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.

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.

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.

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.

Comparative Pathogenicity of Isolates from Wheat Kernels in Korea.

Fusarium head blight (FHB) caused by 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 isolated in Korea from 2012-harvested wheat grain and to test the pathogenicity of these NIV- and DON-producing isolates.

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.

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.

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.

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.

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.

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.

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.

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.

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.

Continuous alkaline pretreatment of Miscanthus sacchariflorus using a bench-scale single screw reactor.

Miscanthus sacchariflorus 'Goedae-Uksae 1' (GU) was developed as an energy crop of high productivity in Korea. For the practical use of GU for bioethanol production, a bench-scale continuous pretreatment system was developed. The reactor performed screw extrusion, soaking and thermochemical pretreatment at the following operating conditions: 3 mm particle size, 22% moisture content, 140 °C reaction temperature, 8 min residence time, 15 g/min biomass feeding and 120 mL/min NaOH input.

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.

A network-based classification model for deriving novel drug-disease associations and assessing their molecular actions.

The growing number and variety of genetic network datasets increases the feasibility of understanding how drugs and diseases are associated at the molecular level. Properly selected features of the network representations of existing drug-disease associations can be used to infer novel indications of existing drugs. To find new drug-disease associations, we generated an integrative genetic network using combinations of interactions, including protein-protein interactions and gene regulatory network datasets.

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.

Impact of TGF-b on breast cancer from a quantitative proteomic analysis.

There has been much active research in bioinformatics to support our understanding of oncogenesis and tumor progression. Most research relies on mRNA gene expression data to identify marker genes or cancer specific gene networks. However, considering that proteins are functional molecules that carry out the biological tasks of genes, they can be direct markers of biological functions.

ICP: A novel approach to predict prognosis of prostate cancer with inner-class clustering of gene expression data.

Prostate cancer has heterogeneous characteristics. For that reason, even if tumors appear histologically similar to each other, there are many cases in which they are actually different, based on their gene expression levels. A single tumor may have multiple expression levels with both high-risk cancer genes and low-risk cancer genes.

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.

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.

CNV detection method optimized for high-resolution arrayCGH by normality test.

High-resolution arrayCGH platform makes it possible to detect small gains and losses which previously could not be measured. However, current CNV detection tools fitted to early low-resolution data are not applicable to larger high-resolution data. When CNV detection tools are applied to high-resolution data, they suffer from high false-positives, which increases validation cost.

A multi-sample based method for identifying common CNVs in normal human genomic structure using high-resolution aCGH data.

Background: It is difficult to identify copy number variations (CNV) in normal human genomic data due to noise and non-linear relationships between different genomic regions and signal intensity. A high-resolution array comparative genomic hybridization (aCGH) containing 42 million probes, which is very large compared to previous arrays, was recently published. Most existing CNV detection algorithms do not work well because of noise associated with the large amount of input data and because most of the current methods were not designed to analyze normal human samples.