Browsing Brage NMBU by Author "Tøndel, Kristin"
Now showing items 1-17 of 17
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A computational pipeline for quantification of mouse myocardial stiffness parameters
Nordbø, Øyvind; Lamata, P.; Land, Sander; Niederer, Steven A.; Aronsen, Jan Magnus; Louch, William Edward; Sjaastad, Ivar; Martens, Harald; Gjuvsland, Arne Bjørke; Tøndel, Kristin; Torp, Hans; Lohezic, M; Schneider, Jürgen; Remme, Espen W.; Smith, Nicolas P; Omholt, Stig W; Vik, Jon Olav (Journal article; Peer reviewed, 2014)The mouse is an important model for theoretical–experimental cardiac research, and biophysically based whole organ models of the mouse heart are now within reach. However, the passive material properties of mouse myocardium ... -
Current overview and way forward for the use of machine learning in the field of petroleum gas hydrates
Gjelsvik, Elise Lunde; Fossen, Martin; Tøndel, Kristin (Peer reviewed; Journal article, 2022)Gas hydrates represent one of the main flow assurance challenges in the oil and gas industry as they can lead to plugging of pipelines and process equipment. In this paper we present a literature study performed to evaluate ... -
Deep convolutional neural network recovers pure absorbance spectra from highly scatter‐distorted spectra of cells
Magnussen, Eirik Almklov; Solheim, Johanne Heitmann; Blazhko, Uladzislau; Tafintseva, Valeria; Tøndel, Kristin; Liland, Kristian Hovde; Dzurendova, Simona; Shapaval, Volha; Sandt, Christophe; Borondics, Ferenc; Kohler, Achim (Peer reviewed; Journal article, 2020) -
Deep learning-assisted analysis of infrared microspectroscopic data for enhanced chemical and optical characterization
Magnussen, Eirik Almklov (PhD thesis;2024:15, Doctoral thesis, 2024)Infrared (IR) spectroscopy is an accurate, rapid and non-destructive analytical technique widely applied across diverse scientific disciplines. It records an absorbance spectrum that quantifies the loss of intensity as IR ... -
Deep learning-enabled Inference of 3D molecular absorption distribution of biological cells from IR spectra
Magnussen, Eirik Almklov; Zimmermann, Boris; Blazhko, Uladzislau; Dzurendová, Simona; Dupuy--Galet, Benjamin Xavier; Byrtusova, Dana; Muthreich, Florian; Tafintseva, Valeria; Liland, Kristian Hovde; Tøndel, Kristin; Shapaval, Volha; Kohler, Achim (Peer reviewed; Journal article, 2022)Infrared spectroscopy delivers abundant information about the chemical composition, as well as the structural and optical properties of intact samples in a non-destructive manner. We present a deep convolutional neural ... -
Deep learning-enabled Inference of 3D molecular absorption distribution of biological cells from IR spectra
Magnussen, Eirik Almklov; Zimmermann, Boris; Blazhko, Uladzislau; Dzurendová, Simona; Dupuy--Galet, Benjamin Xavier; Byrtusova, Dana; Muthreich, Florian; Tafintseva, Valeria; Liland, Kristian Hovde; Tøndel, Kristin; Shapaval, Volha; Kohler, Achim (Peer reviewed; Journal article, 2022) -
Grayscale representation of infrared microscopy images by Extended Multiplicative Signal Correction for registration with histological images
Trukhan, Stanislau; Tafintseva, Valeria; Tøndel, Kristin; Großerueschkamp, Frederik; Mosig, Axel; Kovalev, Vassili; Gerwert, Klaus; Kohler, Achim (Peer reviewed; Journal article, 2020)Fourier-transform infrared (FTIR) microspectroscopy is rounding the corner to become a label-free routine method for cancer diagnosis. In order to build infrared-spectral based classifiers, infrared images need to be ... -
Hierarchical multivariate regression-based sensitivity analysis reveals complex parameter interaction patterns in dynamic models
Tøndel, Kristin; Vik, Jon Olav; Martens, Harald; Indahl, Ulf Geir; Smith, Nic; Omholt, Stig W (Journal article; Peer reviewed, 2013) -
Is heart failure with mid range ejection fraction (HFmrEF) a distinct clinical entity or an overlap group?
Webb, Jessica; Draper, Jane; Fovargue, Lauren; Sieniewicz, Benjamin; Gould, Justin; Claridge, Simon; Barton, Carys; Smith, Silapiya; Tøndel, Kristin; Ronak, Rajani; Kapetanakis, Stamatis; Rinaldi, Christopher A; McDonagh, Theresa A.; Razavi, Reza; Carr-White, Gerald (Journal article; Peer reviewed, 2018) -
Metamodelling of a computational model of cardiac physiology using multivariate regression and deep learning
Gnawali, Ashesh Raj (Master thesis, 2021)The primary goal of this thesis is to model the heart function. This thesis investigates how data-driven modelling might help with this. Mechanistic models, which are theory-driven and guided by a system of differential ... -
Metamodelling of a two-population spiking neural network
Skaar, Jan-Eirik Welle; Haug, Nicolai; Stasik, Alexander Johannes; Einevoll, Gaute; Tøndel, Kristin (Peer reviewed; Journal article, 2023) -
Metamodelling of simulation results from Brunel’s Neural Network model using Local Multivariate Regression (HC-PLSR)
Stene, Anja (Master thesis, 2020)In efforts of explaining biological system behavior, a common mean has been to use mathematical models. To model intricate biological systems does often require complex, non-linear and high-dimensional differential equation ... -
Metamodelling of the Hodgkin-Huxley model and the Pinsky-Rinzel model using local multivariate regression and deep learning
Ødegaard, Lars Erik (Master thesis, 2019)Biological processes, such as the electrical activity in neurons, are often modelled using complex, non-linear and high dimensional differential systems. Such models are usually associated with a high computational cost. ... -
Morphological heterogeneity in pancreatic cancer reflects structural and functional divergence
Santha, Petra; Lenggenhager, Daniela; Vefferstad, Anette; Dorg, Linda Trobe; Tøndel, Kristin; Amrutkar, Manoj; Gladhaug, Ivar Prydz; Verbeke, Caroline Sophie (Peer reviewed; Journal article, 2021) -
New understanding of gas hydrate phenomena and natural inhibitors in crude oil systems through mass spectrometry and machine learning
Gjelsvik, Elise Lunde (PhD thesis;2023:35, Doctoral thesis, 2023)Gas hydrates represent one of the main flow assurance issues in the oil and gas industry as they can cause complete blockage of pipelines and process equipment, forcing shut downs. Previous studies have shown that some ... -
On the application of machine learning techniques for phenotypic classification and clustering of heart failure patients
Adrik, Samir (Master thesis, 2018)In this thesis, we attempt to investigate how well various clustering algorithms (hierarchical clustering, k-means and expectation–maximization) perform in producing phenotypically distinct clinical patient groups (i.e. ... -
Using machine learning-based variable selection to identify hydrate related components from FT-ICR MS spectra
Gjelsvik, Elise Lunde; Fossen, Martin; Brunsvik, Anders; Tøndel, Kristin (Journal article; Peer reviewed, 2022)The blockages of pipelines caused by agglomeration of gas hydrates is a major flow assurance issue in the oil and gas industry. Some crude oils form gas hydrates that remain as transportable particles in a slurry. It is ...