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Computational Intelligence in Bioinformatics and Computational Biology
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Computational Intelligence in Bioinformatics and Computational Biology

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 October 2022) | Viewed by 7493

Special Issue Editor

Dr. Vincenzo Bonnici

E-Mail Website
Guest Editor
Department of Computer Science, University of Verona, Strada le Grazie, 15, 37134 Verona, Italy
Interests: computer science; bioinformatics; computational biology; parallel computing; graph theory; data integration
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Computational Intelligence (CI) is used in the area of bioinformatics (BI) for the purpose of advancing the area of computational niology (CB) and facilitating discoveries from biological data. Many methods, including fuzzy logic, neural networks, machine learning, and soft computing, could be applied to gene expression clustering and classification, protein function prediction and its structure, gene selection, and so on.

The aim of this Special Issue is to collect high-quality papers in related fields that clarify the applications of computational intelligence in bioinformatics and computational biology.

We invite researchers to contribute original research articles and reviews on several pertinent topics, including but not limited to:

  • Bioinformatics;
  • Computational biology;
  • Algorithms and data structures;
  • Graph theory;
  • Parallel computing.

Dr. Vincenzo Bonnici
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
  • computational biology
  • algorithms and data structures
  • graph theory
  • parallel computing

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Further information on MDPI's Special Issue polices can be found here.

Published Papers (2 papers)

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Research

15 pages, 577 KiB  
Article
A Mathematical Model for the Treatment of Melanoma with the BRAF/MEK Inhibitor and Anti-PD-1
by OPhir Nave and Moriah Sigron
Appl. Sci. 2022, 12(23), 12474; https://doi.org/10.3390/app122312474 - 6 Dec 2022
Cited by 6 | Viewed by 1706
Abstract
Skin cancer treatment is a combination of BRAF and MEK kinase inhibitors administered as tablets, along with immunotherapy treatment (treatment into the vein) with a group of drugs that inhibit the activity of the immune barrier proteins PD-1 and PDL1. Here, we propose [...] Read more.
Skin cancer treatment is a combination of BRAF and MEK kinase inhibitors administered as tablets, along with immunotherapy treatment (treatment into the vein) with a group of drugs that inhibit the activity of the immune barrier proteins PD-1 and PDL1. Here, we propose a new approach to the therapy for melanoma with the BRAF/MEK inhibitor and anti-PD-1. With the help of explicit analytical functions, we were able to model this combined treatment and present the treatment in a mathematical model described by a system of differential equations including variables, such as Treg, IL12, Il10, TGF-β, and cytokine, which are significant variables that are all critical factors which determine the effectiveness of therapies. The most significant advantage of a treatment described by a mathematical model with explicit analytical functions is the control of parameters, such as time and dose, which are variable critical parameters in the treatment, that is, these parameters can be adapted to the patient’s personalized treatment. In the current study, we showed that by simultaneously changing and combining these two parameters, we could decrease the tumor volume. To validate the numerical results, we computed the relative error between the results obtained from the mathematical model and clinical data. Full article
(This article belongs to the Special Issue Computational Intelligence in Bioinformatics and Computational Biology)
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16 pages, 789 KiB  
Article
An Interpretable Machine Learning Approach for Hepatitis B Diagnosis
by George Obaido, Blessing Ogbuokiri, Theo G. Swart, Nimibofa Ayawei, Sydney Mambwe Kasongo, Kehinde Aruleba, Ibomoiye Domor Mienye, Idowu Aruleba, Williams Chukwu, Fadekemi Osaye, Oluwaseun F. Egbelowo, Simelane Simphiwe and Ebenezer Esenogho
Appl. Sci. 2022, 12(21), 11127; https://doi.org/10.3390/app122111127 - 2 Nov 2022
Cited by 39 | Viewed by 4787
Abstract
Hepatitis B is a potentially deadly liver infection caused by the hepatitis B virus. It is a serious public health problem globally. Substantial efforts have been made to apply machine learning in detecting the virus. However, the application of model interpretability is limited [...] Read more.
Hepatitis B is a potentially deadly liver infection caused by the hepatitis B virus. It is a serious public health problem globally. Substantial efforts have been made to apply machine learning in detecting the virus. However, the application of model interpretability is limited in the existing literature. Model interpretability makes it easier for humans to understand and trust the machine-learning model. Therefore, in this study, we used SHapley Additive exPlanations (SHAP), a game-based theoretical approach to explain and visualize the predictions of machine learning models applied for hepatitis B diagnosis. The algorithms used in building the models include decision tree, logistic regression, support vector machines, random forest, adaptive boosting (AdaBoost), and extreme gradient boosting (XGBoost), and they achieved balanced accuracies of 75%, 82%, 75%, 86%, 92%, and 90%, respectively. Meanwhile, the SHAP values showed that bilirubin is the most significant feature contributing to a higher mortality rate. Consequently, older patients are more likely to die with elevated bilirubin levels. The outcome of this study can aid health practitioners and health policymakers in explaining the result of machine learning models for health-related problems. Full article
(This article belongs to the Special Issue Computational Intelligence in Bioinformatics and Computational Biology)
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