Special Issue "Emergence of Machine Learning in Biosensor"

A special issue of Biosensors (ISSN 2079-6374). This special issue belongs to the section "Intelligent Biosensors and Bio-Signal Processing".

Deadline for manuscript submissions: closed (31 August 2021).

Special Issue Editor

Prof. Dr. Weng Kung Peng
E-Mail Website
Guest Editor
Spin Dynamics in Health Engineering, Songshan Lake Materials Laboratoy, Building A1, University Innovation Park, Songshan Lake, Dongguan 523808, China
Interests: precision medicine; NMR-based PoCT; machine learning
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Special Issue Information

Dear Colleagues,

On behalf of Biosensors, we cordially invite you to submit a manuscript to our Special Issue entitled “Emergence of Machine Learning in Biosensor”.

With the recent advances (and maturity) in machine learning, it’s becoming computationally cheap to calculate a big dataset. In recent years, significant advances in biosensing technologies and novel methodologies exploiting machine learning have enabled a new wave of personalized medicine. These disruptive technologies have changed the landscape of personalized healthcare from in vivo imaging to in vitro diagnostics.

This Special Issue will discuss the recent advances of emerging technologies/methodologies with machine learning that have transformed or will transform the landscape of precision medicine. Submissions can be original research articles, reviews, or commentaries/perspectives.

Dr. Weng Kung Peng
Guest Editor

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 papers will be 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. Biosensors 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 1800 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.

Published Papers (1 paper)

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Research

Article
Mask R-CNN Based C. Elegans Detection with a DIY Microscope
Biosensors 2021, 11(8), 257; https://doi.org/10.3390/bios11080257 - 30 Jul 2021
Viewed by 1065
Abstract
Caenorhabditis elegans (C. elegans) is an important model organism for studying molecular genetics, developmental biology, neuroscience, and cell biology. Advantages of the model organism include its rapid development and aging, easy cultivation, and genetic tractability. C. elegans has been proven to be a [...] Read more.
Caenorhabditis elegans (C. elegans) is an important model organism for studying molecular genetics, developmental biology, neuroscience, and cell biology. Advantages of the model organism include its rapid development and aging, easy cultivation, and genetic tractability. C. elegans has been proven to be a well-suited model to study toxicity with identified toxic compounds closely matching those observed in mammals. For phenotypic screening, especially the worm number and the locomotion are of central importance. Traditional methods such as human counting or analyzing high-resolution microscope images are time-consuming and rather low throughput. The article explores the feasibility of low-cost, low-resolution do-it-yourself microscopes for image acquisition and automated evaluation by deep learning methods to reduce cost and allow high-throughput screening strategies. An image acquisition system is proposed within these constraints and used to create a large data-set of whole Petri dishes containing C. elegans. By utilizing the object detection framework Mask R-CNN, the nematodes are located, classified, and their contours predicted. The system has a precision of 0.96 and a recall of 0.956, resulting in an F1-Score of 0.958. Considering only correctly located C. elegans with an [email protected] IoU, the system achieved an average precision of 0.902 and a corresponding F1 Score of 0.906. Full article
(This article belongs to the Special Issue Emergence of Machine Learning in Biosensor)
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