Special Issue "Machine Learning for Biomedical Data Analysis"

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 (15 July 2019).

Special Issue Editors

Prof. Dr. Federico Divina
Website1 Website2
Guest Editor
School of Engineerings, Universidad Pablo de Olavide, Sevilla, Spain
Interests: data mining, big data, artificial intelligence, soft computing, bioinformatics
Special Issues and Collections in MDPI journals

Special Issue Information

Dear Colleagues,

Nowadays, more and more data is being produced. This data comes from everywhere: sensors used to gather climate information, posts to social media sites, digital pictures and videos, purchase transaction records, and cell phone GPS signals, to name a few. This data is big data . This tendency also affects biology and medicine, where new techniques, e.g., next generation sequencing, allow us to produce more data than ever. Moreover, the data generated may be of a different nature: text, images, gene expression, signals, etc. In addition to this, typically such data present a high presence of noise. It follows that there is a clear need to analyze and extract useful information from such data. In this context, machine learning (ML) techniques provide the ability to analyse this data and extract relevant information from it, or even make predictions about it.

The overall aim of this Special Issue is to compile the latest research and development, up-to-date issues, and challenges in the field of ML and its applications in bioinformatics and medical applications.

Possible topics of interest include, but are not limited to:

  • Medical imaging, signal processing and text analysis
  • Data mining medical data and records
  • Clinical expert systems
  • Modelling and simulation of biomedical processes
  • Drug description analysis
  • Patient-centric care
  • Medical prognosis based on machine learning approaches
  • Interpreting genomic or metagenomic data
  • Discovering regulatory or expression pathways
  • Rational drug design and personalized medicine
  • Modeling ecosystems or population dynamics
  • Discovering genome–disease or genome–phenotype associations
  • Biomedical text/data mining and visualization
  • Network biology/medicine
  • Omics data analysis and functional genomics for complex diseases
  • Gene–gene interactions and gene–environment interactions for disease association analysis
  • Protein structure prediction
  • Assembling next generation sequence data

 

Submitted papers should not have been previously published nor be currently under consideration for publication elsewhere.

Dr. Federico Divina
Dr. Francisco A. Gómez-Vela
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. 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 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 (9 papers)

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Editorial

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Open AccessEditorial
Special Issue on Machine Learning for Biomedical Data Analysis
Appl. Sci. 2019, 9(21), 4676; https://doi.org/10.3390/app9214676 - 02 Nov 2019
Abstract
In our world, increasing amounts of data are produced everyday [...] Full article
(This article belongs to the Special Issue Machine Learning for Biomedical Data Analysis)

Research

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Open AccessArticle
Investigating the Behaviour of Machine Learning Techniques to Segment Brain Metastases in Radiation Therapy Planning
Appl. Sci. 2019, 9(16), 3335; https://doi.org/10.3390/app9163335 - 14 Aug 2019
Cited by 2
Abstract
This work aimed to investigate whether automated classifiers belonging to feature-based and deep learning may approach brain metastases segmentation successfully. Support Vector Machine and V-Net Convolutional Neural Network are selected as representatives of the two approaches. In the experiments, we consider several configurations [...] Read more.
This work aimed to investigate whether automated classifiers belonging to feature-based and deep learning may approach brain metastases segmentation successfully. Support Vector Machine and V-Net Convolutional Neural Network are selected as representatives of the two approaches. In the experiments, we consider several configurations of the two methods to segment brain metastases on contrast-enhanced T1-weighted magnetic resonance images. Performances were evaluated and compared under critical conditions imposed by the clinical radiotherapy domain, using in-house dataset and public dataset created for the Multimodal Brain Tumour Image Segmentation (BraTS) challenge. Our results showed that the feature-based and the deep network approaches are promising for the segmentation of Magnetic Resonance Imaging (MRI) brain metastases achieving both an acceptable level of performance. Experimental results also highlight different behaviour between the two methods. Support vector machine (SVM) improves performance with a smaller training set, but it is unable to manage a high level of heterogeneity in the data and requires post-processing refinement stages. The V-Net model shows good performances when trained on multiple heterogeneous cases but requires data augmentations and transfer learning procedures to optimise its behaviour. The paper illustrates a software package implementing an integrated set of procedures for active support in segmenting brain metastases within the radiotherapy workflow. Full article
(This article belongs to the Special Issue Machine Learning for Biomedical Data Analysis)
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Open AccessArticle
Special Issue on Using Machine Learning Algorithms in the Prediction of Kyphosis Disease: A Comparative Study
Appl. Sci. 2019, 9(16), 3322; https://doi.org/10.3390/app9163322 - 13 Aug 2019
Cited by 2
Abstract
Machine learning (ML) is the technology that allows a computer system to learn from the environment, through re-iterative processes, and improve itself from experience. Recently, machine learning has gained massive attention across numerous fields, and is making it easy to model data extremely [...] Read more.
Machine learning (ML) is the technology that allows a computer system to learn from the environment, through re-iterative processes, and improve itself from experience. Recently, machine learning has gained massive attention across numerous fields, and is making it easy to model data extremely well, without the importance of using strong assumptions about the modeled system. The rise of machine learning has proven to better describe data as a result of providing both engineering solutions and an important benchmark. Therefore, in this current research work, we applied three different machine learning algorithms, which were, the Random Forest (RF), Support Vector Machines (SVM), and Artificial Neural Network (ANN) to predict kyphosis disease based on a biomedical data. At the initial stage of the experiments, we performed 5- and 10-Fold Cross-Validation using Logistic Regression as a baseline model to compare with our ML models without performing grid search. We then evaluated the models and compared their performances based on 5- and 10-Fold Cross-Validation after running grid search algorithms on the ML models. Among the Support Vector Machines, we experimented with the three kernels (Linear, Radial Basis Function (RBF), Polynomial). We observed overall accuracies of the models between 79%–85%, and 77%–86% based on the 5- and 10-Fold Cross-Validation, after running grid search respectively. Based on the 5- and 10-Fold Cross-Validation as evaluation metrics, the RF, SVM-RBF, and ANN models achieved accuracies more than 80%. The RF, SVM-RBF and ANN models outperformed the baseline model based on the 10-Fold Cross-Validation with grid search. Overall, in terms of accuracies, the ANN model outperformed all the other ML models, achieving 85.19% and 86.42% based on the 5- and 10-Fold Cross-Validation. We proposed that RF, SVM-RBF and ANN models should be used to detect and predict kyphosis disease after a patient had undergone surgery or operation. We suggest that machine learning should be adopted and used as an essential and critical tool across the maximum spectrum of answering biomedical questions. Full article
(This article belongs to the Special Issue Machine Learning for Biomedical Data Analysis)
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Open AccessArticle
Medinoid: Computer-Aided Diagnosis and Localization of Glaucoma Using Deep Learning
Appl. Sci. 2019, 9(15), 3064; https://doi.org/10.3390/app9153064 - 29 Jul 2019
Cited by 1
Abstract
Glaucoma is a leading eye disease, causing vision loss by gradually affecting peripheral vision if left untreated. Current diagnosis of glaucoma is performed by ophthalmologists, human experts who typically need to analyze different types of medical images generated by different types of medical [...] Read more.
Glaucoma is a leading eye disease, causing vision loss by gradually affecting peripheral vision if left untreated. Current diagnosis of glaucoma is performed by ophthalmologists, human experts who typically need to analyze different types of medical images generated by different types of medical equipment: fundus, Retinal Nerve Fiber Layer (RNFL), Optical Coherence Tomography (OCT) disc, OCT macula, perimetry, and/or perimetry deviation. Capturing and analyzing these medical images is labor intensive and time consuming. In this paper, we present a novel approach for glaucoma diagnosis and localization, only relying on fundus images that are analyzed by making use of state-of-the-art deep learning techniques. Specifically, our approach towards glaucoma diagnosis and localization leverages Convolutional Neural Networks (CNNs) and Gradient-weighted Class Activation Mapping (Grad-CAM), respectively. We built and evaluated different predictive models using a large set of fundus images, collected and labeled by ophthalmologists at Samsung Medical Center (SMC). Our experimental results demonstrate that our most effective predictive model is able to achieve a high diagnosis accuracy of 96%, as well as a high sensitivity of 96% and a high specificity of 100% for Dataset-Optic Disc (OD), a set of center-cropped fundus images highlighting the optic disc. Furthermore, we present Medinoid, a publicly-available prototype web application for computer-aided diagnosis and localization of glaucoma, integrating our most effective predictive model in its back-end. Full article
(This article belongs to the Special Issue Machine Learning for Biomedical Data Analysis)
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Open AccessArticle
Deep Convolutional Neural Network Model for Automated Diagnosis of Schizophrenia Using EEG Signals
Appl. Sci. 2019, 9(14), 2870; https://doi.org/10.3390/app9142870 - 18 Jul 2019
Cited by 27
Abstract
A computerized detection system for the diagnosis of Schizophrenia (SZ) using a convolutional neural system is described in this study. Schizophrenia is an anomaly in the brain characterized by behavioral symptoms such as hallucinations and disorganized speech. Electroencephalograms (EEG) indicate brain disorders and [...] Read more.
A computerized detection system for the diagnosis of Schizophrenia (SZ) using a convolutional neural system is described in this study. Schizophrenia is an anomaly in the brain characterized by behavioral symptoms such as hallucinations and disorganized speech. Electroencephalograms (EEG) indicate brain disorders and are prominently used to study brain diseases. We collected EEG signals from 14 healthy subjects and 14 SZ patients and developed an eleven-layered convolutional neural network (CNN) model to analyze the signals. Conventional machine learning techniques are often laborious and subject to intra-observer variability. Deep learning algorithms that have the ability to automatically extract significant features and classify them are thus employed in this study. Features are extracted automatically at the convolution stage, with the most significant features extracted at the max-pooling stage, and the fully connected layer is utilized to classify the signals. The proposed model generated classification accuracies of 98.07% and 81.26% for non-subject based testing and subject based testing, respectively. The developed model can likely aid clinicians as a diagnostic tool to detect early stages of SZ. Full article
(This article belongs to the Special Issue Machine Learning for Biomedical Data Analysis)
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Open AccessArticle
A Method to Detect Type 1 Diabetes Based on Physical Activity Measurements Using a Mobile Device
Appl. Sci. 2019, 9(12), 2555; https://doi.org/10.3390/app9122555 - 22 Jun 2019
Cited by 2
Abstract
Type 1 diabetes is a chronic disease marked by high blood glucose levels, called hyperglycemia. Diagnosis of diabetes typically requires one or more blood tests. The aim of this paper is to discuss a non-invasive method of type 1 diabetes detection, based on [...] Read more.
Type 1 diabetes is a chronic disease marked by high blood glucose levels, called hyperglycemia. Diagnosis of diabetes typically requires one or more blood tests. The aim of this paper is to discuss a non-invasive method of type 1 diabetes detection, based on physical activity measurement. We solved a binary classification problem using a variety of computational intelligence methods, including non-linear classification algorithms, which were applied and comparatively assessed. Prediction of disease presence among children and adolescents was evaluated using performance measures, such as accuracy, sensitivity, specificity, precision, the goodness index, and AUC. The most satisfying results were obtained when using the random forest method. The primary parameters in disease detection were weekly step count and the weekly number of vigorous activity minutes. The dependance between the weekly number of steps and the type 1 diabetes presence was established after an insightful analysis of data using classification and clustering algorithms. The findings have shown promising results that type 1 diabetes can be diagnosed using physical activity measurement. This is essential regarding the non-invasiveness and flexibility of the detection method, which can be tested at any time anywhere. The proposed technique can be implemented on a mobile device. Full article
(This article belongs to the Special Issue Machine Learning for Biomedical Data Analysis)
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Open AccessArticle
DC2Anet: Generating Lumbar Spine MR Images from CT Scan Data Based on Semi-Supervised Learning
Appl. Sci. 2019, 9(12), 2521; https://doi.org/10.3390/app9122521 - 20 Jun 2019
Cited by 1
Abstract
Magnetic resonance imaging (MRI) plays a significant role in the diagnosis of lumbar disc disease. However, the use of MRI is limited because of its high cost and significant operating and processing time. More importantly, MRI is contraindicated for some patients with claustrophobia [...] Read more.
Magnetic resonance imaging (MRI) plays a significant role in the diagnosis of lumbar disc disease. However, the use of MRI is limited because of its high cost and significant operating and processing time. More importantly, MRI is contraindicated for some patients with claustrophobia or cardiac pacemakers due to the possibility of injury. In contrast, computed tomography (CT) scans are much less expensive, are faster, and do not face the same limitations. In this paper, we propose a method for estimating lumbar spine MR images based on CT images using a novel objective function and a dual cycle-consistent adversarial network (DC 2 Anet) with semi-supervised learning. The objective function includes six independent loss terms to balance quantitative and qualitative losses, enabling the generation of a realistic and accurate synthetic MR image. DC 2 Anet is also capable of semi-supervised learning, and the network is general enough for supervised or unsupervised setups. Experimental results prove that the method is accurate, being able to construct MR images that closely approximate reference MR images, while also outperforming four other state-of-the-art methods. Full article
(This article belongs to the Special Issue Machine Learning for Biomedical Data Analysis)
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Open AccessArticle
A Sample-Encoding Generalization of the Kohonen Associative Memory and Application to Knee Kinematic Data Representation and Pathology Classification
Appl. Sci. 2019, 9(9), 1741; https://doi.org/10.3390/app9091741 - 26 Apr 2019
Cited by 3
Abstract
Knee kinematic data consist of a small sample of high-dimensional vectors recording repeated measurements of the temporal variation of each of the three fundamental angles of knee three-dimensional rotation during a walking cycle. In applications such as knee pathology classification, the notorious problems [...] Read more.
Knee kinematic data consist of a small sample of high-dimensional vectors recording repeated measurements of the temporal variation of each of the three fundamental angles of knee three-dimensional rotation during a walking cycle. In applications such as knee pathology classification, the notorious problems of high-dimensionality (the curse of dimensionality), high intra-class variability, and inter-class similarity make this data generally difficult to interpret. In the face of these difficulties, the purpose of this study is to investigate knee kinematic data classification by a Kohonen neural network generalized to encode samples of multidimensional data vectors rather than single such vectors as in the standard network. The network training algorithm and its ensuing classification function both use the Hotelling T 2 statistic to evaluate the underlying sample similarity, thus affording efficient use of training data for network development and robust classification of observed data. Applied to knee osteoarthritis pathology discrimination, namely the femoro-rotulian (FR) and femoro-tibial (FT) categories, the scheme improves on the state-of-the-art methods. Full article
(This article belongs to the Special Issue Machine Learning for Biomedical Data Analysis)
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Open AccessArticle
Deep Learning in the Biomedical Applications: Recent and Future Status
Appl. Sci. 2019, 9(8), 1526; https://doi.org/10.3390/app9081526 - 12 Apr 2019
Cited by 16
Abstract
Deep neural networks represent, nowadays, the most effective machine learning technology in biomedical domain. In this domain, the different areas of interest concern the Omics (study of the genome—genomics—and proteins—transcriptomics, proteomics, and metabolomics), bioimaging (study of biological cell and tissue), medical imaging (study [...] Read more.
Deep neural networks represent, nowadays, the most effective machine learning technology in biomedical domain. In this domain, the different areas of interest concern the Omics (study of the genome—genomics—and proteins—transcriptomics, proteomics, and metabolomics), bioimaging (study of biological cell and tissue), medical imaging (study of the human organs by creating visual representations), BBMI (study of the brain and body machine interface) and public and medical health management (PmHM). This paper reviews the major deep learning concepts pertinent to such biomedical applications. Concise overviews are provided for the Omics and the BBMI. We end our analysis with a critical discussion, interpretation and relevant open challenges. Full article
(This article belongs to the Special Issue Machine Learning for Biomedical Data Analysis)
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