• Bulletin of the Korean Chemical Society
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• v.33 no.4
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• pp.1123-1127
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• 2012

P-gp (P-glycoprotein) is a member of the ATP binding cassette (ABC) family of transporters. It transports many kinds of anticancer drugs out of the cell. It plays a major role as a cause of multidrug resistance (MDR). MDR function may be a cause of the failure of chemotherapy in cancer and influence pharmacokinetic properties of many drugs. Hence classification of candidate drugs as substrates or nonsubstrate of the P-gp is important in drug development. Therefore to identify whether a compound is a P-gp substrate or not, in silico method is promising. Recursive Partitioning (RP) method was explored for prediction of P-gp substrate. A set of 261 compounds, including 146 substrates and 115 nonsubstrates of P-gp, was used to training and validation. Using molecular descriptors that we can interpret their own meaning, we have established two models for prediction of P-gp substrates. In the first model, we chose only 6 descriptors which have simple physical meaning. In the training set, the overall predictability of our model is 78.95%. In case of test set, overall predictability is 69.23%. Second model with 2D and 3D descriptors shows a little better predictability (overall predictability of training set is 79.29%, test set is 79.37%), the second model with 2D and 3D descriptors shows better discriminating power than first model with only 2D descriptors. This approach will be used to reduce the number of compounds required to be run in the P-gp efflux assay.

• Journal of Information Processing Systems
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• v.16 no.2
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• pp.343-359
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• 2020

In this paper, a new algorithm is proposed for three-dimensional (3D) shape recognition using local features of model views and its sparse representation. The algorithm starts with the normalization of 3D models and the extraction of 2D views from uniformly distributed viewpoints. Consequently, the 2D views are stacked over each other to from view cubes. The algorithm employs the descriptors of 3D local features in the view cubes after applying Gabor filters in various directions as the initial features for 3D shape recognition. In the training stage, we store some 3D local features to build the prototype dictionary of local features. To extract an intermediate feature vector, we measure the similarity between the local descriptors of a shape model and the local features of the prototype dictionary. We represent the intermediate feature vectors of 3D models in the sparse domain to obtain the final descriptors of the models. Finally, support vector machine classifiers are used to recognize the 3D models. Experimental results using the Princeton Shape Benchmark database showed the average recognition rate of 89.7% using 20 views. We compared the proposed approach with state-of-the-art approaches and the results showed the effectiveness of the proposed algorithm.

• Research in Mathematical Education
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• v.15 no.1
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• pp.81-91
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• 2011

The aim of this study was to explore the grade 1-3 students' geometrical thinking level descriptors based on van Hiele level descriptors. The data were collected through collection of geometric curriculum materials such as indicators and learning standards in Basic Education Core Curriculum and mathematics textbook for grades 1-3. The findings were found that 1) Inconsistency between descriptors appeared on mathematics curriculum and Thai mathematics textbooks. 2) Using topics on textbooks as criterion for exploring 5 of 7 descriptors appeared on Thai mathematics textbook indicated geometrical thinking levels based on van Hiele's model merely level 0 (Visualization) across textbooks for grades 1-3.

• Journal of Information Processing Systems
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• v.12 no.3
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• pp.468-488
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• 2016

The paper proposes a novel framework for 3D face verification using dimensionality reduction based on highly distinctive local features in the presence of illumination and expression variations. The histograms of efficient local descriptors are used to represent distinctively the facial images. For this purpose, different local descriptors are evaluated, Local Binary Patterns (LBP), Three-Patch Local Binary Patterns (TPLBP), Four-Patch Local Binary Patterns (FPLBP), Binarized Statistical Image Features (BSIF) and Local Phase Quantization (LPQ). Furthermore, experiments on the combinations of the four local descriptors at feature level using simply histograms concatenation are provided. The performance of the proposed approach is evaluated with different dimensionality reduction algorithms: Principal Component Analysis (PCA), Orthogonal Locality Preserving Projection (OLPP) and the combined PCA+EFM (Enhanced Fisher linear discriminate Model). Finally, multi-class Support Vector Machine (SVM) is used as a classifier to carry out the verification between imposters and customers. The proposed method has been tested on CASIA-3D face database and the experimental results show that our method achieves a high verification performance.

• Advances in nano research
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• v.13 no.2
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• pp.121-134
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• 2022

Emerge of nanotechnology impacts all aspects of humans' life. One of important aspects of the nanotechnology and nanoparticles (NPs) is in the food production industry. The safety of such foods is not well recognized and producing safe foods using nanoparticles involves delicate experiments. In this study, we aim to incorporate intelligent computer simulation in predicting safety degree of nanofoods. In this regard, the safety concerns on the nano-foods are addressed considering cytotoxicity levels in metal oxides nanoparticles using adaptive neuro-fuzzy inference system (ANFIS) and response surface method (RSM). Three descriptors including chemical bond length, lattice energy and enthalpy of formation gaseous cation of 15 selected NPs are examined to find their influence on the cytotoxicity of NPs. The most effective descriptor is selected using RSM method and dependency of the toxicity of these NPs on the descriptors are presented in 2D and 3D graphs obtained using ANFIS technique. A comprehensive parameters study is conducted to observe effects of different descriptors on cytotoxicity of NPs. The results indicated that combinations of descriptors have the most effects on the cytotoxicity.

• Journal of Information Processing Systems
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• v.14 no.1
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• pp.176-190
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• 2018

This paper presents a novel convolutional neural network based multi-feature fusion learning method for non-rigid 3D model retrieval, which can investigate the useful discriminative information of the heat kernel signature (HKS) descriptor and the wave kernel signature (WKS) descriptor. At first, we compute the 2D shape distributions of the two kinds of descriptors to represent the 3D model and use them as the input to the networks. Then we construct two convolutional neural networks for the HKS distribution and the WKS distribution separately, and use the multi-feature fusion layer to connect them. The fusion layer not only can exploit more discriminative characteristics of the two descriptors, but also can complement the correlated information between the two kinds of descriptors. Furthermore, to further improve the performance of the description ability, the cross-connected layer is built to combine the low-level features with high-level features. Extensive experiments have validated the effectiveness of the designed multi-feature fusion learning method.

• Journal of the Korean Society for Library and Information Science
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• v.35 no.4
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• pp.97-122
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• 2001

This empirical study investigated the effect of choice of record field(s) upon which to search on retrieval performance for a large operational bibliographic database. The query terms used in the study were identified algorithmically from each target set in four different ways: (1) controlled terms derived from index term frequency weights, (2) uncontrolled terms derived from index term frequency weights. (3) controlled terms derived from inverse document frequency weights, and (4) uncontrolled terms based on universe document frequency weights. Su potable choices of record field were recognised. Using INSPEC terminology, these were the fields: (1) Abstract. (2) 'Anywhere'(i.e., ail fields). (3) Descriptors. (4) Identifiers, (5) 'Subject'(i.e., 'Descriptors' plus Identifiers'). and (6) Title. The study was undertaken in an operational web-based IR environment using the INSPEC bibliographic database. The retrieval performances were evaluated using D measure (bivariate in Recall and Precision). The main findings were that: (1) there exist significant differences in search performance arising from choice of field, using 'mean performance measure' as the criterion statistic; (2) the rankings of field-choices for each of these performance measures is sensitive to the choice of query : and (3) the optimal choice of field for the D-measure is Title.

• Bulletin of the Korean Chemical Society
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• v.30 no.11
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• pp.2717-2722
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• 2009

The use of the classification and regression tree (CART) methodology was studied in a quantitative structure-activity relationship (QSAR) context on a data set consisting of the binding affinities of 39 imidazobenzodiazepines for the α1 benzodiazepine receptor. The 3-D structures of these compounds were optimized using HyperChem software with semiempirical AM1 optimization method. After optimization a set of 1481 zero-to three-dimentional descriptors was calculated for each molecule in the data set. The response (dependent variable) in the tree model consisted of the binding affinities of drugs. Three descriptors (two topological and one 3D-Morse descriptors) were applied in the final tree structure to describe the binding affinities. The mean relative error percent for the data set is 3.20%, compared with a previous model with mean relative error percent of 6.63%. To evaluate the predictive power of CART cross validation method was also performed.

• Genomics & Informatics
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• v.7 no.4
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• pp.212-216
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• 2009

WebChemDB is an integrated chemical database retrieval system that provides access to over 8 million publicly available chemical structures, including related information on their biological activities and direct links to other public chemical resources, such as PubChem, ChEBI, and DrugBank. The data are publicly available over the web, using two-dimensional (2D) and three-dimensional (3D) structure retrieval systems with various filters and molecular descriptors. The web services API also provides researchers with functionalities to programmatically manipulate, search, and analyze the data.

• 한국HCI학회:학술대회논문집
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• 2009.02a
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• pp.669-672
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• 2009

The techniques extract shape descriptors from 3D models and use these descriptors for indices for comparing shape similarities. Most similarity search techniques focus on comparisons of each individual 3D model from databases. However, our similarity search technique can compare not only each individual 3D model, but also partial shape similarities. The partial shape matching technique extends the user's query request by finding similar parts of 3D models and finding 3D models which contain similar parts. We have implemented an experimental partial shape-matching search system for 3D pagoda models, and preliminary experiments show that the system successfully retrieves similar 3D model parts efficiently.