Drug repositioning using drug-disease vectors based on an integrated network.

BACKGROUND: Diverse interactions occur between biomolecules, such as activation, inhibition, expression, or repression. However, previous network-based studies of drug repositioning have employed interaction on the binary protein-protein interaction (PPI) network without considering the characteristics of the interactions. Recently, some studies of drug repositioning using gene expression data found that associations between drug and disease genes are useful information for identifying novel drugs to treat diseases. However, the gene expression profiles for drugs and diseases are not always available. Although gene expression profiles of drugs and diseases are available, existing methods cannot use the drugs or diseases, when differentially expressed genes in the profiles are not included in their network. RESULTS: We developed a novel method for identifying candidate indications of existing drugs considering types of interactions between biomolecules based on known drug-disease associations. To obtain associations between drug and disease genes, we constructed a directed network using protein interaction and gene regulation data obtained from various public databases providing diverse biological pathways. The network includes three types of edges depending on relationships between biomolecules. To quantify the association between a target gene and a disease gene, we explored the shortest paths from the target gene to the disease gene and calculated the types and weights of the shortest paths. For each drug-disease pair, we built a vector consisting of values for each disease gene influenced by the drug. Using the vectors and known drug-disease associations, we constructed classifiers to identify novel drugs for each disease. CONCLUSION: We propose a method for exploring candidate drugs of diseases using associations between drugs and disease genes derived from a directed gene network instead of gene regulation data obtained from gene expression profiles. Compared to existing methods that require information on gene relationships and gene expression data, our method can be applied to a greater number of drugs and diseases. Furthermore, to validate our predictions, we compared the predictions with drug-disease pairs in clinical trials using the hypergeometric test, which showed significant results. Our method also showed better performance compared to existing methods for the area under the receiver operating characteristic curve (AUC).

Improved prediction of breast cancer outcome by identifying heterogeneous biomarkers.

MOTIVATION: Identification of genes that can be used to predict prognosis in patients with cancer is important in that it can lead to improved therapy, and can also promote our understanding of tumor progression on the molecular level. One of the common but fundamental problems that render identification of prognostic genes and prediction of cancer outcomes difficult is the heterogeneity of patient samples. RESULTS: To reduce the effect of sample heterogeneity, we clustered data samples using K-means algorithm and applied modified PageRank to functional interaction (FI) networks weighted using gene expression values of samples in each cluster. Hub genes among resulting prioritized genes were selected as biomarkers to predict the prognosis of samples. This process outperformed traditional feature selection methods as well as several network-based prognostic gene selection methods when applied to Random Forest. We were able to find many cluster-specific prognostic genes for each dataset. Functional study showed that distinct biological processes were enriched in each cluster, which seems to reflect different aspect of tumor progression or oncogenesis among distinct patient groups. Taken together, these results provide support for the hypothesis that our approach can effectively identify heterogeneous prognostic genes, and these are complementary to each other, improving prediction accuracy. AVAILABILITY AND IMPLEMENTATION: https://github.com/mathcom/CPR. CONTACT: jgahn@inu.ac.kr. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

PISTON: Predicting drug indications and side effects using topic modeling and natural language processing.

The process of discovering novel drugs to treat diseases requires a long time and high cost. It is important to understand side effects of drugs as well as their therapeutic effects, because these can seriously damage the patients due to unexpected actions of the derived candidate drugs. In order to overcome these limitations, computational methods for predicting the therapeutic effects and side effects have been proposed. In particular, text mining is a widely used technique in the field of systems biology, because it can discover hidden relationships between drugs, genes and diseases from a large amount of literature data. Compared with in vivo/in vitro experiments, text mining derives meaningful results with less time and cost. In this study, we propose an algorithm for predicting novel drug-phenotype associations and drug-side effect associations using topic modeling and natural language processing (NLP). We extract sentences in which drugs and genes co-occur from the abstracts of the literature and identify words that describe the relationship between them using NLP. Considering the characteristics of the identified words, we determine if the drug has an up-regulation effect or a down-regulation effect on the gene. Based on genes that affect drugs and their regulatory relationships, we group the frequently occurring genes and regulatory relationships into topics, and build a drug-topic probability matrix by calculating the score that the drug will have a topic using topic modeling. Using the matrix, a classifier is constructed for predicting the novel indications and side effects of drugs considering the characteristics of known drug-phenotype associations or drug-side effect associations. The proposed method predicts both indications and side effects with a single algorithm, and it can exclude drugs with serious side effects or side effects that patients do not want to experience from among the candidate drugs provided for the treatment of the phenotype. Furthermore, lists of novel candidate drugs for phenotypes and side effects can be continuously updated with our algorithm every time a document is added. More than a thousand documents are produced per day, and it is possible for our algorithm to efficiently derive candidate drugs because it requires less cost than the existing drug repositioning methods. The resource of PISTON is available at databio.gachon.ac.kr/tools/PISTON.

Drug voyager: a computational platform for exploring unintended drug action.

BACKGROUND: The dominant paradigm in understanding drug action focuses on the intended therapeutic effects and frequent adverse reactions. However, this approach may limit opportunities to grasp unintended drug actions, which can open up channels to repurpose existing drugs and identify rare adverse drug reactions. Advances in systems biology can be exploited to comprehensively understand pharmacodynamic actions, although proper frameworks to represent drug actions are still lacking. RESULTS: We suggest a novel platform to construct a drug-specific pathway in which a molecular-level mechanism of action is formulated based on pharmacologic, pharmacogenomic, transcriptomic, and phenotypic data related to drug response ( http://databio.gachon.ac.kr/tools/ ). In this platform, an adoption of three conceptual levels imitating drug perturbation allows these pathways to be realistically rendered in comparison to those of other models. Furthermore, we propose a new method that exploits functional features of the drug-specific pathways to predict new indications as well as adverse reactions. For therapeutic uses, our predictions significantly overlapped with clinical trials and an up-to-date drug-disease association database. Also, our method outperforms existing methods with regard to classification of active compounds for cancers. For adverse reactions, our predictions were significantly enriched in an independent database derived from the Food and Drug Administration (FDA) Adverse Event Reporting System and meaningfully cover an Adverse Reaction Database provided by Health Canada. Lastly, we discuss several predictions for both therapeutic indications and side-effects through the published literature. CONCLUSIONS: Our study addresses how we can computationally represent drug-signaling pathways to understand unintended drug actions and to facilitate drug discovery and screening.

LGscore: A method to identify disease-related genes using biological literature and Google data.

Since the genome project in 1990s, a number of studies associated with genes have been conducted and researchers have confirmed that genes are involved in disease. For this reason, the identification of the relationships between diseases and genes is important in biology. We propose a method called LGscore, which identifies disease-related genes using Google data and literature data. To implement this method, first, we construct a disease-related gene network using text-mining results. We then extract gene-gene interactions based on co-occurrences in abstract data obtained from PubMed, and calculate the weights of edges in the gene network by means of Z-scoring. The weights contain two values: the frequency and the Google search results. The frequency value is extracted from literature data, and the Google search result is obtained using Google. We assign a score to each gene through a network analysis. We assume that genes with a large number of links and numerous Google search results and frequency values are more likely to be involved in disease. For validation, we investigated the top 20 inferred genes for five different diseases using answer sets. The answer sets comprised six databases that contain information on disease-gene relationships. We identified a significant number of disease-related genes as well as candidate genes for Alzheimer's disease, diabetes, colon cancer, lung cancer, and prostate cancer. Our method was up to 40% more accurate than existing methods.

The optimized CO2-added ammonia explosion pretreatment for bioethanol production from rice straw.

A CO2-added ammonia explosion pretreatment was performed for bioethanol production from rice straw. The pretreatment conditions, such as ammonia concentration, CO2 loading level, residence time, and temperature were optimized using response surface methodology. The response for optimization was defined as the glucose conversion rate. The optimized pretreatment conditions resulting in maximal glucose yield (93.6 %) were determined as 14.3 % of ammonia concentration, 2.2 MPa of CO2 loading level, 165.1 °C of temperature, and 69.8 min of residence time. Scanning electron microscopy analysis showed that pretreatment of rice straw strongly increased the surface area and pore size, thus increasing enzymatic accessibility for enzymatic saccharification. Finally, an ethanol yield of 97 % was achieved via simultaneous saccharification and fermentation. Thus, the present study suggests that CO2-added ammonia pretreatment is an appropriate process for bioethanol production from rice straw.

Identification of functional CNV region networks using a CNV-gene mapping algorithm in a genome-wide scale.

MOTIVATION: Identifying functional relation of copy number variation regions (CNVRs) and gene is an essential process in understanding the impact of genotypic variations on phenotype. There have been many related works, but only a few attempts were made to normal populations. RESULTS: To analyze the functions of genome-wide CNVRs, we applied a novel correlation measure called Correlation based on Sample Set (CSS) to paired Whole Genome TilePath array and messenger RNA (mRNA) microarray data from 210 HapMap individuals with normal phenotypes and calculated the confident CNVR-gene relationships. Two CNVR nodes form an edge if they regulate a common set of genes, allowing the construction of a global CNVR network. We performed functional enrichment on the common genes that were trans-regulated from CNVRs clustered together in our CNVR network. As a result, we observed that most of CNVR clusters in our CNVR network were reported to be involved in some biological processes or cellular functions, while most CNVR clusters from randomly constructed CNVR networks showed no evidence of functional enrichment. Those results imply that CSS is capable of finding related CNVR-gene pairs and CNVR networks that have functional significance. AVAILABILITY: http://embio.yonsei.ac.kr/~ Park/cnv_net.php. CONTACT: sanghyun@cs.yonsei.ac.kr SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Improved method for protein complex detection using bottleneck proteins.

BACKGROUND: Detecting protein complexes is one of essential and fundamental tasks in understanding various biological functions or processes. Therefore accurate identification of protein complexes is indispensable. METHODS: For more accurate detection of protein complexes, we propose an algorithm which detects dense protein sub-networks of which proteins share closely located bottleneck proteins. The proposed algorithm is capable of finding protein complexes which allow overlapping with each other. RESULTS: We applied our algorithm to several PPI (Protein-Protein Interaction) networks of Saccharomyces cerevisiae and Homo sapiens, and validated our results using public databases of protein complexes. The prediction accuracy was even more improved over our previous work which used also bottleneck information of the PPI network, but showed limitation when predicting small-sized protein complex detection. CONCLUSIONS: Our algorithm resulted in overlapping protein complexes with significantly improved F1 score over existing algorithms. This result comes from high recall due to effective network search, as well as high precision due to proper use of bottleneck information during the network search.

Integrative gene network construction for predicting a set of complementary prostate cancer genes.

MOTIVATION: Diagnosis and prognosis of cancer and understanding oncogenesis within the context of biological pathways is one of the most important research areas in bioinformatics. Recently, there have been several attempts to integrate interactome and transcriptome data to identify subnetworks that provide limited interpretations of known and candidate cancer genes, as well as increase classification accuracy. However, these studies provide little information about the detailed roles of identified cancer genes. RESULTS: To provide more information to the network, we constructed the network by incorporating genetic interactions and manually curated gene regulations to the protein interaction network. To make our newly constructed network cancer specific, we identified edges where two genes show different expression patterns between cancer and normal phenotypes. We showed that the integration of various datasets increased classification accuracy, which suggests that our network is more complete than a network based solely on protein interactions. We also showed that our network contains significantly more known cancer-related genes than other feature selection algorithms. Through observations of some examples of cancer-specific subnetworks, we were able to predict more detailed and interpretable roles of oncogenes and other cancer candidate genes in the prostate cancer cells. AVAILABILITY: http://embio.yonsei.ac.kr/~Ahn/tc.php. CONTACT: sanghyun@cs.yonsei.ac.kr

Exploring disease comorbidity in a module-module interaction network.

Understanding disease comorbidity contributes to improved quality of life in patients who are suffering from multiple diseases. Therefore, to better explore comorbid diseases, the clarification of associations between diseases based on biological functions is essential. In our study, we propose a method for identifying disease comorbidity in a module-based network, named the module-module interaction (MMI) network, which represents how biological functions influence each other. To construct the MMI network, we detected gene modules - sets of genes that have a higher probability of taking part in specific functions - and established a link between these modules. Subsequently, we constructed disease-related networks in the MMI network to understand inherent disease mechanisms and calculated comorbidity scores of disease pairs using Gene Ontology (GO) terms. Our results show that we can obtain further information on disease mechanisms by considering interactions between functional modules instead of between genes. In addition, we verified that predicted comorbid relationships of disease pairs based on the MMI network are more significant than those based on the protein-protein interaction (PPI) network. This study can be useful to elucidate the mechanisms underlying comorbidities for further study, which will provide a broader insight into the pathogenesis of diseases.

Prediction of Side Effects Using Comprehensive Similarity Measures.

Identifying the potential side effects of drugs is crucial in clinical trials in the pharmaceutical industry. The existing side effect prediction methods mainly focus on the chemical and biological properties of drugs. This study proposes a method that uses diverse information such as drug-drug interactions from DrugBank, drug-drug interactions from network, single nucleotide polymorphisms, and side effect anatomical hierarchy as well as chemical structures, indications, and targets. The proposed method is based on the assumption that properties used in drug repositioning studies could be utilized to predict side effects because the phenotypic expression of a side effect is similar to that of the disease. The prediction results using the proposed method showed a 3.5% improvement in the area under the curve (AUC) over that obtained when only chemical, indication, and target features were used. The random forest model delivered outstanding results for all combinations of feature types. Finally, after identifying candidate side effects of drugs using the proposed method, the following four popular drugs were discussed: (1) dasatinib, (2) sitagliptin, (3) vorinostat, and (4) clonidine.

Time-Dependent Anti-Demineralization Effect of Silver Diamine Fluoride.

This study compared the demineralization resistance of teeth treated with silver diamine fluoride (SDF) to that treated with fluoride varnish. A total of 105 healthy bovine incisors were divided into control, fluoride varnish, and SDF groups. The enamel surface density change was then measured by micro-computed tomography (micro-CT) at three depths. The demineralized zone volume was measured on 3D micro-CT images to evaluate the total demineralization rate. The enamel surface morphology was assessed by scanning electron microscope. The enamel density had continuously decreased while demineralization increased in the control and fluoride varnish groups. The enamel density had increased in the SDF group till the 7th day of demineralization treatment and decreased thereafter. However, the decrease in the SDF group was less severe than that in the other groups (p < 0.05). The demineralized enamel volume had increased through treatment and was the highest in the control group, followed by the fluoride varnish and SDF group. The enamel surface morphology was the roughest and most irregular in the control group, followed by the fluoride varnish group and SDF groups.

Comparative Pathogenicity of Fusarium graminearum Isolates from Wheat Kernels in Korea.

Fusarium head blight (FHB) caused by Fusarium species is a major disease of wheat and barley around the world. FHB causes yield reductions and contamination of grains with trichothecene mycotoxins including; nivalenol (NIV), deoxynivalenol (DON), 3-acetyldeoxynivalenol (3-ADON), and 15-acetylde-oxynivalenol (15-ADON). The objectives of this study were to identify strains of F. graminearum isolated in Korea from 2012-harvested wheat grain and to test the pathogenicity of these NIV- and DON-producing isolates. Three hundred and four samples of wheat grain, harvested in 2012 in Chungnam, Chungbuk, Gyeongnam, Jeonbuk, Jeonnam, and Gangwon provinces were collected. We recovered 44 isolates from the 304 samples, based on the PCR amplification of internal transcribed spacer (ITS) rRNA region and sequencing. Our findings indicate that F. asiaticum was the predominant (95% of all isolates) species in Korea. We recovered both F. asiaticum and F. graminearum from samples collected in Chungnam province. Of the 44 isolates recovered, 36 isolates had a NIV genotype while 8 isolates belonged to the DON genotype (3-ADON and 15-ADON). In order to characterize the pathogenicity of the strains collected, disease severity was assessed visually on various greenhouse-grown wheat cultivars inoculated using both NIV- and DON-producing isolates. Our results suggest that Korean F. graminearum isolates from wheat belong to F. asiaticum producing NIV, and both F. graminearum and F. asiaticum are not significantly different on virulence in wheat cultivars.

Literature-based prediction of novel drug indications considering relationships between entities.

There have been many attempts to identify and develop new uses for existing drugs, which is known as drug repositioning. Among these efforts, text mining is an effective means of discovering novel knowledge from a large amount of literature data. We identify a gene regulation by a drug and a phenotype based on the biomedical literature. Drugs or phenotypes can activate or inhibit gene regulation. We calculate the therapeutic possibility that a drug acts on a phenotype by means of these two types of regulation. We assume that a drug treats a phenotype if the genes regulated by the phenotype are inversely correlated with the genes regulated by the drug. Based on this hypothesis, we identify drug-phenotype associations with therapeutic possibility. To validate the drug-phenotype associations predicted by our method, we make an enrichment comparison with known drug-phenotype associations. We also identify candidate drugs for drug repositioning from novel associations and thus reveal that our method is a novel approach to drug repositioning.

Identifying the common genetic networks of ADR (adverse drug reaction) clusters and developing an ADR classification model.

Adverse drug reactions (ADRs) are one of the major concerns threatening public health and have resulted in failures in drug development. Thus, predicting ADRs and discovering the mechanisms underlying ADRs have become important tasks in pharmacovigilance. Identification of potential ADRs by computational approaches in the early stages would be advantageous in drug development. Here we propose a computational method that elucidates the action mechanisms of ADRs and predicts potential ADRs by utilizing ADR genes, drug features, and protein-protein interaction (PPI) networks. If some ADRs share similar features, there is a high possibility that they may appear together in a drug and share analogous mechanisms. Proceeding from this assumption, we clustered ADRs according to interactions of ADR genes in the PPI networks and the frequency of co-occurrence of ADRs in drugs. ADR clusters were verified based on a side effect database and literature data regarding whether ADRs have relevance to other ADRs in the same cluster. Gene networks shared by ADRs in each cluster were constructed by cumulating the shortest paths between drug target genes and ADR genes in the PPI network. We developed a classification model to predict potential ADRs using these gene networks shared by ADRs and calculated cross-validation AUC (area under the curve) values for each ADR cluster. In addition, in order to demonstrate correlations between gene networks shared by ADRs and ADRs in a cluster, we applied the Wilcoxon rank sum statistical test to the literature data and results of a Google query search. We attained statistically meaningful p-values (<0.05) for every ADR cluster. The results suggest that our approach provides insights into discovering the action mechanisms of ADRs and is a novel attempt to predict ADRs in a biological aspect.

Systematic identification of an integrative network module during senescence from time-series gene expression.

BACKGROUND: Cellular senescence irreversibly arrests growth of human diploid cells. In addition, recent studies have indicated that senescence is a multi-step evolving process related to important complex biological processes. Most studies analyzed only the genes and their functions representing each senescence phase without considering gene-level interactions and continuously perturbed genes. It is necessary to reveal the genotypic mechanism inferred by affected genes and their interaction underlying the senescence process. RESULTS: We suggested a novel computational approach to identify an integrative network which profiles an underlying genotypic signature from time-series gene expression data. The relatively perturbed genes were selected for each time point based on the proposed scoring measure denominated as perturbation scores. Then, the selected genes were integrated with protein-protein interactions to construct time point specific network. From these constructed networks, the conserved edges across time point were extracted for the common network and statistical test was performed to demonstrate that the network could explain the phenotypic alteration. As a result, it was confirmed that the difference of average perturbation scores of common networks at both two time points could explain the phenotypic alteration. We also performed functional enrichment on the common network and identified high association with phenotypic alteration. Remarkably, we observed that the identified cell cycle specific common network played an important role in replicative senescence as a key regulator. CONCLUSIONS: Heretofore, the network analysis from time series gene expression data has been focused on what topological structure was changed over time point. Conversely, we focused on the conserved structure but its context was changed in course of time and showed it was available to explain the phenotypic changes. We expect that the proposed method will help to elucidate the biological mechanism unrevealed by the existing approaches.

[Working conditions in home health care nursing: a survey of home care nurses in hospitals in Korea].

PURPOSE: The purpose of this research was to address the working conditions of home health nurses through a nationwide home health agency survey conducted at hospitals. METHOD: The mail surveys were sent to 303 home health nurses nation wide and returned with a response rate of 71.8%. RESULT: (a) Seventy-five percent of home health agencies were established within the past 5 years and half of home health nurses are over 40 years old. (b) Working conditions were considered as follows: Seventy-one percent of respondents were full-time employees, sixty-six percent of home health nurses had unscheduled visits on a regular day of duty and forty-eight percent were on vacation. Fifty-one percent of home health nurses have experienced traffic accidents and paid penalties (65.9%). Self-reported monthly income level per year was an average of 28,364,000 won. (c) Rates were significantly higher for shoulder pain (61.5%), lower back pain (54.1%), knee pain (39.4%), and gastrointestinal problems (33.0%). CONCLUSION: These baseline results show the importance of improving home health nursing working conditions, a comprehensive prevention system and safeguards from physical discomfort.

Protein localization vector propagation: a method for improving the accuracy of drug repositioning.

Identifying alternative indications for known drugs is important for the pharmaceutical industry. Many computational methods have been proposed for predicting unknown associations between drugs and target proteins associated with diseases. To produce better prediction, researchers should not only develop accurate algorithms but identify good features that reflect intracellular systems. In this paper, we proposed a novel method for exploiting protein localization. We generated localization vectors (LVs) from protein localization and propagated LVs through a protein interaction network to increase the coverage of the localization information. The LVs showed distinct patterns among targets of known drugs as well as independent characteristics compared to existing features. Based on the experimental results, we determined that including LVs improves cross-validation accuracy and, produces better novel predictions with real and independent clinical trial data. Moreover, the propagation of LVs showed a positive result that it can help in increasing the coverage of the prediction results.

A literature-driven method to calculate similarities among diseases.

BACKGROUND: "Our lives are connected by a thousand invisible threads and along these sympathetic fibers, our actions run as causes and return to us as results". It is Herman Melville's famous quote describing connections among human lives. To paraphrase the Melville's quote, diseases are connected by many functional threads and along these sympathetic fibers, diseases run as causes and return as results. The Melville's quote explains the reason for researching disease-disease similarity and disease network. Measuring similarities between diseases and constructing disease network can play an important role in disease function research and in disease treatment. To estimate disease-disease similarities, we proposed a novel literature-based method. METHODS AND RESULTS: The proposed method extracted disease-gene relations and disease-drug relations from literature and used the frequencies of occurrence of the relations as features to calculate similarities among diseases. We also constructed disease network with top-ranking disease pairs from our method. The proposed method discovered a larger number of answer disease pairs than other comparable methods and showed the lowest p-value. CONCLUSIONS: We presume that our method showed good results because of using literature data, using all possible gene symbols and drug names for features of a disease, and determining feature values of diseases with the frequencies of co-occurrence of two entities. The disease-disease similarities from the proposed method can be used in computational biology researches which use similarities among diseases.

A method of extracting disease-related microRNAs through the propagation algorithm using the environmental factor based global miRNA network.

MicroRNAs (miRNA) are known to be involved in the development of various diseases. Hence various scientists in the field have been utilized computational analyses to determine the relationship between miRNA and diseases. However, the knowledge of miRNA and disease is still very limited. Therefore, we combined Environmental Factor (EF) data to a miRNA global network. Increasing research has shown that relationship between miRNAs and EFs play a significant role in classifying types of diseases. Environmental Factors consist of radiation, drugs, viruses, alcohol, cigarettes, and stress. Our global network considered all the interactions between every pair of miRNAs, which has led to precise analyses in comparison to local networks. As a result, our approaches' performance demonstrated its effectiveness in identifying disease-related miRNA and this is the area under the ROC curve (AUC) of 74.46%. Furthermore, comparative experiment has shown that our approach performs comparable to other existing methods with an accuracy of 94%, 90% and 96% for breast cancer, colonic cancer, and lung cancer respectively. In conclusion, these results support that our research has broadened new biological insights on identifying disease-related miRNAs.