Data-Driven Modeling for Chronic Diseases

A special issue of Journal of Personalized Medicine (ISSN 2075-4426). This special issue belongs to the section "Methodology, Drug and Device Discovery".

Deadline for manuscript submissions: closed (22 May 2022) | Viewed by 7639

Special Issue Editors

Department of Mathematics, The Pennsylvania State University, University Park, PA 16802, USA
Interests: data-driven modeling; personalized medicine; nonlinear computation

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Guest Editor
Department of Mathematics and Statistics, University of Massachusetts Amherst, Amherst, MA 01003, USA
Interests: mathematical biology; bioinformatics; mechanistic modeling
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Computational data-driven modelling approaches have already achieved success in analysing multi-dimensional clinical data in chronic diseases. Such data-driven approaches employ mathematical models for patient populations using clinical, omics, and biomarker data, as well as powerful and new means to personalize such models based on individual data, yielding personal risk profiles. These data-driven modelling approaches can simulate complex systems, helping to elucidate complex physiological interactions and optimize personalized prevention and treatment strategies. This Special Issue of the Journal of Personalized Medicine aims to highlight the current state of the development and showcase some of the latest findings in the field of data-driven modelling for chronic diseases. Scientific advances in the field of data-driven modelling will contribute significantly to personalized medicine of chronic diseases.

Dr. Wenrui Hao
Dr. Leili Shahriyari
Guest Editors

Manuscript Submission Information

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Keywords

  • data-driven modelling
  • artificial intelligence
  • personalized diagnosis/prediction
  • computational modeling of chronic diseases
  • precision medicine

Published Papers (3 papers)

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Research

27 pages, 2281 KiB  
Article
Patient-Specific Mathematical Model of the Clear Cell Renal Cell Carcinoma Microenvironment
by Dilruba Sofia, Navid Mohammad Mirzaei and Leili Shahriyari
J. Pers. Med. 2022, 12(10), 1681; https://doi.org/10.3390/jpm12101681 - 9 Oct 2022
Cited by 2 | Viewed by 1844
Abstract
The interactions between cells and molecules in the tumor microenvironment can give insight into the initiation and progression of tumors and their optimal treatment options. In this paper, we developed an ordinary differential equation (ODE) mathematical model of the interaction network of key [...] Read more.
The interactions between cells and molecules in the tumor microenvironment can give insight into the initiation and progression of tumors and their optimal treatment options. In this paper, we developed an ordinary differential equation (ODE) mathematical model of the interaction network of key players in the clear cell renal cell carcinoma (ccRCC) microenvironment. We then performed a global gradient-based sensitivity analysis to investigate the effects of the most sensitive parameters of the model on the number of cancer cells. The results indicate that parameters related to IL-6 have high a impact on cancer cell growth, such that decreasing the level of IL-6 can remarkably slow the tumor’s growth. Full article
(This article belongs to the Special Issue Data-Driven Modeling for Chronic Diseases)
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26 pages, 5304 KiB  
Article
A PDE Model of Breast Tumor Progression in MMTV-PyMT Mice
by Navid Mohammad Mirzaei, Zuzana Tatarova, Wenrui Hao, Navid Changizi, Alireza Asadpoure, Ioannis K. Zervantonakis, Yu Hu, Young Hwan Chang and Leili Shahriyari
J. Pers. Med. 2022, 12(5), 807; https://doi.org/10.3390/jpm12050807 - 17 May 2022
Cited by 7 | Viewed by 3594
Abstract
The evolution of breast tumors greatly depends on the interaction network among different cell types, including immune cells and cancer cells in the tumor. This study takes advantage of newly collected rich spatio-temporal mouse data to develop a data-driven mathematical model of breast [...] Read more.
The evolution of breast tumors greatly depends on the interaction network among different cell types, including immune cells and cancer cells in the tumor. This study takes advantage of newly collected rich spatio-temporal mouse data to develop a data-driven mathematical model of breast tumors that considers cells’ location and key interactions in the tumor. The results show that cancer cells have a minor presence in the area with the most overall immune cells, and the number of activated immune cells in the tumor is depleted over time when there is no influx of immune cells. Interestingly, in the case of the influx of immune cells, the highest concentrations of both T cells and cancer cells are in the boundary of the tumor, as we use the Robin boundary condition to model the influx of immune cells. In other words, the influx of immune cells causes a dominant outward advection for cancer cells. We also investigate the effect of cells’ diffusion and immune cells’ influx rates in the dynamics of cells in the tumor micro-environment. Sensitivity analyses indicate that cancer cells and adipocytes’ diffusion rates are the most sensitive parameters, followed by influx and diffusion rates of cytotoxic T cells, implying that targeting them is a possible treatment strategy for breast cancer. Full article
(This article belongs to the Special Issue Data-Driven Modeling for Chronic Diseases)
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28 pages, 5258 KiB  
Article
Tracing and Forecasting Metabolic Indices of Cancer Patients Using Patient-Specific Deep Learning Models
by Jianguo Hou, Jun Deng, Chunyan Li and Qi Wang
J. Pers. Med. 2022, 12(5), 742; https://doi.org/10.3390/jpm12050742 - 2 May 2022
Cited by 2 | Viewed by 1697
Abstract
We develop a patient-specific dynamical system model from the time series data of the cancer patient’s metabolic panel taken during the period of cancer treatment and recovery. The model consists of a pair of stacked long short-term memory (LSTM) recurrent neural networks and [...] Read more.
We develop a patient-specific dynamical system model from the time series data of the cancer patient’s metabolic panel taken during the period of cancer treatment and recovery. The model consists of a pair of stacked long short-term memory (LSTM) recurrent neural networks and a fully connected neural network in each unit. It is intended to be used by physicians to trace back and look forward at the patient’s metabolic indices, to identify potential adverse events, and to make short-term predictions. When the model is used in making short-term predictions, the relative error in every index is less than 10% in the L norm and less than 6.3% in the L1 norm in the validation process. Once a master model is built, the patient-specific model can be calibrated through transfer learning. As an example, we obtain patient-specific models for four more cancer patients through transfer learning, which all exhibit reduced training time and a comparable level of accuracy. This study demonstrates that this modeling approach is reliable and can deliver clinically acceptable physiological models for tracking and forecasting patients’ metabolic indices. Full article
(This article belongs to the Special Issue Data-Driven Modeling for Chronic Diseases)
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