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Applied Sciences
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Computational Intelligence in Bioinformatics
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Computational Intelligence in Bioinformatics

A special issue of Applied Sciences (ISSN 2076-3417). This special issue belongs to the section "Computing and Artificial Intelligence".

Deadline for manuscript submissions: closed (31 July 2021) | Viewed by 9640

Special Issue Editor

Prof. Dr. Joanna Polańska

E-Mail Website
Guest Editor
Department of Data Science and Engineering, Silesian University of Technology, 44-100 Gliwice, Poland
Interests: bioinformatics; data mining; statistics; machine learning; artificial intelligence in biology and medicine

Special Issue Information

Dear Colleagues,

Bioinformatics is an interdisciplinary field, including biology, computer science, information engineering, mathematics, and statistics to analyze and interpret biological data. Bioinformatics is widely applied for data management in modern biology and medicine.

Computational intelligence is a methodology involving adaptive mechanisms and/or an ability to learn that facilitates intelligent behavior in complex and changing environments.

In recent years, computational intelligence has been applied to bioinformatics as tools to deal with more complex questions in a quicker and more accurate way. Many methods including fuzzy logic, neural networks, machine learning, and soft computing could be applied gene expression clustering and classification, protein function prediction and its structure, gene selection, and so on.

The aim of this Special Issue is collecting high-quality papers in related fields that clarify the applications of Computational Intelligence in Bioinformatics.

Prof. Dr. Joanna Polańska
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 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. Applied Sciences 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 2400 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

  • bioinformatics
  • machine learning
  • artificial intelligence
  • gene expression
  • protein function prediction

Published Papers (2 papers)

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16 pages, 4471 KiB  
Article
YeastNet: Deep-Learning-Enabled Accurate Segmentation of Budding Yeast Cells in Bright-Field Microscopy
by Danny Salem, Yifeng Li, Pengcheng Xi, Hilary Phenix, Miroslava Cuperlovic-Culf and Mads Kærn
Appl. Sci. 2021, 11(6), 2692; https://doi.org/10.3390/app11062692 - 17 Mar 2021
Cited by 9 | Viewed by 3916
Abstract
Accurate and efficient segmentation of live-cell images is critical in maximizing data extraction and knowledge generation from high-throughput biology experiments. Despite recent development of deep-learning tools for biomedical imaging applications, great demand for automated segmentation tools for high-resolution live-cell microscopy images remains in [...] Read more.
Accurate and efficient segmentation of live-cell images is critical in maximizing data extraction and knowledge generation from high-throughput biology experiments. Despite recent development of deep-learning tools for biomedical imaging applications, great demand for automated segmentation tools for high-resolution live-cell microscopy images remains in order to accelerate the analysis. YeastNet dramatically improves the performance of the non-trainable classic algorithm, and performs considerably better than the current state-of-the-art yeast-cell segmentation tools. We have designed and trained a U-Net convolutional network (named YeastNet) to conduct semantic segmentation on bright-field microscopy images and generate segmentation masks for cell labeling and tracking. YeastNet enables accurate automatic segmentation and tracking of yeast cells in biomedical applications. YeastNet is freely provided with model weights as a Python package on GitHub. Full article
(This article belongs to the Special Issue Computational Intelligence in Bioinformatics)
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11 pages, 681 KiB  
Article
Facial Paralysis Detection on Images Using Key Point Analysis
by Gemma S. Parra-Dominguez, Raul E. Sanchez-Yanez and Carlos H. Garcia-Capulin
Appl. Sci. 2021, 11(5), 2435; https://doi.org/10.3390/app11052435 - 09 Mar 2021
Cited by 26 | Viewed by 5172
Abstract
The inability to move the muscles of the face on one or both sides is known as facial paralysis, which may affect the ability of the patient to speak, blink, swallow saliva, eat, or communicate through natural facial expressions. The well-being of the [...] Read more.
The inability to move the muscles of the face on one or both sides is known as facial paralysis, which may affect the ability of the patient to speak, blink, swallow saliva, eat, or communicate through natural facial expressions. The well-being of the patient could also be negatively affected. Computer-based systems as a means to detect facial paralysis are important in the development of standardized tools for medical assessment, treatment, and monitoring; additionally, they are expected to provide user-friendly tools for patient monitoring at home. In this work, a methodology to detect facial paralysis in a face photograph is proposed. A system consisting of three modules—facial landmark extraction, facial measure computation, and facial paralysis classification—was designed. Our facial measures aim to identify asymmetry levels within the face elements using facial landmarks, and a binary classifier based on a multi-layer perceptron approach provides an output label. The Weka suite was selected to design the classifier and implement the learning algorithm. Tests on publicly available databases reveal outstanding classification results on images, showing that our methodology that was used to design a binary classifier can be expanded to other databases with great results, even if the participants do not execute similar facial expressions. Full article
(This article belongs to the Special Issue Computational Intelligence in Bioinformatics)
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