Special Issue "Bioinformatics Applications Based On Machine Learning"

A special issue of Processes (ISSN 2227-9717). This special issue belongs to the section "Biological Systems".

Deadline for manuscript submissions: 15 December 2020.

Special Issue Editors

Dr. Pablo Chamoso
Website
Guest Editor
1. Department of Computing and Automation, Faculty of Sciences, University of Salamanca. Calle Espejo sn (Edificio Multiusos I+D+i), 37007, Salamanca, Spain.
2. BISITE Research Group, University of Salamanca. Calle Espejo sn (Edificio Multiusos I+D+i), 37007, Salamanca, Spain.
3. IoT Digital Innovation Hub, Edificio Parque Científico Módulo 305, Paseo de Belén 11 Campus Miguel Delibes 47011 Valladolid (Spain).
Interests: Machine Learning, Internet of Things, Distributed Systems, Software Applications
Special Issues and Collections in MDPI journals
Prof. Dr. Sara Rodríguez González

Guest Editor
Professor of Computer Science, University of Salamanca
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Prof. Dr. Mohd Saberi Mohamad
Website
Guest Editor
Institute For Artificial Intelligence and Big Data, Universiti Malaysia Kelantan, Malaysia
Interests: Artificial intelligence and intelligent systems, Bioinformatics and computational biology
Dr. Alfonso González-Briones
Website
Guest Editor
Department of Computer Science and Automation Control, Universidad de Salamanca, Calle Espejo s/n, 37007 Salamanca, Spain
Interests: artificial intelligence; machine learning; IoT; smart cities; blockchain
Special Issues and Collections in MDPI journals

Special Issue Information

Dear Colleagues,

Research in the area of bioinformatics has always been one of the most active lines of research in the scientific community. However, it has gained even more interest thanks to the increased processing capacities of computers, which allow processing large volumes of data and analyzing them with techniques such as machine learning.

Thanks to these advances, new applications appear in the area of bioinformatics.  In them, the results obtained generally improve those of previous applications that do not use these computation techniques.

In this Special Issue, we seek research and case studies that demonstrate the application of machine learning to support applied scientific research, in any area of bioinformatics. Example topics include (but are not limited to) the following topics applied to bioinformatics:

- New machine learning algorithms
- Distributed machine learning systems
- New applications on bioinformatics
- Health-care applications
- Bio imaging
- Next generation sequencing
- Data and software integration
- Visualization of biological systems and networks
- High-throughput data analysis (transcriptomics, proteomics, etc)
- Comparison and alignment methods

Dr. Pablo Chamoso
Dr. Sara Rodríguez González
Prof. Dr. Mohd Saberi Mohamad
Dr. Alfonso González Briones
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 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. Processes 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 1400 CHF (Swiss Francs). Please note that for papers submitted after 30 June 2020 an APC of 1500 CHF applies. 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 applications
  • machine learning
  • artificial intelligence

Published Papers (6 papers)

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Research

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Open AccessFeature PaperArticle
An Adjective Selection Personality Assessment Method Using Gradient Boosting Machine Learning
Processes 2020, 8(5), 618; https://doi.org/10.3390/pr8050618 - 21 May 2020
Abstract
Goldberg’s 100 Unipolar Markers remains one of the most popular ways to measure personality traits, in particular, the Big Five. An important reduction was later preformed by Saucier, using a sub-set of 40 markers. Both assessments are performed by presenting a set of [...] Read more.
Goldberg’s 100 Unipolar Markers remains one of the most popular ways to measure personality traits, in particular, the Big Five. An important reduction was later preformed by Saucier, using a sub-set of 40 markers. Both assessments are performed by presenting a set of markers, or adjectives, to the subject, requesting him to quantify each marker using a 9-point rating scale. Consequently, the goal of this study is to conduct experiments and propose a shorter alternative where the subject is only required to identify which adjectives describe him the most. Hence, a web platform was developed for data collection, requesting subjects to rate each adjective and select those describing him the most. Based on a Gradient Boosting approach, two distinct Machine Learning architectures were conceived, tuned and evaluated. The first makes use of regressors to provide an exact score of the Big Five while the second uses classifiers to provide a binned output. As input, both receive the one-hot encoded selection of adjectives. Both architectures performed well. The first is able to quantify the Big Five with an approximate error of 5 units of measure, while the second shows a micro-averaged f1-score of 83%. Since all adjectives are used to compute all traits, models are able to harness inter-trait relationships, being possible to further reduce the set of adjectives by removing those that have smaller importance. Full article
(This article belongs to the Special Issue Bioinformatics Applications Based On Machine Learning)
Open AccessFeature PaperArticle
Bioinspired Hybrid Model to Predict the Hydrogen Inlet Fuel Cell Flow Change of an Energy Storage System
Processes 2019, 7(11), 825; https://doi.org/10.3390/pr7110825 - 07 Nov 2019
Abstract
The present research work deals with prediction of hydrogen consumption of a fuel cell in an energy storage system. Due to the fact that these kind of systems have a very nonlinear behaviour, the use of traditional techniques based on parametric models and [...] Read more.
The present research work deals with prediction of hydrogen consumption of a fuel cell in an energy storage system. Due to the fact that these kind of systems have a very nonlinear behaviour, the use of traditional techniques based on parametric models and other more sophisticated techniques such as soft computing methods, seems not to be accurate enough to generate good models of the system under study. Due to that, a hybrid intelligent system, based on clustering and regression techniques, has been developed and implemented to predict the necessary variation of the hydrogen flow consumption to satisfy the variation of demanded power to the fuel cell. In this research, a hybrid intelligent model was created and validated over a dataset from a fuel cell energy storage system. Obtained results validate the proposal, achieving better performance than other well-known classical regression methods, allowing us to predict the hydrogen consumption with a Mean Absolute Error (MAE) of 3.73 with the validation dataset. Full article
(This article belongs to the Special Issue Bioinformatics Applications Based On Machine Learning)
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Open AccessFeature PaperArticle
Ear Detection and Localization with Convolutional Neural Networks in Natural Images and Videos
Processes 2019, 7(7), 457; https://doi.org/10.3390/pr7070457 - 17 Jul 2019
Cited by 2
Abstract
The difficulty in precisely detecting and locating an ear within an image is the first step to tackle in an ear-based biometric recognition system, a challenge which increases in difficulty when working with variable photographic conditions. This is in part due to the [...] Read more.
The difficulty in precisely detecting and locating an ear within an image is the first step to tackle in an ear-based biometric recognition system, a challenge which increases in difficulty when working with variable photographic conditions. This is in part due to the irregular shapes of human ears, but also because of variable lighting conditions and the ever changing profile shape of an ear’s projection when photographed. An ear detection system involving multiple convolutional neural networks and a detection grouping algorithm is proposed to identify the presence and location of an ear in a given input image. The proposed method matches the performance of other methods when analyzed against clean and purpose-shot photographs, reaching an accuracy of upwards of 98%, but clearly outperforms them with a rate of over 86% when the system is subjected to non-cooperative natural images where the subject appears in challenging orientations and photographic conditions. Full article
(This article belongs to the Special Issue Bioinformatics Applications Based On Machine Learning)
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Open AccessArticle
An Accurate Clinical Implication Assessment for Diabetes Mellitus Prevalence Based on a Study from Nigeria
Processes 2019, 7(5), 289; https://doi.org/10.3390/pr7050289 - 15 May 2019
Cited by 3
Abstract
The increasing rate of diabetes is found across the planet. Therefore, the diagnosis of pre-diabetes and diabetes is important in populations with extreme diabetes risk. In this study, a machine learning technique was implemented over a data mining platform by employing Rule classifiers [...] Read more.
The increasing rate of diabetes is found across the planet. Therefore, the diagnosis of pre-diabetes and diabetes is important in populations with extreme diabetes risk. In this study, a machine learning technique was implemented over a data mining platform by employing Rule classifiers (PART and Decision table) to measure the accuracy and logistic regression on the classification results for forecasting the prevalence in diabetes mellitus patients suffering simultaneously from other chronic disease symptoms. The real-life data was collected in Nigeria between December 2017 and February 2019 by applying ten non-intrusive and easily available clinical variables. The results disclosed that the Rule classifiers achieved a mean accuracy of 98.75%. The error rate, precision, recall, F-measure, and Matthew’s correlation coefficient MCC were 0.02%, 0.98%, 0.98%, 0.98%, and 0.97%, respectively. The forecast decision, achieved by employing a set of 23 decision rules (DR), indicates that age, gender, glucose level, and body mass are fundamental reasons for diabetes, followed by work stress, diet, family diabetes history, physical exercise, and cardiovascular stroke history. The study validated that the proposed set of DR is practical for quick screening of diabetes mellitus patients at the initial stage without intrusive medical tests and was found to be effective in the initial diagnosis of diabetes. Full article
(This article belongs to the Special Issue Bioinformatics Applications Based On Machine Learning)
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Open AccessArticle
A Machine Learning-based Pipeline for the Classification of CTX-M in Metagenomics Samples
Processes 2019, 7(4), 235; https://doi.org/10.3390/pr7040235 - 24 Apr 2019
Cited by 2
Abstract
Bacterial infections are a major global concern, since they can lead to public health problems. To address this issue, bioinformatics contributes extensively with the analysis and interpretation of in silico data by enabling to genetically characterize different individuals/strains, such as in bacteria. However, [...] Read more.
Bacterial infections are a major global concern, since they can lead to public health problems. To address this issue, bioinformatics contributes extensively with the analysis and interpretation of in silico data by enabling to genetically characterize different individuals/strains, such as in bacteria. However, the growing volume of metagenomic data requires new infrastructure, technologies, and methodologies that support the analysis and prediction of this information from a clinical point of view, as intended in this work. On the other hand, distributed computational environments allow the management of these large volumes of data, due to significant advances in processing architectures, such as multicore CPU (Central Process Unit) and GPGPU (General Propose Graphics Process Unit). For this purpose, we developed a bioinformatics workflow based on filtered metagenomic data with Duk tool. Data formatting was done through Emboss software and a prototype of a workflow. A pipeline was also designed and implemented in bash script based on machine learning. Further, Python 3 programming language was used to normalize the training data of the artificial neural network, which was implemented in the TensorFlow framework, and its behavior was visualized in TensorBoard. Finally, the values from the initial bioinformatics process and the data generated during the parameterization and optimization of the Artificial Neural Network are presented and validated based on the most optimal result for the identification of the CTX-M gene group. Full article
(This article belongs to the Special Issue Bioinformatics Applications Based On Machine Learning)
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Review

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Open AccessFeature PaperReview
A Review of Computational Methods for Clustering Genes with Similar Biological Functions
Processes 2019, 7(9), 550; https://doi.org/10.3390/pr7090550 - 21 Aug 2019
Abstract
Clustering techniques can group genes based on similarity in biological functions. However, the drawback of using clustering techniques is the inability to identify an optimal number of potential clusters beforehand. Several existing optimization techniques can address the issue. Besides, clustering validation can predict [...] Read more.
Clustering techniques can group genes based on similarity in biological functions. However, the drawback of using clustering techniques is the inability to identify an optimal number of potential clusters beforehand. Several existing optimization techniques can address the issue. Besides, clustering validation can predict the possible number of potential clusters and hence increase the chances of identifying biologically informative genes. This paper reviews and provides examples of existing methods for clustering genes, optimization of the objective function, and clustering validation. Clustering techniques can be categorized into partitioning, hierarchical, grid-based, and density-based techniques. We also highlight the advantages and the disadvantages of each category. To optimize the objective function, here we introduce the swarm intelligence technique and compare the performances of other methods. Moreover, we discuss the differences of measurements between internal and external criteria to validate a cluster quality. We also investigate the performance of several clustering techniques by applying them on a leukemia dataset. The results show that grid-based clustering techniques provide better classification accuracy; however, partitioning clustering techniques are superior in identifying prognostic markers of leukemia. Therefore, this review suggests combining clustering techniques such as CLIQUE and k-means to yield high-quality gene clusters. Full article
(This article belongs to the Special Issue Bioinformatics Applications Based On Machine Learning)
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