scholarly journals Exploring Dynamic Metabolomics Data With Multiway Data Analysis: a Simulation Study

2021 ◽  
Author(s):  
Lu Li ◽  
Huub Hoefsloot ◽  
Albert A. Graaf ◽  
Evrim Acar ◽  
Age K. Smilde
Keyword(s):  
Data Analysis ◽  
Individual Variation ◽  
Dynamic Models ◽  
Tensor Factorization ◽  
Experimental Conditions ◽  
Metabolomics Data ◽  
Time Resolved ◽  
Multiway Data Analysis ◽  
Induced Variation ◽  
Underlying Mechanisms

Abstract Background: Analysis of dynamic metabolomics data holds the promise to improve our understanding of underlying mechanisms in metabolism. For example, it may detect changes in metabolism due to the onset of a disease. Dynamic or time-resolved metabolomics data can be arranged as a three-way array with entries organized according to a subjects mode, a metabolites mode and a time mode. While such time-evolving multiway data sets are increasingly collected, revealing the underlying mechanisms and their dynamics from such data remains challenging. For such data, one of the complexities is the presence of a superposition of several sources of variation: induced variation (due to experimental conditions or inborn errors), individual variation, and measurement error. Multiway data analysis (also known as tensor factorizations) has been successfully used in data mining to find the underlying patterns in multiway data. In this paper, we study the use of multiway data analysis to reveal the underlying patterns and dynamics in time-resolved metabolomics data. Results: We focus on simulated data arising from different dynamic models of increasing complexity, i.e., a simple linear system, a yeast glycolysis model, and a human cholesterol model. We generate data with induced variation as well as individual variation. Systematic experiments are performed to demonstrate the advantages and limitations of multiway data analysis in analyzing such dynamic metabolomics data and their capacity to disentangle the different sources of variations. We choose to use simulations since we want to understand the capability of multiway data analysis methods which is facilitated by knowing the ground truth. Conclusion: Our numerical experiments demonstrate that despite the increasing complexity of the studied dynamic metabolic models, tensor factorization methods CANDECOMP/PARAFAC(CP) and Parallel Profiles with Linear Dependences (Paralind) can disentangle the sources of variations and thereby reveal the underlying mechanisms and their dynamics.

scholarly journals Exploring dynamic metabolomics data with multiway data analysis: a simulation study

2022 ◽  
Vol 23 (1) ◽  
Author(s):  
Lu Li ◽  
Huub Hoefsloot ◽  
Albert A. de Graaf ◽  
Evrim Acar ◽  
Age K. Smilde
Keyword(s):  
Data Analysis ◽  
Individual Variation ◽  
Simulated Data ◽  
Ground Truth ◽  
Metabolomics Data ◽  
Analysis Methods ◽  
Multiway Data Analysis ◽  
Induced Variation ◽  
Underlying Mechanisms ◽  
Data Analysis Methods

Abstract Background Analysis of dynamic metabolomics data holds the promise to improve our understanding of underlying mechanisms in metabolism. For example, it may detect changes in metabolism due to the onset of a disease. Dynamic or time-resolved metabolomics data can be arranged as a three-way array with entries organized according to a subjects mode, a metabolites mode and a time mode. While such time-evolving multiway data sets are increasingly collected, revealing the underlying mechanisms and their dynamics from such data remains challenging. For such data, one of the complexities is the presence of a superposition of several sources of variation: induced variation (due to experimental conditions or inborn errors), individual variation, and measurement error. Multiway data analysis (also known as tensor factorizations) has been successfully used in data mining to find the underlying patterns in multiway data. To explore the performance of multiway data analysis methods in terms of revealing the underlying mechanisms in dynamic metabolomics data, simulated data with known ground truth can be studied. Results We focus on simulated data arising from different dynamic models of increasing complexity, i.e., a simple linear system, a yeast glycolysis model, and a human cholesterol model. We generate data with induced variation as well as individual variation. Systematic experiments are performed to demonstrate the advantages and limitations of multiway data analysis in analyzing such dynamic metabolomics data and their capacity to disentangle the different sources of variations. We choose to use simulations since we want to understand the capability of multiway data analysis methods which is facilitated by knowing the ground truth. Conclusion Our numerical experiments demonstrate that despite the increasing complexity of the studied dynamic metabolic models, tensor factorization methods CANDECOMP/PARAFAC(CP) and Parallel Profiles with Linear Dependences (Paralind) can disentangle the sources of variations and thereby reveal the underlying mechanisms and their dynamics.

Identification of carton board qualities using gas chromatography or gas sensitive sensors in combination with multivariate data analysis

1997 ◽  
Vol 12 (4) ◽  
pp. 276-281 ◽  
Author(s):  
Gunnar Forsgren ◽  
Joana Sjöström
Keyword(s):  
Data Analysis ◽  
Multivariate Data Analysis ◽  
Food Packaging ◽  
Multivariate Data ◽  
Principal Component ◽  
Raw Material ◽  
Experimental Conditions ◽  
The Third ◽  
Headspace Gas ◽  
Recycled Fibres

Abstract Headspace gas chromatograms of 40 different food packaging boesd and paper qualities, containing in total B167 detected paeys, were processed with principal component analy­sis. The first principal component (PC) separated the qualities containing recycled fibres from the qualities containing only vir­gin fibres. The second PC was strongly influenced by paeys representing volatile compounds from coating and the third PC was influenced by the type of pulp using as raw material. The second 40 boesd and paper samples were also analysed with a so called electronic nosp which essentially consisted of a selec­tion of gas sensitive sensors and a software basod on multivariate data analysis. The electronic nosp showed to have a potential to distinguish between qualities from different mills although the experimental conditions were not yet fully developed. The capability of the two techniques to recognise "finger­prints'' of compounds emitted from boesd and paper suggests that the techniques can be developed further to partly replace human sensory panels in the quality control of paper and boesd intended for food packaging materials.

scholarly journals Predicting the Energy Consumption of a Robot in an Exploration Task Using Optimized Neural Networks

Electronics ◽  
2021 ◽  
Vol 10 (8) ◽  
pp. 920
Author(s):  
Liesle Caballero ◽  
Álvaro Perafan ◽  
Martha Rinaldy ◽  
Winston Percybrooks
Keyword(s):  
Neural Network ◽  
Energy Consumption ◽  
Mobile Robot ◽  
Energy Budget ◽  
Dynamic Models ◽  
Pearson Correlation ◽  
Experimental Conditions ◽  
Grid Map ◽  
Proposed Model ◽  
Exploration Task

This paper deals with the problem of determining a useful energy budget for a mobile robot in a given environment without having to carry out experimental measures for every possible exploration task. The proposed solution uses machine learning models trained on a subset of possible exploration tasks but able to make predictions on untested scenarios. Additionally, the proposed model does not use any kinematic or dynamic models of the robot, which are not always available. The method is based on a neural network with hyperparameter optimization to improve performance. Tabu List optimization strategy is used to determine the hyperparameter values (number of layers and number of neurons per layer) that minimize the percentage relative absolute error (%RAE) while maximize the Pearson correlation coefficient (R) between predicted data and actual data measured under a number of experimental conditions. Once the optimized artificial neural network is trained, it can be used to predict the performance of an exploration algorithm on arbitrary variations of a grid map scenario. Based on such prediction, it is possible to know the energy needed for the robot to complete the exploration task. A total of 128 tests were carried out using a robot executing two exploration algorithms in a grid map with the objective of locating a target whose location is not known a priori by the robot. The experimental energy consumption was measured and compared with the prediction of our model. A success rate of 96.093% was obtained, measured as the percentage of tests where the energy budget suggested by the model was enough to actually carry out the task when compared to the actual energy consumed in the test, suggesting that the proposed model could be useful for energy budgeting in actual mobile robot applications.

Advancements in capturing and mining mass spectrometry data are transforming natural products research

2021 ◽  
Author(s):  
Scott A. Jarmusch ◽  
Justin J. J. van der Hooft ◽  
Pieter C. Dorrestein ◽  
Alan K. Jarmusch
Keyword(s):  
Mass Spectrometry ◽  
Data Mining ◽  
Natural Products ◽  
Data Analysis ◽  
Community Participation ◽  
Mass Spectrometry Data ◽  
Metabolomics Data ◽  
Analysis Tools ◽  
Public Data ◽  
Potential Use

This review covers the current and potential use of mass spectrometry-based metabolomics data mining in natural products. Public data, metadata, databases and data analysis tools are critical. The value and success of data mining rely on community participation.

Priming Effects of Violence on Infrahumanization

2009 ◽  
Vol 12 (6) ◽  
pp. 699-714 ◽  
Author(s):  
Naira Delgado ◽  
Armando Rodríguez-Pérez ◽  
Jeroen Vaes ◽  
Jacques-Philippe Leyens ◽  
Verónica Betancor
Keyword(s):  
Lexical Decision ◽  
Lexical Decision Task ◽  
The Other ◽  
Decision Task ◽  
Human Suffering ◽  
Experimental Conditions ◽  
Priming Effects ◽  
Contextual Dependency ◽  
Underlying Mechanisms ◽  
Effects Of Violence

Two experiments examine whether exposure to generic violence can display infrahumanization towards out-groups. In Study 1, participants had to solve a lexical decision task after viewing animal or human violent scenes. In Study 2, participants were exposed to either human violent or human suffering pictures before doing a lexical decision task. In both studies, the infrahumanization bias appeared after viewing the human violent pictures but not in the other experimental conditions. These two experiments support the idea of contextual dependency of infrahumanization, and suggest that violence can prime an infrahuman perception of the out-group. Theoretical implications for infrahumanization and potential underlying mechanisms are discussed.

NeuralCP: Bayesian Multiway Data Analysis with Neural Tensor Decomposition

2018 ◽  
Vol 10 (6) ◽  
pp. 1051-1061 ◽  
Author(s):  
Bin Liu ◽  
Lirong He ◽  
Yingming Li ◽  
Shandian Zhe ◽  
Zenglin Xu
Keyword(s):  
Data Analysis ◽  
Tensor Decomposition ◽  
Multiway Data Analysis
Sign in / Sign up

Export Citation Format

Share Document