scholarly journals DNetPRO: A network approach for low-dimensional signatures from high-throughput data

2019 ◽  
Author(s):  
Nico Curti ◽  
Enrico Giampieri ◽  
Giuseppe Levi ◽  
Gastone Castellani ◽  
Daniel Remondini
Keyword(s):  
Discriminant Analysis ◽  
High Throughput ◽  
Classification Models ◽  
Combinatorial Approach ◽  
Linear Classification ◽  
Network Approach ◽  
High Throughput Data ◽  
Sample Classification ◽  
Low Dimensional ◽  
Discriminant Power

The objective of many high-throughput “omics” studies is to obtain a relatively low-dimensional set of observables - signature - for sample classification purposes (diagnosis, prognosis, stratification). We propose DNetPRO, Discriminant Analysis with Network PROcessing, a supervised signature identification method based on a bottom-up combinatorial approach that exploits the discriminant power of all variable pairs. The algorithm is easily scalable allowing efficient computing even for high number of observables (104 − 105). We show applications on real high-throughput genomic datasets in which our method outperforms existing results, or compares to them but with a smaller number of selected variables. Moreover the linearity of DNetPRO allows a clearer interpretation of the obtained signatures in comparison to non linear classification models

Comparison of logistic regression and linear discriminant analysis

2004 ◽  
Vol 1 (1) ◽  
pp. 143-161
Author(s):  
Maja Pohar ◽  
Mateja Blas ◽  
Sandra Turk
Keyword(s):  
Logistic Regression ◽  
Discriminant Analysis ◽  
Linear Discriminant Analysis ◽  
Predictive Accuracy ◽  
Classification Models ◽  
Linear Classification ◽  
Robust Method ◽  
Linear Discriminant ◽  
Explanatory Variables ◽  
The Impact

Two of the most widely used statistical methods for analyzing categorical outcome variables are linear discriminant analysis and logistic regression. While both are appropriate for the development of linear classification models, linear discriminant analysis makes more assumptions about the underlying data. Hence, it is assumed that logistic regression is the more flexible and more robust method in case of violations of these assumptions. In this paper we consider the problem of choosing between the two methods, and set some guidelines for proper choice. The comparison between the methods is based on several measures of predictive accuracy. The performance of the methods is studied by simulations. We start with an example where all the assumptions of the linear discriminant analysis are satisfied and observe the impact of changes regarding the sample size, covariance matrix, Mahalanobis distance and direction of distance between group means. Next, we compare the robustness of the methods towards categorisation and non-normality of explanatory variables in a closely controlled way. We show that the results of LDA and LR are close whenever the normality assumptions are not too badly violated, and set some guidelines for recognizing these situations. We discuss the inappropriateness of LDA in all other cases.

scholarly journals Usefulness of Discriminant Analysis in the Morphometric Differentiation of Six Native Freshwater Species from Ecuador

Animals ◽  
2021 ◽  
Vol 11 (1) ◽  
pp. 111
Author(s):  
Ana Gonzalez-Martinez ◽  
Carmen De-Pablos-Heredero ◽  
Martin González ◽  
Jorge Rodriguez ◽  
Cecilio Barba ◽  
...  
Keyword(s):  
Discriminant Analysis ◽  
Rural Communities ◽  
Development Program ◽  
Freshwater Species ◽  
Hydrographic Basin ◽  
Wilks Lambda ◽  
Morphological Variables ◽  
Morphometric Differentiation ◽  
Discriminant Power

The aim of this research was to find out the morphometric differentiation of six native freshwater species in the Guayas Hydrographic Basin (Ecuador) by means of discriminant analysis. A total of 1355 mature fishes (Cichlasoma festae, Andinoacara rivulatus, Dormitator latifrons, Bryncon dentex, Hoplias microlepis and Leporinus ecuadorensis) were captured and 27 morphometric measurements and 20 landmarks were used. Two-way analysis of variance with species and sex as fixed factors and discriminant analysis were applied. The selection of the most discriminant variables was made applying the F of Snedecor, Wilks’-Lambda and the 1-Tolerance. While sex within species had no significant effect on the morphology, differences among species were significant. Twenty-seven morphological variables showed highly significant differences among six native freshwater species. Nine biometric variables with high discriminant power were selected. The six species analyzed were discriminated by the morphometric models generated, thus showing that discriminant analysis was useful for differentiating species. The morphometric differentiation by discriminant analysis is a direct, simple and economic methodology to be applied in situ in rural communities. It favors the implementation of a livestock development program and it could be used with other native freshwater species in the Guayas Hydrographic Basin.

High Throughput Data Mapping for Coarse-Grained Reconfigurable Architectures

2011 ◽  
Vol 30 (11) ◽  
pp. 1599-1609 ◽  
Author(s):  
Yongjoo Kim ◽  
Jongeun Lee ◽  
A. Shrivastava ◽  
J. W. Yoon ◽  
Doosan Cho ◽  
...  
Keyword(s):  
High Throughput ◽  
Coarse Grained ◽  
Reconfigurable Architectures ◽  
Data Mapping ◽  
High Throughput Data

Protein function prediction with high-throughput data

Amino Acids ◽  
2008 ◽  
Vol 35 (3) ◽  
pp. 517-530 ◽  
Author(s):  
Xing-Ming Zhao ◽  
Luonan Chen ◽  
Kazuyuki Aihara
Keyword(s):  
High Throughput ◽  
Protein Function ◽  
Protein Function Prediction ◽  
Function Prediction ◽  
High Throughput Data

Excavating the pathogenic gene of breast cancer based on high throughput data of tumor and somatic reprogramming

Cell Cycle ◽  
2021 ◽  
pp. 1-15
Author(s):  
Lian Duan ◽  
Zhendong Wang ◽  
Xin Zheng ◽  
Junjian Li ◽  
Huamin Yin ◽  
...  
Keyword(s):  
Breast Cancer ◽  
High Throughput ◽  
High Throughput Data ◽  
Pathogenic Gene
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