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Sensors
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Sensors-Based Biomarker Detection and Bioinformatics Analysis
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Sensors-Based Biomarker Detection and Bioinformatics Analysis

A special issue of Sensors (ISSN 1424-8220). This special issue belongs to the section "Intelligent Sensors".

Deadline for manuscript submissions: 25 January 2025 | Viewed by 1001

Special Issue Editors

Prof. Dr. Panayiotis Vlamos

E-Mail Website
Guest Editor
Bioinformatics and Human Electrophysiology Laboratory, Department of Informatics, Ionian University, 49100 Corfu, Greece
Interests: bioinformatics; mathematical modeling; big data; neurodegenerative diseases
Special Issues, Collections and Topics in MDPI journals
Dr. Marios Krokidis

E-Mail Website
Guest Editor
Bioinformatics and Human Electrophysiology Laboratory, Department of Informatics, Ionian University, 49100 Corfu, Greece
Interests: bioinformatics; computational biomedicine; drug discovery, molecular analysis, biomarkers; neurodegenerative diseases
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

The research on digital health and biosensing technology has been growing rapidly in recent years. In parallel, bioinformatics and data analytics have revolutionized systems biology and clinical practice, providing important tools to identify molecular mechanisms and biological networks, and diminishing the time and cost required. Data mining extracts biomedical results that we are not able to obtain through traditional computing tools and contributes to the early diagnosis and monitoring of progression. AI technologies make innovations possible that are fundamental for transforming large amounts of data to detect patient subgroups that might benefit more in a clinical trial. Digitalization and innovative technologies in healthcare, such as Internet of Things (IoT), image analysis and data analytics, can also contribute to the real-time remote monitoring of patients and contribute to the informatics infrastructure necessary to facilitate the appropriate utilization of precision medicine.

This Special Issue aims to highlight recent advances in biosensors, remote sensing platforms and digital developments, and will cover promising methods and models for big data analytics and bioinformatics approaches in clinical practice, as well as biomarkers for prognosis, diagnosis and therapeutics. Both original research and review articles dealing with the application of these mentioned methods are expected and welcome.

Prof. Dr. Panagiotis Vlamos
Dr. Marios G. Krokidis
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. Sensors is an international peer-reviewed open access semimonthly 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 2600 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 analytics
  • remote sensing platforms
  • biosensors
  • biomarkers
  • bioinformatics
  • digital health
  • neurodegenerative diseases
  • clinical trials
  • artificial intelligence
  • wearable devices

Published Papers (1 paper)

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Research

19 pages, 6342 KiB  
Article
Modeling and Optimization of an Enhanced Soft Sensor for the Fermentation Process of Pichia pastoris
by Bo Wang, Ameng Yu, Haibo Wang and Jun Liu
Sensors 2024, 24(10), 3017; https://doi.org/10.3390/s24103017 - 9 May 2024
Viewed by 423
Abstract
This paper proposes a novel soft sensor modeling approach, MIC-TCA-INGO-LSSVM, to address the decline in performance of soft sensor models during the fermentation process of Pichia pastoris, caused by changes in working conditions. Initially, the transfer component analysis (TCA) method is utilized [...] Read more.
This paper proposes a novel soft sensor modeling approach, MIC-TCA-INGO-LSSVM, to address the decline in performance of soft sensor models during the fermentation process of Pichia pastoris, caused by changes in working conditions. Initially, the transfer component analysis (TCA) method is utilized to minimize the differences in data distribution across various working conditions. Subsequently, a least squares support vector machine (LSSVM) model is constructed using the dataset adapted by TCA, and strategies for improving the northern goshawk optimization (INGO) algorithm are proposed to optimize the parameters of the LSSVM model. Finally, to further enhance the model’s generalization ability and prediction accuracy, considering the transfer of knowledge from multiple-source working conditions, a sub-model weighted ensemble scheme is proposed based on the maximum information coefficient (MIC) algorithm. The proposed soft sensor model is employed to predict cell and product concentrations during the fermentation process of Pichia pastoris. Simulation results indicate that the RMSE of the INGO-LSSVM model in predicting cell and product concentrations is reduced by 47.3% and 42.1%, respectively, compared to the NGO-LSSVM model. Additionally, TCA significantly enhances the model’s adaptability when working conditions change. Moreover, the soft sensor model based on TCA and the MIC-weighted ensemble method achieves a reduction of 41.6% and 31.3% in the RMSE for predicting cell and product concentrations, respectively, compared to the single-source condition transfer model TCA-INGO-LSSVM. These results demonstrate the high reliability and predictive performance of the proposed soft sensor method under varying working conditions. Full article
(This article belongs to the Special Issue Sensors-Based Biomarker Detection and Bioinformatics Analysis)
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