scholarly journals A quantified discrimination of a query compound between target classes under 3D-similarity metric

2020 ◽  
Author(s):  
Sanghyeok Lee ◽  
Sangjin Ahn ◽  
Mi-hyun Kim
Keyword(s):  
Expectation Maximization ◽  
Estrogen Receptor Alpha ◽  
Target Prediction ◽  
Gaussian Mixture ◽  
Conformational Sampling ◽  
Target Class ◽  
Unknown Compound ◽  
Ml Estimation ◽  
Tanimoto Similarity ◽  
3D Similarity

Abstract Background: 3D similarity is useful to predict the profiles of unprecedented molecular frameworks, 2D dissimilar to known compounds. Basically, when comparing compound pairs, 3D similarity of the pairs depends on conformational sampling of compounds, alignment method, chosen descriptors, and metric to show limited discriminative power. In addition to four factors, 3D chemocentric target prediction of an unknown compound requires compound - target associations. The associations for the target prediction replace compound-to-compound comparison with compound-to-target comparison. Results: Quantitative comparison of query compounds to target classes (one-to-group) could be acquired using two type similarity distributions: one is from maximum likelihood (ML) estimation of queries and another is from Gaussian mixture model (GMM) of target classes. While Jaccard-Tanimoto similarity of query-to-ligand pairs could be transformed into query distribution through ML estimation, the similarity of ligand pairs within each target class could be transformed into the representative distribution of a target class through GMM, hyperparameterized through expectation-maximization (EM) algorithm. To quantify the discriminativeness of a query ligand against target classes, Kullback-Leibler (K-L) divergence was calculated between two distributions.Conclusions: Stratified sampled 14K ligands from four target classes, estrogen receptor alpha (ESR), vitamin D receptor (VDR), cyclooxygenase-2 (COX2), and cathepsin D (CTSD) presented whether or not each query can be a representative ligand of each target through compared K-L divergence value. The feasibility index, Fm and the probability, from K-L divergence could summarize 3D chemocentric relationship between target classes.

scholarly journals Comparing a Query Compound with Drug Target Classes Using 3D-Chemical Similarity

2020 ◽  
Vol 21 (12) ◽  
pp. 4208
Author(s):  
Sang-Hyeok Lee ◽  
Sangjin Ahn ◽  
Mi-hyun Kim
Keyword(s):  
Target Prediction ◽  
Gaussian Mixture ◽  
Similarity Score ◽  
P Value ◽  
Conformational Sampling ◽  
Similarity Coefficients ◽  
Target Class ◽  
Unknown Compound ◽  
Ml Estimation ◽  
3D Similarity

3D similarity is useful in predicting the profiles of unprecedented molecular frameworks that are 2D dissimilar to known compounds. When comparing pairs of compounds, 3D similarity of the pairs depends on conformational sampling, the alignment method, the chosen descriptors, and the similarity coefficients. In addition to these four factors, 3D chemocentric target prediction of an unknown compound requires compound–target associations, which replace compound-to-compound comparisons with compound-to-target comparisons. In this study, quantitative comparison of query compounds to target classes (one-to-group) was achieved via two types of 3D similarity distributions for the respective target class with parameter optimization for the fitting models: (1) maximum likelihood (ML) estimation of queries, and (2) the Gaussian mixture model (GMM) of target classes. While Jaccard–Tanimoto similarity of query-to-ligand pairs with 3D structures (sampled multi-conformers) can be transformed into query distribution using ML estimation, the ligand pair similarity within each target class can be transformed into a representative distribution of a target class through GMM, which is hyperparameterized via the expectation–maximization (EM) algorithm. To quantify the discriminativeness of a query ligand against target classes, the Kullback–Leibler (K–L) divergence of each query was calculated and compared between targets. 3D similarity-based K–L divergence together with the probability and the feasibility index, (Fm), showed discriminative power with regard to some query–class associations. The K–L divergence of 3D similarity distributions can be an additional method for (1) the rank of the 3D similarity score or (2) the p-value of one 3D similarity distribution to predict the target of unprecedented drug scaffolds.

scholarly journals Robust Non-Rigid Feature Matching for Image Registration Using Geometry Preserving

Sensors ◽  
2019 ◽  
Vol 19 (12) ◽  
pp. 2729 ◽  
Author(s):  
Hao Zhu ◽  
Ke Zou ◽  
Yongfu Li ◽  
Ming Cen ◽  
Lyudmila Mihaylova
Keyword(s):  
Image Registration ◽  
Expectation Maximization ◽  
Feature Matching ◽  
State Of The Art ◽  
Gaussian Mixture ◽  
Connectivity Matrix ◽  
Local Geometry ◽  
Feature Points ◽  
Ml Estimation ◽  
Current State

In this paper, a robust non-rigid feature matching approach for image registration with geometry constraints is proposed. The non-rigid feature matching approach is formulated as a maximum likelihood (ML) estimation problem. The feature points of one image are represented by Gaussian mixture model (GMM) centroids, and are fitted to the feature points of the other image by moving coherently to encode the global structure. To preserve the local geometry of these feature points, two local structure descriptors of the connectivity matrix and Laplacian coordinate are constructed. The expectation maximization (EM) algorithm is applied to solve this ML problem. Experimental results demonstrate that the proposed approach has better performance than current state-of-the-art methods.

scholarly journals Towards Application of One-Class Classification Methods to Medical Data

2014 ◽  
Vol 2014 ◽  
pp. 1-7 ◽  
Author(s):  
Itziar Irigoien ◽  
Basilio Sierra ◽  
Concepción Arenas
Keyword(s):  
State Of The Art ◽  
Gaussian Mixture ◽  
Support Vector ◽  
Support Vector Data Description ◽  
Data Sets ◽  
Biomedical Data ◽  
Vector Data ◽  
Target Class ◽  
Tumor Recognition ◽  
One Class Classification

In the problem of one-class classification (OCC) one of the classes, the target class, has to be distinguished from all other possible objects, considered as nontargets. In many biomedical problems this situation arises, for example, in diagnosis, image based tumor recognition or analysis of electrocardiogram data. In this paper an approach to OCC based on a typicality test is experimentally compared with reference state-of-the-art OCC techniques—Gaussian, mixture of Gaussians, naive Parzen, Parzen, and support vector data description—using biomedical data sets. We evaluate the ability of the procedures using twelve experimental data sets with not necessarily continuous data. As there are few benchmark data sets for one-class classification, all data sets considered in the evaluation have multiple classes. Each class in turn is considered as the target class and the units in the other classes are considered as new units to be classified. The results of the comparison show the good performance of the typicality approach, which is available for high dimensional data; it is worth mentioning that it can be used for any kind of data (continuous, discrete, or nominal), whereas state-of-the-art approaches application is not straightforward when nominal variables are present.

Gaussian Mixture Model of Ground Filtering Based on Hierarchical Curvature Constraints for Airborne Lidar Point Clouds

2021 ◽  
Vol 87 (9) ◽  
pp. 615-630
Author(s):  
Longjie Ye ◽  
Ka Zhang ◽  
Wen Xiao ◽  
Yehua Sheng ◽  
Dong Su ◽  
...  
Keyword(s):  
Gaussian Mixture Model ◽  
Mixture Model ◽  
Expectation Maximization ◽  
Latent Variables ◽  
Expectation Maximization Algorithm ◽  
Point Clouds ◽  
Gaussian Mixture ◽  
Airborne Lidar ◽  
Height Difference ◽  
Hierarchical Constraints

This paper proposes a Gaussian mixture model of a ground filtering method based on hierarchical curvature constraints. Firstly, the thin plate spline function is iteratively applied to interpolate the reference surface. Secondly, gradually changing grid size and curvature threshold are used to construct hierarchical constraints. Finally, an adaptive height difference classifier based on the Gaussian mixture model is proposed. Using the latent variables obtained by the expectation-maximization algorithm, the posterior probability of each point is computed. As a result, ground and objects can be marked separately according to the calculated possibility. 15 data samples provided by the International Society for Photogrammetry and Remote Sensing are used to verify the proposed method, which is also compared with eight classical filtering algorithms. Experimental results demonstrate that the average total errors and average Cohen's kappa coefficient of the proposed method are 6.91% and 80.9%, respectively. In general, it has better performance in areas with terrain discontinuities and bridges.

Detection of Network Intrusion Signal in Deep Camouflage Based on Chaotic Synchronization

2013 ◽  
Vol 380-384 ◽  
pp. 2695-2698
Author(s):  
Cai Tian Zhang ◽  
Yi Bo Zhang
Keyword(s):  
Signal Detection ◽  
Expectation Maximization ◽  
Detection Method ◽  
Expectation Maximization Algorithm ◽  
Gaussian Mixture ◽  
Chaotic Synchronization ◽  
New Method ◽  
Network Data ◽  
Network Intrusion ◽  
Better Than

For detecting the network intrusion signal in deep camouflage precisely and effectively, a new detection method based chaotic synchronization is proposed in this paper. The Gaussian mixture model of the network data combined with expectation maximization algorithm is established firstly for the afterwards detection, the chaotic synchronization concept is proposed to detect the intrusion signals. According to the simulation result, the new method which this paper proposed shows good performance of detection the intrusion signals. The detection ROC is plotted for the chaotic synchronization detection method and traditional ARMA method, and it shows that the detection performance of the chaotic synchronization algorithm is much better than the traditional ARMA detection method. It shows good application prospect of the new method in the network intrusion signal detection.

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