• Korean Journal of Cognitive Science
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• v.28 no.4
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• pp.299-314
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• 2017

Predicting compound-protein interactions in-silico is significant for the drug discovery. In this paper, we propose an scalable machine learning model to predict compound-protein interaction. The key idea of this scalable machine learning model is the architecture of pairwise neural network model and feature embedding method from the raw data, especially for protein. This method automatically extracts the features without additional knowledge of compound and protein. Also, the pairwise architecture elevate the expressiveness and compact dimension of feature by preventing biased learning from occurring due to the dimension and type of features. Through the 5-fold cross validation results on large scale database show that pairwise neural network improves the performance of predicting compound-protein interaction compared to previous prediction models.

• Journal of the Ergonomics Society of Korea
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• v.31 no.4
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• pp.601-607
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• 2012

Objective: The aim of this study is to classify between intentional and natural blinks in vision based eye tracking system. Through implementing the classification method, we expect that the great eye tracking method will be designed which will perform well both navigation and selection interactions. Background: Currently, eye tracking is widely used in order to increase immersion and interest of user by supporting natural user interface. Even though conventional eye tracking system is well focused on navigation interaction by tracking pupil movement, there is no breakthrough selection interaction method. Method: To determine classification threshold between intentional and natural blinks, we performed experiment by capturing eye images including intentional and natural blinks from 12 subjects. By analyzing successive eye images, two features such as eye closed duration and pupil size variation after eye open were collected. Then, the classification threshold was determined by performing SVM(Support Vector Machine) training. Results: Experimental results showed that the average detection accuracy of intentional blinks was 97.4% in wearable eye tracking system environments. Also, the detecting accuracy in non-wearable camera environment was 92.9% on the basis of the above used SVM classifier. Conclusion: By combining two features using SVM, we could implement the accurate selection interaction method in vision based eye tracking system. Application: The results of this research might help to improve efficiency and usability of vision based eye tracking method by supporting reliable selection interaction scheme.

• International Journal of Computer Science & Network Security
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• v.21 no.6
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• pp.319-328
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• 2021

Parallel administration of numerous drugs increases Drug-Drug Interaction (DDI) because one drug might affect the activity of other drugs. DDI causes negative or positive impacts on therapeutic output. So there is a need to discover DDI to enhance the safety of consuming drugs. Though there are several DDI system exist to predict an interaction but nowadays it becomes impossible to maintain with a large number of biomedical texts which is getting increased rapidly. Mostly the existing DDI system address classification issues, and especially rely on handcrafted features, and some features which are based on particular domain tools. The objective of this paper to predict DDI in a way to avoid adverse effects caused by the consumed drugs, to predict similarities among the drug, Drug pair similarity calculation is performed. The best optimal weight is obtained with the support of KHA. LSTM function with weight obtained from KHA and makes bets prediction of DDI. Our methodology depends on (LSTM-KHA) for the detection of DDI. Similarities among the drugs are measured with the help of drug pair similarity calculation. KHA is used to find the best optimal weight which is used by LSTM to predict DDI. The experimental result was conducted on three kinds of dataset DS1 (CYP), DS2 (NCYP), and DS3 taken from the DrugBank database. To evaluate the performance of proposed work in terms of performance metrics like accuracy, recall, precision, F-measures, AUPR, AUC, and AUROC. Experimental results express that the proposed method outperforms other existing methods for predicting DDI. LSTMKHA produces reasonable performance metrics when compared to the existing DDI prediction model.

• Journal of Advanced Marine Engineering and Technology
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• v.40 no.5
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• pp.437-446
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• 2016

Feature selection has become an essential technique to reduce the dimensionality of data sets. Many features are frequently irrelevant or redundant for the classification tasks. The purpose of feature selection is to select relevant features and remove irrelevant and redundant features. Applications of the feature selection range from text processing, face recognition, bioinformatics, speaker verification, and medical diagnosis to financial domains. In this study, we focus on filter methods based on information entropy : IG (Information Gain), FCBF (Fast Correlation Based Filter), and mRMR (minimum Redundancy Maximum Relevance). FCBF has the advantage of reducing computational burden by eliminating the redundant features that satisfy the condition of approximate Markov blanket. However, FCBF considers only the relevance between the feature and the class in order to select the best features, thus failing to take into consideration the interaction between features. In this paper, we propose an improved FCBF to overcome this shortcoming. We also perform a comparative study to evaluate the performance of the proposed method.

• The KIPS Transactions:PartB
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• v.13B no.7 s.110
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• pp.679-688
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• 2006

Protein-protein interaction data obtained from high-throughput experiments includes high false positives. In this paper, we introduce a new protein-protein interaction reliability verification system. The proposed system integrates various biological features related with protein-protein interactions, and then selects the most relevant and informative features among them using a feature selection method. To assess the reliability of each protein-protein interaction data, the system construct a classifier that can distinguish true interacting protein pairs from noisy protein-protein interaction data based on the selected biological evidences using a classification technique. Since the performance of feature selection methods and classification techniques depends heavily upon characteristics of data, we performed rigorous comparative analysis of various feature selection methods and classification techniques to obtain optimal performance of our system. Experimental results show that the combination of feature selection method and classification algorithms provide very powerful tools in distinguishing true interacting protein pairs from noisy protein-protein interaction dataset. Also, we investigated the effects on performances of feature selection methods and classification techniques in the proposed protein interaction verification system.

• Journal of the Korean association of regional geographers
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• v.20 no.1
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• pp.30-48
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• 2014

Relying on the interaction indices - dominance index, relative strength index and entropy index, this paper analyzes the structural features of urban network in the Seoul metropolitan area with the flows of commuting, business, and freight. Analytical results show that the Seoul metropolitan urban system is vertical, size-dependent, one-way, and the highest city-dominant network rather than horizontal, size-neutral, two-way, complementary one. The network of freight flow is a little bit more symmetrical than the networks of commuting and business. However, the interaction with Seoul is still determinant in all aspects of hierarchical structure, relative strength, and symmetry of flow.

• Structural Engineering and Mechanics
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• v.31 no.5
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• pp.545-566
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• 2009

Bridge columns are subjected to combined actions of axial force, shear force and bending moment during earthquakes, caused by spatially-complex earthquake motions, features of structural configurations and the interaction between input and response characteristics. Combined actions can have significant effects on the force and deformation capacity of RC columns, resulting in unexpected large deformations and extensive damage that in turn influences the performance of bridges as vital components of transportation systems. This paper evaluates the seismic response of three prototype reinforced concrete bridges using comprehensive numerical models that are capable of simulating the complex soil-structural interaction effects and nonlinear behavior of columns. An analytical approach that can capture the shear-flexural interacting behavior is developed to model the realistic nonlinear behavior of RC columns, including the pinching behavior, strength deterioration and stiffness softening due to combined actions of shear force, axial force and bending moment. Seismic response analyses were conducted on the prototype bridges under suites of ground motions. Response quantities of bridges (e.g., drift, acceleration, section force and section moment etc.) are compared and evaluated to identify the effects of vertical motion, structural characteristics and the shear-flexural interaction on seismic demand of bridges.

• Mycobiology
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• v.49 no.5
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• pp.439-453
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• 2021

During the rainy season in Thailand, specimens of Phallus chiangmaiensis sp. nov. and P. merulinus were collected from Chiang Mai and Samut Sakhon Provinces, respectively. Molecular phylogenetic analyses based on sequences of the nuclear ribosomal large subunit (LSU), nuclear ribosomal 5.8S gene including the internal transcribed spacer regions 1 and 2 (ITS), and the protein-coding gene atp6 (mitochondrial adenosine triphosphate [ATP] synthase subunit 6) support the placement of the new species within Phallus. Phallus chiangmaiensis has a well-developed white indusium and campanulated caps with reticulate surfaces. It differs morphologically from the related species, as supported by the phylogenetic data. Phallus merulinus is reported here as a species that was re-encountered in Thailand. The descriptions of the species are accompanied by illustrations of macro- and micro- morphological features, and a discussion of the related taxa is presented.

• The Bulletin of The Korean Astronomical Society
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• v.44 no.1
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• pp.54.1-54.1
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• 2019

Solar observations often show that interaction of more than one flux rope is involved in solar eruptions. In this regard, Lau and Finn (1996) intensively studied the interaction of two flux ropes, which reside in between two parallel planes each mimicking one polarity region of the solar photosphere. However, this geometry is quite far from the real solar situation, in which all feet of flux tubes are rooted in one surface only. In this paper, we study the interaction of two flux ropes in a semi-infinite region above a plane representing the solar photosphere. Four cases of the flux rope interaction are investigated in our MHD simulation study: (1) parallel axial fields and parallel axial currents (co-helicity), (2) antiparallel axial fields and parallel axial currents (counter-helicity), (3) parallel axial fields and antiparallel axial currents (counter-helicity), and (4) antiparallel axial fields and antiparallel axial currents (co-helicity). Each case consists of four or six subcases according to the background field direction relative to the flux ropes and the relative positions of the flux rope footpoints. In our simulations, all the cases eventually show eruptive behaviors, but their degree of explosiveness and field topological evolutions are quite different. We construct artificial emission measure maps based on the simulations and compare them with images of CME observations, which provides us with information on what field configurations may generate certain eruption features.

• Journal of KIISE:Software and Applications
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• v.33 no.4
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• pp.365-377
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• 2006

Proteins are known to perform a biological function by interacting with other proteins or compounds. Since protein interaction is intrinsic to most cellular processes, prediction of protein interaction is an important issue in post-genomic biology where abundant interaction data have been produced by many research groups. In this paper, we present an associative feature mining method to predict implicit protein-protein interactions of Saccharomyces cerevisiae from public protein interaction data. We discretized continuous-valued features by maximal interdependence-based discretization approach. We also employed feature dimension reduction filter (FDRF) method which is based on the information theory to select optimal informative features, to boost prediction accuracy and overall mining speed, and to overcome the dimensionality problem of conventional data mining approaches. We used association rule discovery algorithm for associative feature and rule mining to predict protein interaction. Using the discovered associative feature we predicted implicit protein interactions which have not been observed in training data. According to the experimental results, the proposed method accomplished about 96.5% prediction accuracy with reduced computation time which is about 29.4% faster than conventional method with no feature filter in association rule mining.