we applied our method to a large-scale dataset from the Cancer Genome Atlas extracting significantly associated MEGP and analyzed their functional correlation. RESULTS: We observed that many MEGP physically interacted with each other and showed high semantic similarity with gene ontology terms. Furthermore, we performed gene set enrichment tests to identify how they are correlated in a complex biological process. Our MEGP were highly enriched in the biological pathway associated with ovarian cancers. CONCLUSIONS: Our approach is useful for discovering coordinated epigenetic markers associated with specific diseases.

BACKGROUND: Semantic similarity analysis facilitates automated semantic explanations of biological and clinical data annotated by biomedical ontologies. Gene ontology (GO) has become one of the most important biomedical ontologies with a set of controlled vocabularies, providing rich semantic annotations for genes and molecular phenotypes for diseases. Current methods for measuring GO semantic similarities are limited to considering only the ancestor terms while neglecting the descendants. One can find many GO term pairs whose ancestors are identical but whose descendants are very different and vice versa. Moreover, the lower parts of GO trees are full of terms with more specific semantics. METHODS: This study proposed a method of measuring semantic similarities between GO terms using the entire GO tree structure, including both the upper (ancestral) and the lower (descendant) parts. Comprehensive comparison studies were performed with well-known information content-based and graph structure-based semantic similarity measures with protein sequence similarities, gene expression-profile correlations, protein-protein interactions, and biological pathway analyses. CONCLUSION: The proposed bidirectional measure of semantic similarity outperformed other graph-based and information content-based methods.

Most methods for large-scale gene expression microarray and RNA-Seq data analysis are designed to determine the lists of genes or gene products that show distinct patterns and/or significant differences. The most challenging and rate-liming step, however, is to determine what the resulting lists of genes and/or transcripts biologically mean. Biomedical ontology and pathway-based functional enrichment analysis is widely used to interpret the functional role of tightly correlated or differentially expressed genes. The groups of genes are assigned to the associated biological annotations using Gene Ontology terms or biological pathways and then tested if they are significantly enriched with the corresponding annotations. Unlike previous approaches, Gene Set Enrichment Analysis takes quite the reverse approach by using pre-defined gene sets. Differential co-expression analysis determines the degree of co-expression difference of paired gene sets across different conditions. Outcomes in DNA microarray and RNA-Seq data can be transformed into the graphical structure that represents biological semantics. A number of biomedical annotation and external repositories including clinical resources can be systematically integrated by biological semantics within the framework of concept lattice analysis. This array of methods for biological knowledge assembly and interpretation has been developed during the past decade and clearly improved our biological understanding of large-scale genomic data from the high-throughput technologies.

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.

BACKGROUND: Although many biological databases are applying semantic web technologies, meaningful biological hypothesis testing cannot be easily achieved. Database-driven high throughput genomic hypothesis testing requires both of the capabilities of obtaining semantically relevant experimental data and of performing relevant statistical testing for the retrieved data. Tissue Microarray (TMA) data are semantically rich and contains many biologically important hypotheses waiting for high throughput conclusions. METHODS: An application-specific ontology was developed for managing TMA and DNA microarray databases by semantic web technologies. Data were represented as Resource Description Framework (RDF) according to the framework of the ontology. Applications for hypothesis testing (Xperanto-RDF) for TMA data were designed and implemented by (1) formulating the syntactic and semantic structures of the hypotheses derived from TMA experiments, (2) formulating SPARQLs to reflect the semantic structures of the hypotheses, and (3) performing statistical test with the result sets returned by the SPARQLs. RESULTS: When a user designs a hypothesis in Xperanto-RDF and submits it, the hypothesis can be tested against TMA experimental data stored in Xperanto-RDF. When we evaluated four previously validated hypotheses as an illustration, all the hypotheses were supported by Xperanto-RDF. CONCLUSIONS: We demonstrated the utility of high throughput biological hypothesis testing. We believe that preliminary investigation before performing highly controlled experiment can be benefited.

MOTIVATION: The small number of samples in many microarray experiments is a challenge for the correct identification of differentially expressed gens (DEGs) by conventional statistical means. Information from public microarray databases can help more efficient identification of DEGs. To model various experimental conditions of a public microarray database, we applied Gaussian mixture model and extracted bi- or tri-modal distributions of gene expression. Prior variance of Baldi's Bayesian framework was estimate for the analysis of the small sample-sized datasets. RESULTS: First, we estimated the prior variance of a gene expression by pooling variances obtained from mixture modeling of large samples in the public microarray database. Then, using the prior variance, we identified DEGs in small sample-sized test datasets using the Baldi's framework. For benchmark study, we generated test datasets having several samples from relatively large datasets. Our proposed method outperformed other benchmark methods in terms of detecting gold-standard DEGs from the test datasets. The results may be a challenging evidence for usage of public microarray databases in microarray data analysis.

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.

BACKGROUND: Previous differential coexpression analyses focused on identification of differentially coexpressed gene pairs, revealing many insightful biological hypotheses. However, this method could not detect coexpression relationships between pairs of gene sets. Considering the success of many set-wise analysis methods for microarray data, a coexpression analysis based on gene sets may elucidate underlying biological processes provoked by the conditional changes. Here, we propose a differentially coexpressed gene sets (dCoxS) algorithm that identifies the differentially coexpressed gene set pairs between conditions. RESULTS: dCoxS is a two-step analysis method. In each condition, dCoxS measures the interaction score (IS), which represents the expression similarity between two gene sets using Renyi relative entropy. When estimating the relative entropy, multivariate kernel density estimation was used to model gene-gene correlation structure. Statistical tests for the conditional difference between the ISs determined the significance of differential coexpression of the gene set pair. Simulation studies supported that the IS is a representative measure of similarity between gene expression matrices. Single gene coexpression analysis of two publicly available microarray datasets detected no significant results. However, the dCoxS analysis of the datasets revealed differentially coexpressed gene set pairs related to the biological conditions of the datasets. CONCLUSION: dCoxS identified differentially coexpressed gene set pairs not found by single gene analysis. The results indicate that set-wise differential coexpression analysis is useful for understanding biological processes induced by conditional changes.

Genetical genomics has been established to study genetic variation of gene expression. It treats transcript expression as a quantitative trait and identifies putative regulatory loci for the expression of each gene. It is, however, well known that biological functions are often carried out through concerted activity of multiple genes. Therefore, we studied genetic regulators of biological pathways in lymphoblastoid cells. To identify the association of genomic loci and pathways, we applied two genetical genomics approaches, principal component analysis (PCA) and the differential allelic co - expression (DACE) test. We found that PCA is inadequate to identify regulatory loci for functional pathways. Association between a principal component, which summarizes the expression of a certain pathway, and a given marker was observed only when a direct association existed between the marker and the gene contributing most to that principal component. Another approach was the DACE test, a method that identifies regulatory loci that affect the inter-correlation structure of a gene set. Utilizing this test, we identified seven putative regulatory loci of immune-related pathways in lymphoblastoid cells after controlling for family-wise error rate.

The genetical genomics approach has been used to study the genetic basis of variation in gene expression, where putative transcriptional regulators of genes are identified via genetic quantitative trait mapping. The genetic regulators identified through such efforts can partially account for an individual gene's natural variation. However, genes in a molecular pathway often exhibit coordinated activities, the patterns and levels of which are also regulated. In an effort to understand these complicated mechanisms, we propose a method that searches for the genomic regulators of set-wise co-expression of related genes, based on current genetical genomics data. Using this method, we studied genomic regulators of 233 biological pathways for a BXD RI data set. For 15 pathways, we obtained significant regulatory loci after controlling for the false discovery rate. The results presented in this paper constitute important evidence of the heritability of mRNA co-expression between individuals. We have shown that, by defining new phenotypes using existing genetical genomics data, evidence on regulation of co-expression can be derived.

BACKGROUND: There are some limitations associated with conventional clustering methods for short time-course gene expression data. The current algorithms require prior domain knowledge and do not incorporate information from replicates. Moreover, the results are not always easy to interpret biologically. RESULTS: We propose a novel algorithm for identifying a subset of genes sharing a significant temporal expression pattern when replicates are used. Our algorithm requires no prior knowledge, instead relying on an observed statistic which is based on the first and second order differences between adjacent time-points. Here, a pattern is predefined as the sequence of symbols indicating direction and the rate of change between time-points, and each gene is assigned to a cluster whose members share a similar pattern. We evaluated the performance of our algorithm to those of K-means, Self-Organizing Map and the Short Time-series Expression Miner methods. CONCLUSIONS: Assessments using simulated and real data show that our method outperformed aforementioned algorithms. Our approach is an appropriate solution for clustering short time-course microarray data with replicates.

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.

SUMMARY: ChromoViz is an R package for the visualization of microarray gene expression data, cross-species and cross-platform comparisons, as well as non-expression genomic data obtained from public databases onto chromosomes. Chromosomal visualization format is proposed for the clear decoupling of the data layer from the procedure layer and the combined visualization of genomic data from heterogeneous data sources. Visualization with Javascript-enabled scalable vector graphics enables interactive visualization and navigation of data objects on the Web. AVAILABILITY: http://www.snubi.org/software/ChromoViz/

Cluster analysis is the most important method for analyzing large-scale gene expression patterns. The matrix representation of microarray data and its successive 'optimal' incisional hyperplanes that create top-down hierarchical tree are a useful platform for developing optimization algorithms to determine the 'optimal' clusters from a pairwise proximity matrix which represents completely connected and weighted graph. Evolution strategy is applied to determine the 'globally optimal' incisional hyperplanes to construct hierarchical tree structure and tested with Fisher's iris and Golub's leukemia data sets. The results were compared with those of bottom-up hierarchical clustering, K-means and SOMs (Self-Organizing Maps) algorithms with promising results.

A watermarking scheme is presented which embeds two different watermarks to the same frequency and extracts the marks with two different methods in the frequency domain. One of the two extraction methods needs a source image, whereas the other does not. Each extraction method uses a unique operation in extracting the watermark. The use of two watermark schemes is more effective in claiming rightful ownership. Furthermore, this watermarking is non-invertible and is robust against IBM attack as it obtains two different extraction results from the same image.