AI & Machine Learning in Bioinformatics and Healthcare Informatics

A special issue of Methods and Protocols (ISSN 2409-9279). This special issue belongs to the section "Omics and High Throughput".

Deadline for manuscript submissions: closed (30 November 2023) | Viewed by 10807

Special Issue Editors


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School of Computing, Ulster University, Belfast BT15 1AP, UK
Interests: machine learning; bioinformatics; healthcare informatics; healthcare technology; intelligent data analysis; integrative data analytics; assistive technologies
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School of Computing, Jordanstown campus, Ulster University, Room 16E15, Shore Road, Newtownabbey, Co., Antrim BT37 0QB, UK
Interests: machine learning; data mining; data integration; robotics and computational biology
Special Issues, Collections and Topics in MDPI journals

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Faculty of Mathematics and Computer Science, University of Hagen, 58097 Hagen, Germany
Interests: multimedia and internet applications; information systems; semantic Web; knowledge management; big data analysis; bioinformatics healthcare informatics; digital preservation and long term archival

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Guest Editor
Faculty of Mathematics and Computer Science, University of Hagen, 58097 Hagen, Germany
Interests: information retrieval information systems; semantic Web, knowledge management; data management; digital preservation and long term archival

Special Issue Information

Dear Colleagues,

AI and machine learning have been applied to bioinformatics and healthcare informatics in analysing omics data, electronic health records, medical imaging, biomarks and lifestyle data. In this Special Issue titled “AI and Machine Learning in Bioinformatics and Healthcare Informatics”, we seek original regular, review articles, innovative methods and protocols, and reports on AI and machine learning with applications to bioinformatics and healthcare informatics.

Additionally, selected papers from CERC2021, the Machine Learning and Artificial Intelligence in Bioinformatics and Medical Informatics (MABM2021) Workshop, and Integrative Data Analysis in Systmes Biology Workshop (IDASB2021), in conjunction with BIBM2021, are particularly invited to this Special Issue.

Prof. Dr. Huiru Zheng
Dr. Haiying Wang
Prof. Dr. Matthias L. Hemmje
Dr. Felix Engel
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. Methods and Protocols 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 (3 papers)

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Research

15 pages, 1891 KiB  
Article
Performance and Information Leakage in Splitfed Learning and Multi-Head Split Learning in Healthcare Data and Beyond
by Praveen Joshi, Chandra Thapa, Seyit Camtepe, Mohammed Hasanuzzaman, Ted Scully and Haithem Afli
Methods Protoc. 2022, 5(4), 60; https://doi.org/10.3390/mps5040060 - 13 Jul 2022
Cited by 4 | Viewed by 2922
Abstract
Machine learning (ML) in healthcare data analytics is attracting much attention because of the unprecedented power of ML to extract knowledge that improves the decision-making process. At the same time, laws and ethics codes drafted by countries to govern healthcare data are becoming [...] Read more.
Machine learning (ML) in healthcare data analytics is attracting much attention because of the unprecedented power of ML to extract knowledge that improves the decision-making process. At the same time, laws and ethics codes drafted by countries to govern healthcare data are becoming stringent. Although healthcare practitioners are struggling with an enforced governance framework, we see the emergence of distributed learning-based frameworks disrupting traditional-ML-model development. Splitfed learning (SFL) is one of the recent developments in distributed machine learning that empowers healthcare practitioners to preserve the privacy of input data and enables them to train ML models. However, SFL has some extra communication and computation overheads at the client side due to the requirement of client-side model synchronization. For a resource-constrained client side (hospitals with limited computational powers), removing such conditions is required to gain efficiency in the learning. In this regard, this paper studies SFL without client-side model synchronization. The resulting architecture is known as multi-head split learning (MHSL). At the same time, it is important to investigate information leakage, which indicates how much information is gained by the server related to the raw data directly out of the smashed data—the output of the client-side model portion—passed to it by the client. Our empirical studies examine the Resnet-18 and Conv1-D architecture model on the ECG and HAM-10000 datasets under IID data distribution. The results find that SFL provides 1.81% and 2.36% better accuracy than MHSL on the ECG and HAM-10000 datasets, respectively (for cut-layer value set to 1). Analysis of experimentation with various client-side model portions demonstrates that it has an impact on the overall performance. With an increase in layers in the client-side model portion, SFL performance improves while MHSL performance degrades. Experiment results also demonstrate that information leakage provided by mutual information score values in SFL is more than MHSL for ECG and HAM-10000 datasets by 2×105 and 4×103, respectively. Full article
(This article belongs to the Special Issue AI & Machine Learning in Bioinformatics and Healthcare Informatics)
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14 pages, 11711 KiB  
Article
Assessing Impact of Sensors and Feature Selection in Smart-Insole-Based Human Activity Recognition
by Luigi D’Arco, Haiying Wang and Huiru Zheng
Methods Protoc. 2022, 5(3), 45; https://doi.org/10.3390/mps5030045 - 31 May 2022
Cited by 13 | Viewed by 2712
Abstract
Human Activity Recognition (HAR) is increasingly used in a variety of applications, including health care, fitness tracking, and rehabilitation. To reduce the impact on the user’s daily activities, wearable technologies have been advanced throughout the years. In this study, an improved smart insole-based [...] Read more.
Human Activity Recognition (HAR) is increasingly used in a variety of applications, including health care, fitness tracking, and rehabilitation. To reduce the impact on the user’s daily activities, wearable technologies have been advanced throughout the years. In this study, an improved smart insole-based HAR system is proposed. The impact of data segmentation, sensors used, and feature selection on HAR was fully investigated. The Support Vector Machine (SVM), a supervised learning algorithm, has been used to recognise six ambulation activities: downstairs, sit to stand, sitting, standing, upstairs, and walking. Considering the impact that data segmentation can have on the classification, the sliding window size was optimised, identifying the length of 10 s with 50% of overlap as the best performing. The inertial sensors and pressure sensors embedded into the smart insoles have been assessed to determine the importance that each one has in the classification. A feature selection technique has been applied to reduce the number of features from 272 to 227 to improve the robustness of the proposed system and to investigate the importance of features in the dataset. According to the findings, the inertial sensors are reliable for the recognition of dynamic activities, while pressure sensors are reliable for stationary activities; however, the highest accuracy (94.66%) was achieved by combining both types of sensors. Full article
(This article belongs to the Special Issue AI & Machine Learning in Bioinformatics and Healthcare Informatics)
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15 pages, 10098 KiB  
Article
BiGAMi: Bi-Objective Genetic Algorithm Fitness Function for Feature Selection on Microbiome Datasets
by Mike Leske, Francesca Bottacini, Haithem Afli and Bruno G. N. Andrade
Methods Protoc. 2022, 5(3), 42; https://doi.org/10.3390/mps5030042 - 23 May 2022
Cited by 3 | Viewed by 3135
Abstract
The relationship between the host and the microbiome, or the assemblage of microorganisms (including bacteria, archaea, fungi, and viruses), has been proven crucial for its health and disease development. The high dimensionality of microbiome datasets has often been addressed as a major difficulty [...] Read more.
The relationship between the host and the microbiome, or the assemblage of microorganisms (including bacteria, archaea, fungi, and viruses), has been proven crucial for its health and disease development. The high dimensionality of microbiome datasets has often been addressed as a major difficulty for data analysis, such as the use of machine-learning (ML) and deep-learning (DL) models. Here, we present BiGAMi, a bi-objective genetic algorithm fitness function for feature selection in microbial datasets to train high-performing phenotype classifiers. The proposed fitness function allowed us to build classifiers that outperformed the baseline performance estimated by the original studies by using as few as 0.04% to 2.32% features of the original dataset. In 35 out of 42 performance comparisons between BiGAMi and other feature selection methods evaluated here (sequential forward selection, SelectKBest, and GARS), BiGAMi achieved its results by selecting 6–93% fewer features. This study showed that the application of a bi-objective GA fitness function against microbiome datasets succeeded in selecting small subsets of bacteria whose contribution to understood diseases and the host state was already experimentally proven. Applying this feature selection approach to novel diseases is expected to quickly reveal the microbes most relevant to a specific condition. Full article
(This article belongs to the Special Issue AI & Machine Learning in Bioinformatics and Healthcare Informatics)
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