Browsing Brage NMBU by Author "Tøndel, Kristin"
Now showing items 1-10 of 10
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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 ... -
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) -
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 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) -
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. ...