Insight into binding modes of p53 and inhibitors to MDM2 based on molecular dynamic simulations and principal component analysis

2015 ◽  
Vol 114 (1) ◽  
pp. 128-140 ◽  
Author(s):  
Weiyuan Cheng ◽  
Zhiqiang Liang ◽  
Wei Wang ◽  
Changhong Yi ◽  
Hongyun Li ◽  
...  
Keyword(s):  
Principal Component Analysis ◽  
Molecular Dynamic ◽  
Principal Component ◽  
Component Analysis ◽  
Molecular Dynamic Simulations ◽  
Binding Modes ◽  
Dynamic Simulations ◽  
Insight Into

scholarly journals The use of principal component analysis for the construction of the Water Poverty Index

RBRH ◽  
2019 ◽  
Vol 24 ◽  
Author(s):  
Larynne Dantas de Senna ◽  
Adelena Gonçalves Maia ◽  
Joana Darc Freire de Medeiros
Keyword(s):  
Principal Component Analysis ◽  
Poverty Reduction ◽  
Principal Component ◽  
Component Analysis ◽  
Poverty Index ◽  
Multiple Dimensions ◽  
Water Poverty Index ◽  
Clear Identification ◽  
Natural Characteristics ◽  
Insight Into

ABSTRACT In relation to water resources, indexes can be created to express the multiple dimensions involved with it to aid the planning and management of basins. In this regard, the Water Poverty Index is globally used, but one of its criticisms includes the subjectivity associated with how the sub-indexes are weighted. Therefore, in this study, we applied principal component analysis (PCA) to determine the sub-indexes’ weight: resource, access, capacity, use, and environment of the Seridó river basin. This new index with PCA presents an average range with broader values compared to methodologies without, allowing clear identification of the disparities among the cities and the possibility to better prioritize investments concerning water poverty reduction. Our results show that this approach makes it possible to qualitatively identify geographical locations that have greater water poverty compared to others. Additionally, with this approach, it can be determined whether water poverty is caused due to natural characteristics or deficits in water infrastructure investment, providing insight into social fragilities as well. Overall, the presented hierarchical tool in this study has a high value to improve the planning of water resource uses.

scholarly journals Principal Component Analysis based on data characteristics for dimensionality reduction of ECG recordings in arrhythmia classification

Open Physics ◽  
2019 ◽  
Vol 17 (1) ◽  
pp. 489-496 ◽  
Author(s):  
Agnieszka Wosiak
Keyword(s):  
Principal Component Analysis ◽  
Heart Diseases ◽  
Principal Component ◽  
Feature Space ◽  
Data Interpretation ◽  
Component Analysis ◽  
Feature Reduction ◽  
Analysis Method ◽  
Intrinsic Structure ◽  
Insight Into

Abstract Due to the growing problem of heart diseases, the computer improvement of their diagnostics becomes of great importance. One of the most common heart diseases is cardiac arrhythmia. It is usually diagnosed by measuring the heart activity using electrocardiograph (ECG) and collecting the data as multidimensional medical datasets. However, their storage, analysis and knowledge extraction become highly complex issues. Feature reduction not only enables saving storage and computing resources, but it primarily makes the process of data interpretation more comprehensive. In the paper the new igPCA (in-group Principal Component Analysis) method for feature reduction is proposed. We assume that the set of attributes can be split into subgroups of similar characteristic and then subjected to principal component analysis. The presented method transforms the feature space into a lower dimension and gives the insight into intrinsic structure of data. The method has been verified by experiments done on a dataset of ECG recordings. The obtained effects have been evaluated regarding the number of kept features and classification accuracy of arrhythmia types. Experiment results showed the advantage of the presented method compared to base PCA approach.

scholarly journals Symbolic covariance matrix for interval-valued variables and its application to principal component analysis

2014 ◽  
Vol 11 (1) ◽  
Author(s):  
Katarina Košmelj ◽  
Jennifer Le-Rademacher ◽  
Lynne Billard
Keyword(s):  
Principal Component Analysis ◽  
Covariance Matrix ◽  
Information Gain ◽  
Principal Component ◽  
Component Analysis ◽  
Classical Approach ◽  
Interval Valued ◽  
Insight Into ◽  
Variance Explained

In the last two decades, principal component analysis (PCA) was extended to interval-valued data; several adaptations of the classical approach are known from the literature. Our approach is based on the symbolic covariance matrix Cov for the interval-valued variables proposed by Billard (2008). Its crucial advantage, when compared to other approaches, is that it fully utilizes all the information in the data. The symbolic covariance matrix can be decomposed into a within part CovW and a between part CovB. We propose a further insight into the PCA results: the proportion of variance explained due to the within information and the proportion of variance explained due to the between information can be calculated. Additionally, we suggest PCA on CovB and CovW to be done to obtain deeper insights into the data under study. In the case study presented, the information gain when performing PCA on the intervals instead of the interval midpoints (conditionally the means) is about 45%. It turns out that, for these data, the uniformity assumption over intervals does not hold and so analysis of the data represented by histogram-valued variables is suggested.

scholarly journals Focused Principal Component Analysis: a graphical method for exploring dietary patterns

2010 ◽  
Vol 26 (11) ◽  
pp. 2149-2156 ◽  
Author(s):  
Raquel Canuto ◽  
Suzi Camey ◽  
Denise P. Gigante ◽  
Ana M. B. Menezes ◽  
Maria Teresa Anselmo Olinto
Keyword(s):  
Principal Component Analysis ◽  
Dietary Patterns ◽  
Fast Food ◽  
Principal Component ◽  
Component Analysis ◽  
R Software ◽  
Animal Fats ◽  
Whole Wheat ◽  
Show Evidence ◽  
Insight Into

The aim of the present study was to introduce Focused Principal Component Analysis (FPCA) as a novel exploratory method for providing insight into dietary patterns that emerge based on a given characteristic of the sample. To demonstrate the use of FPCA, we used a database of 1,968 adults. Food intake was obtained using a food frequency questionnaire covering 26 food items. The focus variables used for analysis were age, income, and schooling. All analyses were carried out using R software. The graphs generated show evidence of socioeconomic inequities in dietary patterns. Intake of whole-wheat foods, fruit, and vegetables was positively correlated with income and schooling, whereas for refined cereals, animal fats (lard), and white bread this correlation was negative. Age was inversely associated with intake of fast-food and processed foods and directly associated with a pattern that included fruit, green salads, and other vegetables. In an easy and direct fashion, FPCA allowed us to visualize dietary patterns based on a given focus variable.

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