Digital Commons Network^™

Visualizing Metabolic Network Dynamics Through Time-Series Metabolomic Data., Lea F Buchweitz, James T Yurkovich, Christoph Blessing, Veronika Kohler, Fabian Schwarzkopf, Zachary A King, Laurence Yang, Freyr Jóhannsson, Ólafur E Sigurjónsson, Óttar Rolfsson, Julian Heinrich, Andreas Dräger

Articles, Abstracts, and Reports

BACKGROUND: New technologies have given rise to an abundance of -omics data, particularly metabolomic data. The scale of these data introduces new challenges for the interpretation and extraction of knowledge, requiring the development of innovative computational visualization methodologies. Here, we present GEM-Vis, an original method for the visualization of time-course metabolomic data within the context of metabolic network maps. We demonstrate the utility of the GEM-Vis method by examining previously published data for two cellular systems-the human platelet and erythrocyte under cold storage for use in transfusion medicine.

RESULTS: The results comprise two animated videos that allow for new insights …

Migrating From Partial Least Squares Discriminant Analysis To Artificial Neural Networks: A Comparison Of Functionally Equivalent Visualisation And Feature Contribution Tools Using Jupyter Notebooks, Kevin M. Mendez, David I. Broadhurst, Stacey N. Reinke

Research outputs 2014 to 2021

Introduction:

Metabolomics data is commonly modelled multivariately using partial least squares discriminant analysis (PLS-DA). Its success is primarily due to ease of interpretation, through projection to latent structures, and transparent assessment of feature importance using regression coefficients and Variable Importance in Projection scores. In recent years several non-linear machine learning (ML) methods have grown in popularity but with limited uptake essentially due to convoluted optimisation and interpretation. Artificial neural networks (ANNs) are a non-linear projection-based ML method that share a structural equivalence with PLS, and as such should be amenable to equivalent optimisation and interpretation methods.

Objectives:

We hypothesise that …

Chemometric And Bioinformatic Analyses Of Cellular Biochemistry, Bradley Worley

Department of Chemistry: Dissertations, Theses, and Student Research

The amount of information collected and analyzed in biochemical and bioanalytical research has exploded over the last few decades, due in large part to the increasing availability of analytical instrumentation that yields information-rich spectra. Datasets from Nuclear Magnetic Resonance (NMR), Mass Spectrometry (MS), infrared (IR) or Raman spectroscopy may easily carry tens to hundreds of thousands of potentially correlated variables observed from only a few samples, making the application of classical statistical methods inappropriate, if not impossible. Drawing useful biochemical conclusions from these unique sources of data requires the use of specialized multivariate data handling techniques.

Unfortunately, proper implementation of …

Metabolomics Characterization Of U.S. And Japanese F-15 And C-130 Flight Line Crews Exposed To Jet Fuel Volatile Organic Compounds And Aerosols, Nicholas J. Delraso, David Mattie, Asao Kobayashi, Stephen W. Mitchell, Scott Dillard, Michael L. Raymer, Isaie Sibomana, Nicholas V. Reo

Computer Science and Engineering Faculty Publications

Air and ground crews transfer a significant amount of jet fuel, and as a result of transfers, breathe its volatile emission from residues. Working on the flight line also exposes maintainers to exhaust from the jet fuel as engines are tested or run before and after flight. Since little is known concerning level of exposure and the corresponding biological response associated with human jet fuel exposure, nuclear magnetic resonance (NMR)-based metabolomics analysis of human urine was utilized for characterization of metabolite profiles of flight line personnel for potential biomarker discovery. This project was a collaborative research effort between the US …

Biomarkers Of Fatigue: Metabolomics Profiles Predictive Of Cognitive Performance, Nicholas J. Delraso, Deirdre A. Mahle, John J. Schlager, Donald L. Harville, Scott R. Chaiken, Danelle K. Roddy, Mari Chamberlain, Paul E. Anderson, Nicholas V. Reo, Michael L. Raymer, Isaie Sibomana

Computer Science and Engineering Faculty Publications

Cognitive performance and fatigue are well known to be inversely related. Continuous and sustained actions in operational environments typically lead to reduced sleep normally required to perform optimally. These operational environments subject the warfighter to intense physical and mental exertion. Because fatigue continues to be an occupational hazard, leading to cognitive defects in performance, there has been a recognized need for real-time detection technologies that minimize fatigue-induced mishaps. I the current study, 23 subjects were subjected to 36h of sleep deprivation and cognitive psychomotor vigilance and automated neuropsychological assessment metric tests were conducted over the last 24 h of sleep …

Localized Deconvolution: Characterizing Nmr-Based Metabolomics Spectroscopic Data Using Localized High-Throughput Deconvolution, Paul E. Anderson, Ajith H. Ranabahu, Deirdre A. Mahle, Nicholas V. Reo, Michael L. Raymer, Amit P. Sheth, Nicholas J. Delraso

Kno.e.sis Publications

The interpretation of nuclear magnetic resonance (NMR) experimental results for metabolomics studies requires intensive signal processing and multivariate data analysis techniques. Standard quantification techniques attempt to minimize effects from variations in peak positions caused by sample pH, ionic strength, and composition. These techniques fail to account for adjacent signals which can lead to drastic quantification errors. Attempts at full spectrum deconvolution have been limited in adoption and development due to the computational resources required. Herein, we develop a novel localized deconvolution algorithm for general purpose quantification of NMR-based metabolomics studies. Localized deconvolution decreases average absolute quantification error by 97% and …

Identifying And Implementing The Underlying Operators For Nuclear Magnetic Resonance Based Metabolomics Data Analysis, Ashwin Manjunatha, Ajith H. Ranabahu, Paul E. Anderson, Amit P. Sheth

Kno.e.sis Publications

The science of metabolomics is a relatively young field that requires intensive signal processing and multivariate data analysis for interpretation of experimental results. The lack of integration and standardization for metabolomics compounded by the complexity of the experimental data has lead to a fragmented research community. While efforts have been undertaken to approach these problems, the efforts to develop a set of standards for reporting processing and analysis procedures has stalled.

In this paper, we propose a set of fundamental operators for nuclear magnetic resonance(NMR) based metabolomics. These operators are implementation independent, and can be used to easily and precisely …

Getting Code Near The Data: A Study Of Generating Customized Data Intensive Scientific Workflows With Domain Specific Language, Ashwin Manjunatha, Ajith Harshana Ranabahu, Paul E. Anderson, Amit P. Sheth

Kno.e.sis Publications

The amount of data produced in modern biological experiments such as Nuclear Magnetic Resonance (NMR) analysis far exceeds the processing capability of a single machine. The present state-of-the-art is taking the ”data to code”, the philosophy followed by many of the current service oriented workflow systems. However this is not feasible in some cases such as NMR data analysis, primarily due to the large scale of data.

The objective of this research is to bring ”code to data”, preferred in the cases when the data is extremely large. We present a DSL based approach to develop customized data intensive scientific …