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Related papers: Dynamical models for metabolomics data integration

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Generalized models provide a framework for the study of evolution equations without specifying all functional forms. The generalized formulation of problems has been shown to facilitate the analytical investigation of local dynamics and has…

Dynamical Systems · Mathematics 2014-06-24 Christian Kuehn , Stefan Siegmund , Thilo Gross

The use of machine learning algorithms to predict behaviors of complex systems is booming. However, the key to an effective use of machine learning tools in multi-physics problems, including combustion, is to couple them to physical and…

Modern communication networks are inherently complex in nature. First of all, they have a large number of heterogeneous components. Secondly, their connectivity is extremely dynamic. Nodes can come and go, links can be removed and added…

Social and Information Networks · Computer Science 2017-08-08 Bisma S. Khan , Muaz A. Niazi

This paper has been withdrawn. With the advancement of statistical theory and computing power, data sets are providing a greater amount of insight into the problems of today. Statisticians have an ever increasing number of tools to attack…

Statistics Theory · Mathematics 2012-12-20 Derek S. Young

Background: Determining sample sizes for metabolomic experiments is important but due to the complexity of these experiments, there are currently no standard methods for sample size estimation in metabolomics. Since pilot studies are rarely…

The metabolic networks are very well characterized for a large set of organisms, a unique case in within the large-scale biological networks. For this reason they provide a a very interesting framework for the construction of analytically…

Molecular Networks · Quantitative Biology 2007-05-23 Ginestra Bianconi , Riccardo Zecchina

Deep learning is transforming many areas in science, and it has great potential in modeling molecular systems. However, unlike the mature deployment of deep learning in computer vision and natural language processing, its development in…

Computational Physics · Physics 2021-03-19 Jun Zhang , Yao-Kun Lei , Zhen Zhang , Junhan Chang , Maodong Li , Xu Han , Lijiang Yang , Yi Isaac Yang , Yi Qin Gao

Metabonomics time-course experiments provide the opportunity to understand the changes to an organism by observing the evolution of metabolic profiles in response to internal or external stimuli. Along with other omic longitudinal profiling…

Applications · Statistics 2018-04-09 Takoua Jendoubi , Timothy M. D. Ebbels

Data-driven modeling and machine learning are widely used to model the behavior of dynamic systems. One application is the residual evaluation of technical systems where model predictions are compared with measurement data to create…

Machine Learning · Computer Science 2023-05-09 Arman Mohammadi , Theodor Westny , Daniel Jung , Mattias Krysander

Nonadiabatic molecular dynamics simulations aim to describe the coupled electron-nuclear dynamics of molecules in excited electronic states. These simulations have been applied to understand a plethora of photochemical and photophysical…

In this work, we have developed a framework for synthesizing data driven controllers for a class of uncertain switched systems arising in an application to physical activity interventions. In particular, we present an application of…

Systems and Control · Electrical Eng. & Systems 2021-08-27 Ibrahim E. Bardakci , Sahar Hojjatinia , Sarah Hojjatinia , Constantino M. Lagoa , David E. Conroy

The human-associated microbiome is closely tied to human health and is of substantial clinical interest. Metagenomics-based tools are emerging for clinical diagnostics, tracking the spread of diseases, and surveillance of potential…

Modern scientific computational methods are undergoing a transformative change; big data and statistical learning methods now have the potential to outperform the classical first-principles modeling paradigm. This book bridges this…

Data Analysis, Statistics and Probability · Physics 2018-03-22 John Harlim

Modern intelligent urban mobility applications are underpinned by large-scale, multivariate, spatiotemporal data streams. Working with this data presents unique challenges of data management, processing and presentation that is often…

Computers and Society · Computer Science 2021-01-25 Michael Wilbur , Philip Pugliese , Aron Laszka , Abhishek Dubey

At the core of some of the most important problems in plasma physics -- from controlled nuclear fusion to the acceleration of cosmic rays -- is the challenge to describe nonlinear, multi-scale plasma dynamics. The development of reduced…

Plasma Physics · Physics 2022-09-13 E. Paulo Alves , Frederico Fiuza

Control of a dynamical system without the knowledge of dynamics is an important and challenging task. Modern machine learning approaches, such as deep neural networks (DNNs), allow for the estimation of a dynamics model from control inputs…

Systems and Control · Electrical Eng. & Systems 2023-11-14 Suruchi Sharma , Volodymyr Makarenko , Gautam Kumar , Stas Tiomkin

The majority of real-world networks are dynamic and extremely large (e.g., Internet Traffic, Twitter, Facebook, ...). To understand the structural behavior of nodes in these large dynamic networks, it may be necessary to model the dynamics…

Social and Information Networks · Computer Science 2015-03-20 Ryan Rossi , Brian Gallagher , Jennifer Neville , Keith Henderson

Recent work on database application development platforms has sought to include a declarative formulation of a conceptual data model in the application code, using annotations or attributes. Some recent work has used metadata to include the…

Databases · Computer Science 2023-08-15 Malcolm Crowe , Fritz Laux

Biological networks are one of the most studied object in computational biology. Several methods have been developed for studying qualitative properties of biological networks. Last decade had seen the improvement of molecular techniques…

Quantitative Methods · Quantitative Biology 2016-08-14 Jérémie Bourdon , Damien Eveillard

Machine learning models often require large datasets and struggle to generalize beyond their training distribution. These limitations pose significant challenges in scientific and engineering contexts, where generating exhaustive datasets…

Chemical Physics · Physics 2025-06-12 Salman N. Salman , Sergey A. Shteingolts , Ron Levie , Dan Mendels