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Bioengineering
Special Issues
Data-Driven Modeling of Biological Systems
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Data-Driven Modeling of Biological Systems

A special issue of Bioengineering (ISSN 2306-5354). This special issue belongs to the section "Biosignal Processing".

Deadline for manuscript submissions: closed (31 March 2024) | Viewed by 1368

Special Issue Editors

Prof. Dr. Jianfei Dong

E-Mail Website
Guest Editor
School of Future Science and Engineering, Soochow University, 1 Jiuyong West Road, Suzhou 215299, China
Interests: data-driven control and diagnosis; modeling and control of biological systems; artificial intelligence; light therapies
Dr. Marzia Cescon

E-Mail Website
Guest Editor
Department of Mechanical Engineering, University of Houston, Houston, TX 77004, USA
Interests: model-based control; data-driven control; first-principle based modeling

Special Issue Information

Dear Colleagues,

Modeling biological systems is extremely challenging compared with modeling their physical counterparts due to the numerous unmeasurable and even unknown processes underlying a phenomenon. Therefore, mechanistic models of such systems are still very rare and limited. Thanks to the advancement of computational sciences, system theories, and artificial intelligence, data-driven modeling methods and tools provide a great opportunity to boost biological system modeling. For instance, the analysis of DNA sequences and the development of drugs have been empowered with convolutional neural networks. Data-driven modeling also helps bridge the gap between analytical approaches to mechanistic modeling and the rich biological data observable from modern testing equipment.

This Special Issue on “Data-Driven Modeling of Biological Systems” will focus on innovative, original research articles and comprehensive reviews that reflect the latest developments in the related field. Both fundamental and applied research are welcome.

Submissions suitable for consideration may include but are not limited to:

  1. Modeling the interaction between cells and various physical factors, e.g., light, heat, and magnetic fields;
  2. Modeling dynamic reactions of signaling pathways triggered by medicine or chemicals;
  3. Modeling biomechanics of the human body and tissues in reaction to forces;
  4. Modeling neural connectivity and neurocognitive dynamics;
  5. Modeling and analysis of physiological signals, e.g., ECG and EEG;
  6. Modeling and analysis of biomedical images, e.g., CT and endoscopic images;
  7. System identification and AI methodology for modeling biological systems.

Prof. Dr. Jianfei Dong
Dr. Marzia Cescon
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Bioengineering is an international peer-reviewed open access monthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2700 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • data driven modeling
  • complex biological systems
  • chemical kinetics
  • signaling pathways
  • biomechanics
  • biomedical images

Published Papers (1 paper)

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Research

17 pages, 1429 KiB  
Article
Emotion Recognition from Physiological Signals Collected with a Wrist Device and Emotional Recall
by Enni Mattern, Roxanne R. Jackson, Roya Doshmanziari, Marieke Dewitte, Damiano Varagnolo and Steffi Knorn
Bioengineering 2023, 10(11), 1308; https://doi.org/10.3390/bioengineering10111308 - 11 Nov 2023
Viewed by 1033
Abstract
Implementing affective engineering in real-life applications requires the ability to effectively recognize emotions using physiological measurements. Despite being a widely researched topic, there seems to be a lack of systems that translate results from data collected in a laboratory setting to higher technology [...] Read more.
Implementing affective engineering in real-life applications requires the ability to effectively recognize emotions using physiological measurements. Despite being a widely researched topic, there seems to be a lack of systems that translate results from data collected in a laboratory setting to higher technology readiness levels. In this paper, we delve into the feasibility of emotion recognition beyond controlled laboratory environments. For this reason, we create a minimally-invasive experimental setup by combining emotional recall via autobiographical emotion memory tasks with a user-friendly Empatica wristband measuring blood volume pressure, electrodermal activity, skin temperature, and acceleration. We employ standard practices of feature-based supervised learning and specifically use support vector machines to explore subject dependency through various segmentation methods. We collected data from 45 participants. After preprocessing, using a data set of 134 segments from 40 participants, the accuracy of the classifier after 10-fold cross-validation was barely better than random guessing (36% for four emotions). However, when extracting multiple segments from each emotion task per participant using 10-fold cross-validation (i.e., including subject-dependent data in the training set), the classification rate increased to up to 75% for four emotions but was still as low as 32% for leave-one-subject-out cross-validation (i.e., subject-independent training). We conclude that highly subject-dependent issues might pose emotion recognition. Full article
(This article belongs to the Special Issue Data-Driven Modeling of Biological Systems)
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