Jaejoon Choi

Generation and application of drug indication inference models using typed network motif comparison analysis.

BackgroundAs the amount of publicly available biomedical data increases, discovering hidden knowledge from biomedical data (i.e., Undiscovered Public Knowledge (UPK) proposed by Swanson) became an important research topic in the biological literature mining field. Drug indication inference, or drug repositioning, is one of famous UPK tasks, which infers alternative indications for approved drugs. Many previous studies tried to find novel candidate indications of existing drugs, but these works have following limitations: 1) models are not fully automated which required manual modulations to desired tasks, 2) are not able to cover various biomedical entities, and 3) have inference limitations that those works could infer only pre-defined cases using limited patterns. To overcome these problems, we suggest a new drug indication inference model.MethodsIn this paper, we adopted the Typed Network Motif Comparison Algorithm (TNMCA) to infer novel drug indications using topology of given network. Typed Network Motifs (TNM) are network motifs, which store types of data, instead of values of data. TNMCA is a powerful inference algorithm for multi-level biomedical interaction data as TNMs depend on the different types of entities and relations. We utilized a new normalized scoring function as well as network exclusion to improve the inference results. To validate our method, we applied TNMCA to a public database, Comparative Toxicogenomics Database (CTD).ResultsThe results show that enhanced TNMCA was able to infer meaningful indications with high performance (AUC = 0.801, 0.829) compared to the ABC model (AUC = 0.7050) and previous TNMCA model (AUC = 0.5679, 0.7469). The literature analysis also shows that TNMCA inferred meaningful results.ConclusionsWe proposed and enhanced a novel drug indication inference model by incorporating topological patterns of given network. By utilizing inference models from the topological patterns, we were able to improve inference power in drug indication inferences.

The Effects of Injector Temperature on Spray and Combustion Characteristics in a Single Cylinder DISI Engine

The authors would like to thank the support of NRL (National Research Laboratory) project of Korea.

Simulation of Fuel/Air Mixture Formation for Heavy Duty Liquid Phase LPG Injection (LPLI) Engines

Submodels are developed for injection, evaporation and wall impingement of a liquid LPG spray. The injection model determines the quality of fuel as two-phase choke flow at the nozzle exit. Wind tunnel experiments show the spray penetration more sensitive to ambient flow velocity than to injection pressure. Most evaporation occurs during choking, while heat transfer from surrounding air has a negligible effect on downstream droplet sizes. Three dimensional simulation shows that the bathtub cavity is better than the dog-dish cavity for stable flame propagation in lean-burn conditions. The injection timing during the IVC period has a negligible effect, while injection during an intake stroke enhances fuel/air mixing to result in more homogeneous cylinder charge.

Context-specific functional module based drug efficacy prediction.

BackgroundIt is necessary to evaluate the efficacy of individual drugs on patients to realize personalized medicine. Testing drugs on patients in clinical trial is the only way to evaluate the efficacy of drugs. The approach is labour intensive and requires overwhelming costs and a number of experiments. Therefore, preclinical model system has been intensively investigated for predicting the efficacy of drugs. Current computational drug sensitivity prediction approaches use general biological network modules as their prediction features. Therefore, they miss indirect effectors or the effects from tissue-specific interactions.ResultsWe developed cell line specific functional modules. Enriched scores of functional modules are utilized as cell line specific features to predict the efficacy of drugs. Cell line specific functional modules are clusters of genes, which have similar biological functions in cell line specific networks. We used linear regression for drug efficacy prediction. We assessed the prediction performance in leave-one-out cross-validation (LOOCV). Our method was compared with elastic net model, which is a popular model for drug efficacy prediction. In addition, we analysed drug sensitivity-associated functions of five drugs - lapatinib, erlotinib, raloxifene, tamoxifen and gefitinib- by our model.ConclusionsOur model can provide cell line specific drug efficacy prediction and also provide functions which are associated with drug sensitivity. Therefore, we could utilize drug sensitivity associated functions for drug repositioning or for suggesting secondary drugs for overcoming drug resistance.

TNMCA: generation and application of network motif based inference models for drug repositioning

Since the increase of the public biomedical data, Undiscovered Public Knowledge (UPK, proposed by Swanson) became an important research topic in the biological field. Drug repositioning is one of famous UPK tasks which infer alternative indications for approved drugs. Many researchers tried to find novel candidates of existing drugs, but these previous works are not fully automated which required manual modulations to desired tasks, and was not able to cover various biomedical entities. In addition, they had inference limitations that those works could infer only pre-defined cases using limited patterns. In this paper, we propose the Typed Network Motif Comparison Algorithm (TNMCA) to discover novel drug indications using topological patterns of data. Typed network motifs (TNM) are connected sub-graphs of data, which store types of data, instead of values of data. While previous researches depends on ABC model (or extension of it), TNMCA utilizes more generalized patterns as its inference models. Also, TNMCA can infer not only an existence of interaction, but also the type of the interaction. TNMCA is suited for multi-level biomedical interaction data as TNMs depend on the different types of entities and relations. We apply TNMCA to a public database, Comparative Toxicogenomics Database (CTD), to validate our method. The results show that TNMCA could infer meaningful indications with high performance (AUC=0.7469) compared to the ABC model (AUC=0.7050).

Measurements of droplet size distribution and in-cylinder mixture formation from a slit injector in a direct-injection gasoline engine

The droplet size distribution and in-cylinder mixture formation of a slit injector were investigated under varied fuel temperature and air flow conditions. This variance in fuel temperature and air flow represents the altered spray momentum and external forces acting upon the spray. Phase Doppler anemometry (PDA) was used to investigate the effect of fuel temperature and air flow on droplet size distribution. The in-cylinder mixture formation process and the factors affecting the in-cylinder mixture distribution were analyzed under various fuel temperature and air flow conditions using laser induced fluorescence (LIF). When the fuel temperature and air flow velocity increased, the smaller droplets were entrained to the upper and central parts of the spray altering the initial droplet size distribution. The reduced spray momentum decreased the spray penetration in the combustion chamber, and the interaction between the spray and piston bowl was degraded. This phenomenon eventually caused a relatively lean and dispersed mixture distribution near the spark plug at high fuel temperatures. The optimal spray momentum and external force depend on the fuel quantity (air-fuel ratio) and piston bowl shape. Consequently, the spray momentum and the external forces acting upon the spray should be optimized to form the stoichiometric and well-distributed mixture near the spark plug.

BSML: bio-synergy modeling language for multi-component and multi-target analysis

Multi-compound drugs are considered as the most promising solution to overcome the limited efficacy and off-target effect of drugs. However, identifying promising multiple compounds by experimental tests requires overwhelming costs and a number of tests. Systems biology-based approaches are regarded as one of the most promising strategy. To predict responses of drugs in biological systems is one of aims of Systems biology. We made Bio-Synergy Modeling Language (BSML) for modeling biological systems, which are multi-scale systems. BSML contains context information that covers spatial scales, temporal scales, and condition information, such as disease. We have applied BSML to generate type 2 diabetes (T2D) model, which involves malfunctions of numerous organs such as pancreas, liver, and muscle. We have extracted 12,522 T2D-related rules from public databases automatically. We simulated responses of single drugs and combination drugs on the T2D model by Petri nets. The results of our simulation show candidate T2D drugs and how combination drugs could act on whole-body scales. We expect that our work would provide an insight for identifying promising combination drugs and mechanisms of combination drugs on whole body scales.

Improved Successive-Cancellation Decoding of Polar Codes Based on Recursive Syndrome Decomposition

In this letter an improved method for the successive-cancellation decoding of polar codes is proposed. To avoid computations associated with redundant tree-traversals and syndrome calculations, recursive properties of polar codes are newly exploited in the proposed algorithm. Instead of computing a syndrome vector at every node, some syndrome vectors are directly obtained by recursively decomposing the syndrome vector computed previously. Furthermore, a modified syndrome check rule is proposed to prune unnecessary sub-trees efficiently. Compared with the latest pruning method, the proposed method reduces the latency by 23% for a (2048, 1024) polar code without sacrificing the error-correcting performance.

Area-Efficient Approach for Generating Quantized Gaussian Noise

This paper presents an efficient method to generate quantized Gaussian noise. The proposed method is derived based on the fact that any signal received at a digital system should be quantized to several bits. On the contrary to the previous works that have focused on the precision of noise, the quantization process is taken into account in generating noise samples. As a result, the resultant bit-width of noise is significantly reduced and the computation complexity of generating Gaussian noise is also reduced by simplifying the interpolation process. The proposed architecture based on the inversion method is implemented on field-programmable gate array (FPGA) devices. Compared to the previous architecture based on the conventional inversion method, the proposed approach improves the throughput per slice by 460% while maintaining the statistical properties of Gaussian noise.

The Gasoline Atomization Characteristics and Static Pressure Distribution of Tapered Nozzle Swirl Spray

The static pressure distribution, atomization characteristics and velocity distribution of tapered nozzle swirl spray is analyzed and then compared with original swirl spray. The static pressure distribution inside the swirl spray is measured using a piezoresistive pressure transducer. Phase Doppler anemometry (PDA) is applied to measure and analyze the droplet size and velocity distribution of tapered nozzle and original swirl spray. The static pressure inside the spray shows the lower value compared to the atmospheric pressure and this pressure drop is getting attenuated as the taper angle is increased. The droplet size of tapered nozzle spray shows similar value compared to the original swirl spray at the horizontal mainstream while it shows increased value at vertical mainstream. The deteriorated atomization characteristics of tapered nozzle spray is improved by applying high fuel temperature injection without causing the spray collapse. The velocity results show that the larger portion of fuel is positioned with higher injection velocity, and the smaller portion of fuel is positioned with lower injection velocity with causing spatially non-uniform mixture distribution.