Principal component analysis on molecular descriptors as an alternative point of view in the search of new Hsp90 inhibitors

2009 ◽  
Vol 33 (5) ◽  
pp. 386-390 ◽  
Author(s):  
Antonino Lauria ◽  
Mario Ippolito ◽  
Anna Maria Almerico
Keyword(s):  
Principal Component Analysis ◽  
Molecular Descriptors ◽  
Principal Component ◽  
Component Analysis ◽  
Point Of View ◽  
Hsp90 Inhibitors

scholarly journals REAL-TIME CLASSIFICATION OF FACIAL EXPRESSIONS USING A PRINCIPAL COMPONENT ANALYSIS AND CONVOLUTIONAL NEURAL NETWORK

SINERGI ◽  
2019 ◽  
Vol 23 (3) ◽  
pp. 239
Author(s):  
Dwi Lydia Zuharah Astuti ◽  
Samsuryadi Samsuryadi ◽  
Dian Palupi Rini
Keyword(s):  
Neural Network ◽  
Principal Component Analysis ◽  
Convolutional Neural Network ◽  
Customer Service ◽  
Facial Expressions ◽  
Principal Component ◽  
Component Analysis ◽  
Point Of View ◽  
In The Wild

Classification of facial expressions has become an essential part of computer systems and human-computer fast interaction. It is employed in various applications such as digital entertainment, customer service, driver monitoring, and emotional robots. Moreover, it has been studied through several aspects related to the face itself when facial expressions change based on the point of view or perspective. Facial curves such as eyebrows, nose, lips, and mouth will automatically change. Most of the proposed methods have limited frontal Face Expressions Recognition (FER), and their performance decrease when handling non-frontal and multi-view FER cases.  This study combined both methods in the classification of facial expressions, namely the Principal Component Analysis (PCA) and Convolutional Neural Network (CNN) methods. The results of this study proved to be more accurate than that of previous studies. The combination of PCA and CNN methods in the Static Facial Expressions in The Wild (SFEW) 2.0 dataset obtained an accuracy amounting to 70.4%; the CNN method alone only obtained an accuracy amounting to 60.9%.

scholarly journals Visualization of Chemical Space using Kernel Based Principal Component Research

2019 ◽  
Vol 8 (11S) ◽  
pp. 590-593
Keyword(s):  
Principal Component Analysis ◽  
Molecular Descriptors ◽  
Chemical Space ◽  
Principal Component ◽  
Component Analysis ◽  
Kernel Principal Component Analysis ◽  
Multivariate Statistical Methods ◽  
Multivariate Statistical ◽  
Non Linear ◽  
Hidden Patterns

Principal Component analysis (PCA) is one of the important and popular multivariate statistical methods applied over various data modeling applications. Traditional PCA handles linear variance in molecular descriptors or features. Handling complicated data by standard PCA will not be very helpful. This drawback can be handled by introducing kernel matrix over PCA. Kernel Principal Component Analysis (KPCA) is an extension of conventional PCA which handles non-linear hidden patterns exists in variables. It results in computational efficiency for data analysis and data visualization. In this paper, KPCA has been applied over dug-likeness dataset for visualization of non-linear relations exists in variables.

Identification of key local factors influencing revenue water ratio of Korean cities using principal component analysis and clustering analysis

2005 ◽  
Vol 5 (3-4) ◽  
pp. 197-208
Author(s):  
S. Chung ◽  
H. Lee ◽  
M. Yu ◽  
J. Koo ◽  
I. Hyun ◽  
...  
Keyword(s):  
Principal Component Analysis ◽  
Distribution System ◽  
Clustering Analysis ◽  
Principal Component ◽  
Component Analysis ◽  
Point Of View ◽  
Factor Scores ◽  
Effect Factors ◽  
Local Factors ◽  
System A

In order to identify the relation between revenue water (RW) ratio and key local factors in a quantifiable way, 90 effect factors were considered as regional characteristics for 79 Korean cities. Seven statistically significant effect factors were chosen through correlation analysis. Three principal components independently influencing RW ratio were extracted by principal component analysis (PCA). The 79 cities were grouped into six clusters by k-means clustering (KMC) of the factor scores of the cities. Then key local factors were identified and their impacts were quantified by multiple regression analysis (MRA) and they were justified by T-test and F-test. The approach through correlation-PCA-KMC-MRA was proved to be one of scientific ways for identification of key local factors. According to the result, it was suggested that a shorter length of distribution system, a water supply with smaller number of bigger customer meters a and gravitational supply through reservoir would be advantageous from a RW ratio's point of view.

A German version of the Intermittent Claudication Questionnaire (ICQ): cultural adaptation and validation

VASA ◽  
2012 ◽  
Vol 41 (5) ◽  
pp. 333-342 ◽  
Author(s):  
Kirchberger ◽  
Finger ◽  
Müller-Bühl
Keyword(s):  
Principal Component Analysis ◽  
Intermittent Claudication ◽  
Completion Time ◽  
Short Form ◽  
Principal Component ◽  
Component Analysis ◽  
German Version ◽  
Average Completion Time ◽  
Sf 36 ◽  
Related Quality

Background: The Intermittent Claudication Questionnaire (ICQ) is a short questionnaire for the assessment of health-related quality of life (HRQOL) in patients with intermittent claudication (IC). The objective of this study was to translate the ICQ into German and to investigate the psychometric properties of the German ICQ version in patients with IC. Patients and methods: The original English version was translated using a forward-backward method. The resulting German version was reviewed by the author of the original version and an experienced clinician. Finally, it was tested for clarity with 5 German patients with IC. A sample of 81 patients were administered the German ICQ. The sample consisted of 58.0 % male patients with a median age of 71 years and a median IC duration of 36 months. Test of feasibility included completeness of questionnaires, completion time, and ratings of clarity, length and relevance. Reliability was assessed through a retest in 13 patients at 14 days, and analysis of Cronbach’s alpha for internal consistency. Construct validity was investigated using principal component analysis. Concurrent validity was assessed by correlating the ICQ scores with the Short Form 36 Health Survey (SF-36) as well as clinical measures. Results: The ICQ was completely filled in by 73 subjects (90.1 %) with an average completion time of 6.3 minutes. Cronbach’s alpha coefficient reached 0.75. Intra-class correlation for test-retest reliability was r = 0.88. Principal component analysis resulted in a 3 factor solution. The first factor explained 51.5 of the total variation and all items had loadings of at least 0.65 on it. The ICQ was significantly associated with the SF-36 and treadmill-walking distances whereas no association was found for resting ABPI. Conclusions: The German version of the ICQ demonstrated good feasibility, satisfactory reliability and good validity. Responsiveness should be investigated in further validation studies.

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