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Entropy
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Machine Learning in Biomedical Data Analysis
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Machine Learning in Biomedical Data Analysis

A special issue of Entropy (ISSN 1099-4300). This special issue belongs to the section "Multidisciplinary Applications".

Deadline for manuscript submissions: closed (31 July 2023) | Viewed by 11415

Special Issue Editors

Prof. Dr. Xiaobo Li

E-Mail Website
Guest Editor
College of Mathematics and Computer Science, Zhejiang Normal University, Jinhua 321004, China
Interests: machine learning; data mining; bioinformatics; computational intelligence; tumor pathology
Prof. Dr. Dexing Kong

E-Mail Website
Guest Editor
1. School of Mathematical Sciences, Zhejiang University, Hangzhou 310027, China
2. College of Mathematical Medicine, Zhejiang Normal University, Jinhua 321004, China
Interests: medical imaging analysis; deep learning; mathematical medicine; mathematical physics; partial differential equations
Prof. Dr. Changjun Zhou

E-Mail Website
Guest Editor
College of Mathematics and Computer Science, Zhejiang Normal University, Jinhua 321004, China
Interests: intelligent computing; pattern recognition; image processing; machine learning; biological computing

Special Issue Information

Dear Colleagues,

With the rapid development of biomedical data acquisition technology, the accumulation of research data has exploded exponentially, and researchers can use more ways to conduct biomedical data analysis and research more comprehensively. Because of the complex characteristics such as high dimensionality, nonlinearity, high noise, and diversity of biomedical data, which requires higher data calculation and analysis ability of the model, the traditional biomedical analysis methods may be less efficient in dealing with the massive growth of biomedical data.

In recent years, due to its strong inherent ability to extract information from complex systems, machine learning methods, including deep learning, have made a breakthrough in biomedical research fields such as medical image analysis, genomics data analysis and protein structure prediction, etc. In this context, information theory has been widely accepted and applied in the domain of machine learning. For example, the information entropy is used to construct the decision tree, the cross entropy is employed as the loss function in BP neural network, and so on. The current machine learning algorithms are still rapidly evolving and developing, in this regard, to benefit from these advances, we shall need to explore deeply the theory of information and expand extremely the applications of information theory in the field of machine learning. This Special Issue will collect new ideas and introduce promising methods arising from the application of information theory on machine learning in the domain of biomedical data analysis.

This Special Issue will accept unpublished original papers and comprehensive reviews focused on (but not restricted to) the following research areas: deep learning models applied on biomedical data; analysis of medical image data; high throughput sequencing data analysis; multi-omics data analysis; complex biological network; machine learning in precision medicine; medical data processing; drug design and discovery; and computational intelligence in machine learning methods applied on biomedical data.

Prof. Dr. Xiaobo Li
Prof. Dr. Dexing Kong
Prof. Dr. Changjun Zhou
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. Entropy 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 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

  • Machine learning
  • Deep learning
  • Biomedical data analysis
  • Medical image processing
  • High throughput sequencing
  • Microarray data analysis
  • Complex biological network
  • Precision medicine
  • Computational intelligence

Published Papers (5 papers)

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Research

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21 pages, 2090 KiB  
Article
Multi-Task Time Series Forecasting Based on Graph Neural Networks
by Xiao Han, Yongjie Huang, Zhisong Pan, Wei Li, Yahao Hu and Gengyou Lin
Entropy 2023, 25(8), 1136; https://0-doi-org.brum.beds.ac.uk/10.3390/e25081136 - 28 Jul 2023
Viewed by 2009
Abstract
Accurate time series forecasting is of great importance in real-world scenarios such as health care, transportation, and finance. Because of the tendency, temporal variations, and periodicity of the time series data, there are complex and dynamic dependencies among its underlying features. In time [...] Read more.
Accurate time series forecasting is of great importance in real-world scenarios such as health care, transportation, and finance. Because of the tendency, temporal variations, and periodicity of the time series data, there are complex and dynamic dependencies among its underlying features. In time series forecasting tasks, the features learned by a specific task at the current time step (such as predicting mortality) are related to the features of historical timesteps and the features of adjacent timesteps of related tasks (such as predicting fever). Therefore, capturing dynamic dependencies in data is a challenging problem for learning accurate future prediction behavior. To address this challenge, we propose a cross-timestep feature-sharing multi-task time series forecasting model that can capture global and local dynamic dependencies in time series data. Initially, the global dynamic dependencies of features within each task are captured through a self-attention mechanism. Furthermore, an adaptive sparse graph structure is employed to capture the local dynamic dependencies inherent in the data, which can explicitly depict the correlation between features across timesteps and tasks. Lastly, the cross-timestep feature sharing between tasks is achieved through a graph attention mechanism, which strengthens the learning of shared features that are strongly correlated with a single task. It is beneficial for improving the generalization performance of the model. Our experimental results demonstrate that our method is significantly competitive compared to baseline methods. Full article
(This article belongs to the Special Issue Machine Learning in Biomedical Data Analysis)
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15 pages, 3743 KiB  
Article
Colorectal Cancer Survival Prediction Using Deep Distribution Based Multiple-Instance Learning
by Xingyu Li, Jitendra Jonnagaddala, Min Cen, Hong Zhang and Steven Xu
Entropy 2022, 24(11), 1669; https://0-doi-org.brum.beds.ac.uk/10.3390/e24111669 - 15 Nov 2022
Cited by 4 | Viewed by 1748
Abstract
Most deep-learning algorithms that use Hematoxylin- and Eosin-stained whole slide images (WSIs) to predict cancer survival incorporate image patches either with the highest scores or a combination of both the highest and lowest scores. In this study, we hypothesize that incorporating wholistic patch [...] Read more.
Most deep-learning algorithms that use Hematoxylin- and Eosin-stained whole slide images (WSIs) to predict cancer survival incorporate image patches either with the highest scores or a combination of both the highest and lowest scores. In this study, we hypothesize that incorporating wholistic patch information can predict colorectal cancer (CRC) cancer survival more accurately. As such, we developed a distribution-based multiple-instance survival learning algorithm (DeepDisMISL) to validate this hypothesis on two large international CRC WSIs datasets called MCO CRC and TCGA COAD-READ. Our results suggest that combining patches that are scored based on percentile distributions together with the patches that are scored as highest and lowest drastically improves the performance of CRC survival prediction. Including multiple neighborhood instances around each selected distribution location (e.g., percentiles) could further improve the prediction. DeepDisMISL demonstrated superior predictive ability compared to other recently published, state-of-the-art algorithms. Furthermore, DeepDisMISL is interpretable and can assist clinicians in understanding the relationship between cancer morphological phenotypes and a patient’s cancer survival risk. Full article
(This article belongs to the Special Issue Machine Learning in Biomedical Data Analysis)
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18 pages, 6415 KiB  
Article
A Spiking Neural Network Based on Retinal Ganglion Cells for Automatic Burn Image Segmentation
by Jiakai Liang, Ruixue Li, Chao Wang, Rulin Zhang, Keqiang Yue, Wenjun Li and Yilin Li
Entropy 2022, 24(11), 1526; https://0-doi-org.brum.beds.ac.uk/10.3390/e24111526 - 25 Oct 2022
Cited by 2 | Viewed by 1408
Abstract
Burn is a common traumatic disease. After severe burn injury, the human body will increase catabolism, and burn wounds lead to a large amount of body fluid loss, with a high mortality rate. Therefore, in the early treatment for burn patients, it is [...] Read more.
Burn is a common traumatic disease. After severe burn injury, the human body will increase catabolism, and burn wounds lead to a large amount of body fluid loss, with a high mortality rate. Therefore, in the early treatment for burn patients, it is essential to calculate the patient’s water requirement based on the percentage of the burn wound area in the total body surface area (TBSA%). However, burn wounds are so complex that there is observer variability by the clinicians, making it challenging to locate the burn wounds accurately. Therefore, an objective, accurate location method of burn wounds is very necessary and meaningful. Convolutional neural networks (CNNs) provide feasible means for this requirement. However, although the CNNs continue to improve the accuracy in the semantic segmentation task, they are often limited by the computing resources of edge hardware. For this purpose, a lightweight burn wounds segmentation model is required. In our work, we constructed a burn image dataset and proposed a U-type spiking neural networks (SNNs) based on retinal ganglion cells (RGC) for segmenting burn and non-burn areas. Moreover, a module with cross-layer skip concatenation structure was introduced. Experimental results showed that the pixel accuracy of the proposed reached 92.89%, and our network parameter only needed 16.6 Mbytes. The results showed our model achieved remarkable accuracy while achieving edge hardware affinity. Full article
(This article belongs to the Special Issue Machine Learning in Biomedical Data Analysis)
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20 pages, 3519 KiB  
Article
A Pragmatic Ensemble Strategy for Missing Values Imputation in Health Records
by Shivani Batra, Rohan Khurana, Mohammad Zubair Khan, Wadii Boulila, Anis Koubaa and Prakash Srivastava
Entropy 2022, 24(4), 533; https://0-doi-org.brum.beds.ac.uk/10.3390/e24040533 - 10 Apr 2022
Cited by 21 | Viewed by 2330
Abstract
Pristine and trustworthy data are required for efficient computer modelling for medical decision-making, yet data in medical care is frequently missing. As a result, missing values may occur not just in training data but also in testing data that might contain a single [...] Read more.
Pristine and trustworthy data are required for efficient computer modelling for medical decision-making, yet data in medical care is frequently missing. As a result, missing values may occur not just in training data but also in testing data that might contain a single undiagnosed episode or a participant. This study evaluates different imputation and regression procedures identified based on regressor performance and computational expense to fix the issues of missing values in both training and testing datasets. In the context of healthcare, several procedures are introduced for dealing with missing values. However, there is still a discussion concerning which imputation strategies are better in specific cases. This research proposes an ensemble imputation model that is educated to use a combination of simple mean imputation, k-nearest neighbour imputation, and iterative imputation methods, and then leverages them in a manner where the ideal imputation strategy is opted among them based on attribute correlations on missing value features. We introduce a unique Ensemble Strategy for Missing Value to analyse healthcare data with considerable missing values to identify unbiased and accurate prediction statistical modelling. The performance metrics have been generated using the eXtreme gradient boosting regressor, random forest regressor, and support vector regressor. The current study uses real-world healthcare data to conduct experiments and simulations of data with varying feature-wise missing frequencies indicating that the proposed technique surpasses standard missing value imputation approaches as well as the approach of dropping records holding missing values in terms of accuracy. Full article
(This article belongs to the Special Issue Machine Learning in Biomedical Data Analysis)
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Review

Jump to: Research

25 pages, 2009 KiB  
Review
Techniques and Algorithms for Hepatic Vessel Skeletonization in Medical Images: A Survey
by Jianfeng Zhang, Fa Wu, Wanru Chang and Dexing Kong
Entropy 2022, 24(4), 465; https://0-doi-org.brum.beds.ac.uk/10.3390/e24040465 - 28 Mar 2022
Cited by 5 | Viewed by 2571
Abstract
Hepatic vessel skeletonization serves as an important means of hepatic vascular analysis and vessel segmentation. This paper presents a survey of techniques and algorithms for hepatic vessel skeletonization in medical images. We summarized the latest developments and classical approaches in this field. These [...] Read more.
Hepatic vessel skeletonization serves as an important means of hepatic vascular analysis and vessel segmentation. This paper presents a survey of techniques and algorithms for hepatic vessel skeletonization in medical images. We summarized the latest developments and classical approaches in this field. These methods are classified into five categories according to their methodological characteristics. The overview and brief assessment of each category are provided in the corresponding chapters, respectively. We provide a comprehensive summary among the cited publications, image modalities and datasets from various aspects, which hope to reveal the pros and cons of every method, summarize its achievements and discuss the challenges and future trends. Full article
(This article belongs to the Special Issue Machine Learning in Biomedical Data Analysis)
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