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Mathematics
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Computational Intelligence and Machine Learning in Bioinformatics
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Computational Intelligence and Machine Learning in Bioinformatics

A special issue of Mathematics (ISSN 2227-7390). This special issue belongs to the section "Computational and Applied Mathematics".

Deadline for manuscript submissions: closed (25 March 2023) | Viewed by 10515

Special Issue Editors

Prof. Dr. Miguel Enrique Iglesias Martínez

E-Mail Website
Guest Editor
Physical Engineering Group, School of Aeronautical and Space Engineering, University of Vigo, Edif. Manuel Martínez Risco, Campus de As Lagoas, 32004 Ourense, Spain
Interests: statistical signal processing; automated pattern recognition; electronics and communication
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. Isabel De la Torre Díez

E-Mail Website
Guest Editor
Department of Signal Theory and Communications and Telematics Engineering, University of Valladolid, Valladolid, Spain
Interests: ehealth; medical data analysis; telemedicine

Special Issue Information

Dear Colleagues,

The importance of machine learning computational intelligence is well known today. This branch is included in the resolution of problems of various kinds, whether theoretical or practical. To solve real-life problems, computational intelligence has a great impact in terms of quality and adaptability in various domains, including bioinformatic applications, derived from results with biological and health processes.

The objective of this Special Issue is to compile a collection of articles that reflect the latest advances in computational intelligence and machine learning in bioinformatics applications, including genetic algorithm techniques, neural networks, fuzzy logic, support vector machines, and other techniques related to the application of signal analysis and pattern recognition applying learning and classification algorithms.

Contributions are welcome on both theoretical and practical models. The selection criteria will be based on formal and technical soundness, experimental support, and the relevance of the contribution.

Prof. Dr. Miguel Enrique Iglesias Martínez
Prof. Dr. Isabel De la Torre Díez
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. Mathematics 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

  • evolutionary algorithms and optimization
  • computational biology
  • machine learning in bioinformatics
  • deep learning in bioinformatics
  • fuzzy logic and genetic algorithms
  • biomedical signals and image processing
  • adaptive systems in bioinformatics
  • big data and pattern recognition in bioinformatics

Published Papers (5 papers)

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Research

Jump to: Review

28 pages, 4307 KiB  
Article
EEG-Based Classification of Epileptic Seizure Types Using Deep Network Model
by Hend Alshaya and Muhammad Hussain
Mathematics 2023, 11(10), 2286; https://doi.org/10.3390/math11102286 - 14 May 2023
Cited by 5 | Viewed by 1537
Abstract
Accurately identifying the seizure type is vital in the treatment plan and drug prescription for epileptic patients. The most commonly adopted test for identifying epileptic seizures is electroencephalography (EEG). EEG signals include important information about the brain’s electrical activities and are widely used [...] Read more.
Accurately identifying the seizure type is vital in the treatment plan and drug prescription for epileptic patients. The most commonly adopted test for identifying epileptic seizures is electroencephalography (EEG). EEG signals include important information about the brain’s electrical activities and are widely used for epilepsy analysis. Among various deep network architectures, convolutional neural networks (CNNs) have been widely used for EEG signal representation learning for epilepsy analysis. However, most of the existing CNN-based methods suffer from the overfitting problem due to a small number of EEG trials and the huge number of learnable parameters. This paper introduces the design of an efficient, lightweight, and expressive deep network model based on ResNet theory and long short-term memory (LSTM) for classifying seizure types from EEG trials. A 1D ResNet module is adopted to train a deeper network without encountering vanishing gradient problems and to avoid the overfitting problem of CNN models. The LSTM module encodes and learns long-term dependencies over time. The synthetic minority oversampling technique (SMOTE) is applied to balance the data by increasing the trials of minority classes. The proposed method was evaluated using the public domain benchmark TUH database. Experimental results revealed the superior performance of the proposed model over other state-of-the-art models with an F1-score of 97.4%. The proposed deep learning model will help neurologists precisely interpret and classify epileptic seizure types and enhance the patient’s life. Full article
(This article belongs to the Special Issue Computational Intelligence and Machine Learning in Bioinformatics)
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12 pages, 1786 KiB  
Article
Nonlinear Techniques and Ridge Regression as a Combined Approach: Carcinoma Identification Case Study
by Gerardo Alfonso Perez and Raquel Castillo
Mathematics 2023, 11(8), 1795; https://doi.org/10.3390/math11081795 - 10 Apr 2023
Cited by 2 | Viewed by 1040
Abstract
As more genetic information becomes available, such as DNA methylation levels, it becomes increasingly important to have techniques to analyze such data in the context of cancers such as anal and cervical carcinomas. In this paper, we present an algorithm that differentiates between [...] Read more.
As more genetic information becomes available, such as DNA methylation levels, it becomes increasingly important to have techniques to analyze such data in the context of cancers such as anal and cervical carcinomas. In this paper, we present an algorithm that differentiates between healthy control patients and individuals with anal and cervical carcinoma, using as an input DNA methylation data. The algorithm used a combination of ridge regression and neural networks for the classification task, achieving high accuracy, sensitivity and specificity. The relationship between methylation levels and carcinoma could in principle be rather complex, particularly given that a large number of CpGs could be involved. Therefore, nonlinear techniques (machine learning) were used. Machine learning techniques (nonlinear) can be used to model linear processes, but the opposite (linear techniques simulating nonlinear models) would not likely generate accurate forecasts. The feature selection process is carried out using a combination of prefiltering, ridge regression and nonlinear modeling (artificial neural networks). The model selected 13 CpGs from a total of 450,000 CpGs available per patient with 171 patients in total. The model was also tested for robustness and compared to other more complex models that generated less precise classifications. The model obtained (testing dataset) an accuracy, sensitivity and specificity of 97.69%, 95.02% and 98.26%, respectively. The reduction of the dimensionality of the data, from 450,000 to 13 CpGs per patient, likely also reduced the likelihood of overfitting, which is a very substantial risk in this type of modelling. All 13 CpGs individually generated classification forecasts less accurate than the proposed model. Full article
(This article belongs to the Special Issue Computational Intelligence and Machine Learning in Bioinformatics)
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30 pages, 6146 KiB  
Article
An Ensemble Classification Method for Brain Tumor Images Using Small Training Data
by Dat Tien Nguyen, Se Hyun Nam, Ganbayar Batchuluun, Muhammad Owais and Kang Ryoung Park
Mathematics 2022, 10(23), 4566; https://doi.org/10.3390/math10234566 - 02 Dec 2022
Cited by 2 | Viewed by 1678
Abstract
Computer-aided diagnosis (CAD) systems have been used to assist doctors (radiologists) in diagnosing many types of diseases, such as thyroid, brain, breast, and lung cancers. Previous studies have successfully built CAD systems using large, annotated datasets to train their models. The use of [...] Read more.
Computer-aided diagnosis (CAD) systems have been used to assist doctors (radiologists) in diagnosing many types of diseases, such as thyroid, brain, breast, and lung cancers. Previous studies have successfully built CAD systems using large, annotated datasets to train their models. The use of a large volume of training data helps these CAD systems to collect rich information for application in the diagnosis process. However, a large amount of training data is sometimes unavailable for training the models, such as for a new or less common disease and diseases that require expensive image acquisition devices. In such cases, conventional CAD systems are unable to learn their models efficiently. As a result, diagnostic performance is reduced. In this study, we focus on dealing with this problem; thus, our classification method can enhance the performance of conventional CAD systems based on the ensemble model of a support vector machine (SVM), multilayer perceptron (MLP), and few-shot (FS) learning network when working with small training datasets of brain tumor images. Through experiments, we confirmed that our proposed method outperforms conventional deep learning-based CAD systems when working with a small training dataset. In detail, we verified that the lack of training data led to the reduction of classification performance. In addition, we enhanced the classification accuracy from 3% to 10% compared to previous studies that used the SVM-based classification method or fine-tuning of a convolutional neural network (CNN) using two public datasets. Full article
(This article belongs to the Special Issue Computational Intelligence and Machine Learning in Bioinformatics)
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28 pages, 18008 KiB  
Article
End-to-End Deep Learning Architectures Using 3D Neuroimaging Biomarkers for Early Alzheimer’s Diagnosis
by Deevyankar Agarwal, Manuel Alvaro Berbis, Teodoro Martín-Noguerol, Antonio Luna, Sara Carmen Parrado Garcia and Isabel de la Torre-Díez
Mathematics 2022, 10(15), 2575; https://doi.org/10.3390/math10152575 - 25 Jul 2022
Cited by 4 | Viewed by 2309
Abstract
This study uses magnetic resonance imaging (MRI) data to propose end-to-end learning implementing volumetric convolutional neural network (CNN) models for two binary classification tasks: Alzheimer’s disease (AD) vs. cognitively normal (CN) and stable mild cognitive impairment (sMCI) vs. AD. The baseline MP-RAGE T1 [...] Read more.
This study uses magnetic resonance imaging (MRI) data to propose end-to-end learning implementing volumetric convolutional neural network (CNN) models for two binary classification tasks: Alzheimer’s disease (AD) vs. cognitively normal (CN) and stable mild cognitive impairment (sMCI) vs. AD. The baseline MP-RAGE T1 MR images of 245 AD patients and 229 with sMCI were obtained from the ADNI dataset, whereas 245 T1 MR images of CN people were obtained from the IXI dataset. All of the images were preprocessed in four steps: N4 bias field correction, denoising, brain extraction, and registration. End-to-end-learning-based deep CNNs were used to discern between different phases of AD. Eight CNN-based architectures were implemented and assessed. The DenseNet264 excelled in both types of classification, with 82.5% accuracy and 87.63% AUC for training and 81.03% accuracy for testing relating to the sMCI vs. AD and 100% accuracy and 100% AUC for training and 99.56% accuracy for testing relating to the AD vs. CN. Deep learning approaches based on CNN and end-to-end learning offer a strong tool for examining minute but complex properties in MR images which could aid in the early detection and prediction of Alzheimer’s disease in clinical settings. Full article
(This article belongs to the Special Issue Computational Intelligence and Machine Learning in Bioinformatics)
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Review

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33 pages, 906 KiB  
Review
Role of Artificial Intelligence for Analysis of COVID-19 Vaccination-Related Tweets: Opportunities, Challenges, and Future Trends
by Wajdi Aljedaani, Eysha Saad, Furqan Rustam, Isabel de la Torre Díez and Imran Ashraf
Mathematics 2022, 10(17), 3199; https://doi.org/10.3390/math10173199 - 05 Sep 2022
Cited by 8 | Viewed by 3078
Abstract
Pandemics and infectious diseases are overcome by vaccination, which serves as a preventative measure. Nevertheless, vaccines also raise public concerns; public apprehension and doubts challenge the acceptance of new vaccines. COVID-19 vaccines received a similarly hostile reaction from the public. In addition, misinformation [...] Read more.
Pandemics and infectious diseases are overcome by vaccination, which serves as a preventative measure. Nevertheless, vaccines also raise public concerns; public apprehension and doubts challenge the acceptance of new vaccines. COVID-19 vaccines received a similarly hostile reaction from the public. In addition, misinformation from social media, contradictory comments from medical experts, and reports of worse reactions led to negative COVID-19 vaccine perceptions. Many researchers analyzed people’s varying sentiments regarding the COVID-19 vaccine using artificial intelligence (AI) approaches. This study is the first attempt to review the role of AI approaches in COVID-19 vaccination-related sentiment analysis. For this purpose, insights from publications are gathered that analyze the (a) approaches used to develop sentiment analysis tools, (b) major sources of data, (c) available data sources, and (d) the public perception of COVID-19 vaccine. Analysis suggests that public perception-related COVID-19 tweets are predominantly analyzed using TextBlob. Moreover, to a large extent, researchers have employed the Latent Dirichlet Allocation model for topic modeling of Twitter data. Another pertinent discovery made in our study is the variation in people’s sentiments regarding the COVID-19 vaccine across different regions. We anticipate that our systematic review will serve as an all-in-one source for the research community in determining the right technique and data source for their requirements. Our findings also provide insight into the research community to assist them in their future work in the current domain. Full article
(This article belongs to the Special Issue Computational Intelligence and Machine Learning in Bioinformatics)
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