Rigorous 3-dimensional spectral data activity relationship approach modeling strategy for ToxCast estrogen receptor data classification, validation, and feature extraction

2016 ◽  
Vol 36 (3) ◽  
pp. 823-830 ◽  
Author(s):  
Svetoslav H. Slavov ◽  
Richard D. Beger
Keyword(s):  
Estrogen Receptor ◽  
Feature Extraction ◽  
Spectral Data ◽  
Data Classification ◽  
Activity Relationship ◽  
3 Dimensional ◽  
Modeling Strategy

Ensemble Classification System for Scientific Chart Recognition from PDF Files

2012 ◽  
Vol 2 (4) ◽  
pp. 1-10 ◽  
Author(s):  
S. Nagarajan ◽  
V. Karthikeyani
Keyword(s):  
Feature Extraction ◽  
Classification System ◽  
Texture Features ◽  
Portable Document Format ◽  
Ensemble Classification ◽  
3 Dimensional ◽  
Feature Based ◽  
Separation Feature ◽  
Object Features ◽  
Document Format

Portable Document Format (PDF) is the most frequently used universal document format on the Internet and E-Publishing. Wide usage of PDF files has increased the need of conversion tools that convert PDF file content to text or HTML formats. A PDF converter can be categorized into two domains, namely, text recognition and graphics recognition. This paper focus on graphic recognition, especially chart type identification, which is concerned with developing algorithms that has the ability to determine the type of a given chart image from a PDF file. In the proposed system, initially an enhanced connected component and statistical feature based method is used to separate the chart region from other regions. The chart region is then analyzed and grouped as either 2-dimensional or 3-dimensional chart. After separating the graphic component from the text components, feature extraction is performed. The features can be grouped as object features, texture features and shape features. The combined feature vector is then classified using ensemble classification system. Experimental results show that the chart separation, feature extraction and ensemble classification models significantly improve the quality of chart identification.

scholarly journals Quantitative Structure-Activity Relationship of Various Endogenous Estrogen Metabolites for Human Estrogen Receptor α and β Subtypes: Insights into the Structural Determinants Favoring a Differential Subtype Binding

Endocrinology ◽  
2006 ◽  
Vol 147 (9) ◽  
pp. 4132-4150 ◽  
Author(s):  
Bao Ting Zhu ◽  
Gui-Zhen Han ◽  
Joong-Youn Shim ◽  
Yujing Wen ◽  
Xiang-Rong Jiang
Keyword(s):  
Estrogen Receptor ◽  
Binding Affinity ◽  
Quantitative Structure Activity Relationship ◽  
Activity Relationship ◽  
Quantitative Structure ◽  
Structural Determinants ◽  
Structure Activity ◽  
Preferential Binding ◽  
Human Estrogen Receptor ◽  
Endogenous Estrogen

To search for endogenous estrogens that may have preferential binding affinity for human estrogen receptor (ER) α or β subtype and also to gain insights into the structural determinants favoring differential subtype binding, we studied the binding affinities of 74 natural or synthetic estrogens, including more than 50 steroidal analogs of estradiol-17β (E2) and estrone (E1) for human ERα and ERβ. Many of the endogenous estrogen metabolites retained varying degrees of similar binding affinity for ERα and ERβ, but some of them retained differential binding affinity for the two subtypes. For instance, several of the D-ring metabolites, such as 16α-hydroxyestradiol (estriol), 16β-hydroxyestradiol-17α, and 16-ketoestrone, had distinct preferential binding affinity for human ERβ over ERα (difference up to 18-fold). Notably, although E2 has nearly the highest and equal binding affinity for ERα and ERβ, E1 and 2-hydroxyestrone (two quantitatively predominant endogenous estrogens in nonpregnant woman) have preferential binding affinity for ERα over ERβ, whereas 16α-hydroxyestradiol (estriol) and other D-ring metabolites (quantitatively predominant endogenous estrogens formed during pregnancy) have preferential binding affinity for ERβ over ERα. Hence, facile metabolic conversion of parent hormone E2 to various metabolites under different physiological conditions may serve unique functions by providing differential activation of the ERα or ERβ signaling system. Lastly, our computational three-dimensional quantitative structure-activity relationship/comparative molecular field analysis of 47 steroidal estrogen analogs for human ERα and ERβ yielded useful information on the structural features that determine the preferential activation of the ERα and ERβ subtypes, which may aid in the rational design of selective ligands for each human ER subtype.

Spectral Data Modeling Based on Feature Extraction and Extreme Support Vector Regression

2011 ◽  
Vol 128-129 ◽  
pp. 297-300
Author(s):  
Shao Wei Liu ◽  
Dong Yan ◽  
Zhi Hua Liu ◽  
Jian Tang
Keyword(s):  
Feature Extraction ◽  
Support Vector Regression ◽  
Spectral Data ◽  
Latent Variables ◽  
Near Infrared ◽  
Support Vector ◽  
Feature Extraction Method ◽  
Near Infrared Spectra ◽  
Learning Machine ◽  
Elm Kernel

Spectral data such as near-infrared spectrum and frequency spectrum can simply the modeling of the difficulty-to-measured parameters. A novel modeling approach combined the feature extraction with extreme support vector regression (ESVR) is proposed. The latent variables space based feature extraction method can successfully complete the dimension reduction and independent variable extraction. The novel proposed ESVR leaning algorithm is realized by using extreme learning machine (ELM) kernel as SVR kernel, which is used to construct final models with better generalization. The experimental results based on the orange juice near-infrared spectra demonstrate that the proposed approach has better generalization performance and prediction accuracy.

New Method for Spectral Data Classification: Two-Way Moving Window Principal Component Analysis

2006 ◽  
Vol 60 (8) ◽  
pp. 884-891 ◽  
Author(s):  
Hideyuki Shinzawa ◽  
Shigeaki Morita ◽  
Yukihiro Ozaki ◽  
Roumiana Tsenkova
Keyword(s):  
Principal Component Analysis ◽  
Spectral Data ◽  
Principal Component ◽  
Data Classification ◽  
Component Analysis ◽  
New Method ◽  
Moving Window
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