Novel Informatics Algorithms and Applications to Biomedicine and Healthcare

A special issue of Informatics (ISSN 2227-9709). This special issue belongs to the section "Medical and Clinical Informatics".

Deadline for manuscript submissions: closed (28 February 2024) | Viewed by 11012

Special Issue Editors


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Biomedical Informatics, Department of Health Outcomes & Policy, College of Medicine, University of Florida, Gainesville, FL 32610, USA
Interests: real-world data; electronic health records; data science; machine learning; data privacy; security; clinical and clinical research informatics
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College of Health Solution, Arizona State University, Scottsdale, AZ 85281, USA
Interests: integrative analysis of multi-modal data; cancer evolution; health disparities
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Institute for Informatics, Department of Pediatrics, Washington University in St Louis, St Louis, MO 63108, USA
Interests: artificial intelligence and deep learning; graph neural network; multi-omics data analysis; network inference; disease-immune cell-cell signaling interactions; drug repurposing
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USF Genomics & College of Public Health, University of South Florida, Tampa, FL 33612, USA
Interests: variant functional annotation and prediction; mendelian disease; genetic epidemiology; human population genetics
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Special Issue Information

Dear Colleagues,

Recent advances in informatics and data science have revolutionized biomedical research and clinical practice, enabling precision medicine in various aspects. This Special Issue focuses on the latest research on computational methods and discoveries in biology, medicine, and human health. We welcome original studies from the International Conference on Intelligent Biology and Medicine 2023 (ICIBM 2023, Tampa, FL) that develop novel algorithms, databases, models, software, and other resources for biomedical and healthcare science. These include new tools for data storage, management, quality assessment, standardization, harmonization, analysis, modeling, visualization, interpretation, and dissemination. Application domains include Bioinformatics, Systems Biology, Medical Informatics, Health Informatics, Imaging Informatics, Population Health Informatics, and other biomedicine-related fields. Studies that analyze data using existing methods or compare existing methods must report significant novel discoveries. Reviews must include comprehensive and quantitative evaluations.

Prof. Dr. Jiang Bian
Dr. Li Liu
Dr. Fuhai Li
Dr. Xiaoming Liu
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. Informatics is an international peer-reviewed open access quarterly 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.

Keywords

  • health informatics
  • medical informatics
  • bioinformatics
  • systems biology
  • imaging informatics
  • healthcare

Published Papers (5 papers)

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Research

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27 pages, 1241 KiB  
Article
Computational Ensemble Gene Co-Expression Networks for the Analysis of Cancer Biomarkers
by Julia Figueroa-Martínez, Dulcenombre M. Saz-Navarro, Aurelio López-Fernández, Domingo S. Rodríguez-Baena and Francisco A. Gómez-Vela
Informatics 2024, 11(2), 14; https://doi.org/10.3390/informatics11020014 - 28 Mar 2024
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Abstract
Gene networks have become a powerful tool for the comprehensive examination of gene expression patterns. Thanks to these networks generated by means of inference algorithms, it is possible to study different biological processes and even identify new biomarkers for such diseases. These biomarkers [...] Read more.
Gene networks have become a powerful tool for the comprehensive examination of gene expression patterns. Thanks to these networks generated by means of inference algorithms, it is possible to study different biological processes and even identify new biomarkers for such diseases. These biomarkers are essential for the discovery of new treatments for genetic diseases such as cancer. In this work, we introduce an algorithm for genetic network inference based on an ensemble method that improves the robustness of the results by combining two main steps: first, the evaluation of the relationship between pairs of genes using three different co-expression measures, and, subsequently, a voting strategy. The utility of this approach was demonstrated by applying it to a human dataset encompassing breast and prostate cancer-associated stromal cells. Two gene networks were computed using microarray data, one for breast cancer and one for prostate cancer. The results obtained revealed, on the one hand, distinct stromal cell behaviors in breast and prostate cancer and, on the other hand, a list of potential biomarkers for both diseases. In the case of breast tumor, ST6GAL2, RIPOR3, COL5A1, and DEPDC7 were found, and in the case of prostate tumor, the genes were GATA6-AS1, ARFGEF3, PRR15L, and APBA2. These results demonstrate the usefulness of the ensemble method in the field of biomarker discovery. Full article
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15 pages, 5438 KiB  
Article
EndoNet: A Model for the Automatic Calculation of H-Score on Histological Slides
by Egor Ushakov, Anton Naumov, Vladislav Fomberg, Polina Vishnyakova, Aleksandra Asaturova, Alina Badlaeva, Anna Tregubova, Evgeny Karpulevich, Gennady Sukhikh and Timur Fatkhudinov
Informatics 2023, 10(4), 90; https://doi.org/10.3390/informatics10040090 - 12 Dec 2023
Viewed by 1922
Abstract
H-score is a semi-quantitative method used to assess the presence and distribution of proteins in tissue samples by combining the intensity of staining and the percentage of stained nuclei. It is widely used but time-consuming and can be limited in terms of accuracy [...] Read more.
H-score is a semi-quantitative method used to assess the presence and distribution of proteins in tissue samples by combining the intensity of staining and the percentage of stained nuclei. It is widely used but time-consuming and can be limited in terms of accuracy and precision. Computer-aided methods may help overcome these limitations and improve the efficiency of pathologists’ workflows. In this work, we developed a model EndoNet for automatic H-score calculation on histological slides. Our proposed method uses neural networks and consists of two main parts. The first is a detection model which predicts the keypoints of centers of nuclei. The second is an H-score module that calculates the value of the H-score using mean pixel values of predicted keypoints. Our model was trained and validated on 1780 annotated tiles with a shape of 100 × 100 µm and we achieved 0.77 mAP on a test dataset. We obtained our best results in H-score calculation; these results proved superior to QuPath predictions. Moreover, the model can be adjusted to a specific specialist or whole laboratory to reproduce the manner of calculating the H-score. Thus, EndoNet is effective and robust in the analysis of histology slides, which can improve and significantly accelerate the work of pathologists. Full article
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14 pages, 3824 KiB  
Article
A Machine Learning-Based Multiple Imputation Method for the Health and Aging Brain Study–Health Disparities
by Fan Zhang, Melissa Petersen, Leigh Johnson, James Hall, Raymond F. Palmer, Sid E. O’Bryant and on behalf of the Health and Aging Brain Study (HABS–HD) Study Team
Informatics 2023, 10(4), 77; https://doi.org/10.3390/informatics10040077 - 11 Oct 2023
Viewed by 1965
Abstract
The Health and Aging Brain Study–Health Disparities (HABS–HD) project seeks to understand the biological, social, and environmental factors that impact brain aging among diverse communities. A common issue for HABS–HD is missing data. It is impossible to achieve accurate machine learning (ML) if [...] Read more.
The Health and Aging Brain Study–Health Disparities (HABS–HD) project seeks to understand the biological, social, and environmental factors that impact brain aging among diverse communities. A common issue for HABS–HD is missing data. It is impossible to achieve accurate machine learning (ML) if data contain missing values. Therefore, developing a new imputation methodology has become an urgent task for HABS–HD. The three missing data assumptions, (1) missing completely at random (MCAR), (2) missing at random (MAR), and (3) missing not at random (MNAR), necessitate distinct imputation approaches for each mechanism of missingness. Several popular imputation methods, including listwise deletion, min, mean, predictive mean matching (PMM), classification and regression trees (CART), and missForest, may result in biased outcomes and reduced statistical power when applied to downstream analyses such as testing hypotheses related to clinical variables or utilizing machine learning to predict AD or MCI. Moreover, these commonly used imputation techniques can produce unreliable estimates of missing values if they do not account for the missingness mechanisms or if there is an inconsistency between the imputation method and the missing data mechanism in HABS–HD. Therefore, we proposed a three-step workflow to handle missing data in HABS–HD: (1) missing data evaluation, (2) imputation, and (3) imputation evaluation. First, we explored the missingness in HABS–HD. Then, we developed a machine learning-based multiple imputation method (MLMI) for imputing missing values. We built four ML-based imputation models (support vector machine (SVM), random forest (RF), extreme gradient boosting (XGB), and lasso and elastic-net regularized generalized linear model (GLMNET)) and adapted the four ML-based models to multiple imputations using the simple averaging method. Lastly, we evaluated and compared MLMI with other common methods. Our results showed that the three-step workflow worked well for handling missing values in HABS–HD and the ML-based multiple imputation method outperformed other common methods in terms of prediction performance and change in distribution and correlation. The choice of missing handling methodology has a significant impact on the accompanying statistical analyses of HABS–HD. The conceptual three-step workflow and the ML-based multiple imputation method perform well for our Alzheimer’s disease models. They can also be applied to other disease data analyses. Full article
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15 pages, 2586 KiB  
Article
Exploring How Healthcare Organizations Use Twitter: A Discourse Analysis
by Aditya Singhal and Vijay Mago
Informatics 2023, 10(3), 65; https://doi.org/10.3390/informatics10030065 - 8 Aug 2023
Viewed by 1949
Abstract
The use of Twitter by healthcare organizations is an effective means of disseminating medical information to the public. However, the content of tweets can be influenced by various factors, such as health emergencies and medical breakthroughs. In this study, we conducted a discourse [...] Read more.
The use of Twitter by healthcare organizations is an effective means of disseminating medical information to the public. However, the content of tweets can be influenced by various factors, such as health emergencies and medical breakthroughs. In this study, we conducted a discourse analysis to better understand how public and private healthcare organizations use Twitter and the factors that influence the content of their tweets. Data were collected from the Twitter accounts of five private pharmaceutical companies, two US and two Canadian public health agencies, and the World Health Organization from 1 January 2020, to 31 December 2022. The study applied topic modeling and association rule mining to identify text patterns that influence the content of tweets across different Twitter accounts. The findings revealed that building a reputation on Twitter goes beyond just evaluating the popularity of a tweet in the online sphere. Topic modeling, when applied synchronously with hashtag and tagging analysis can provide an increase in tweet popularity. Additionally, the study showed differences in language use and style across the Twitter accounts’ categories and discussed how the impact of popular association rules could translate to significantly more user engagement. Overall, the results of this study provide insights into natural language processing for health literacy and present a way for organizations to structure their future content to ensure maximum public engagement. Full article
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Review

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29 pages, 1909 KiB  
Review
Cloud-Based Platforms for Health Monitoring: A Review
by Isaac Machorro-Cano, José Oscar Olmedo-Aguirre, Giner Alor-Hernández, Lisbeth Rodríguez-Mazahua, Laura Nely Sánchez-Morales and Nancy Pérez-Castro
Informatics 2024, 11(1), 2; https://doi.org/10.3390/informatics11010002 - 20 Dec 2023
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Abstract
Cloud-based platforms have gained popularity over the years because they can be used for multiple purposes, from synchronizing contact information to storing and managing user fitness data. These platforms are still in constant development and, so far, most of the data they store [...] Read more.
Cloud-based platforms have gained popularity over the years because they can be used for multiple purposes, from synchronizing contact information to storing and managing user fitness data. These platforms are still in constant development and, so far, most of the data they store is entered manually by users. However, more and better wearable devices are being developed that can synchronize with these platforms to feed the information automatically. Another aspect that highlights the link between wearable devices and cloud-based health platforms is the improvement in which the symptomatology and/or physical status information of users can be stored and syn-chronized in real-time, 24 h a day, in health platforms, which in turn enables the possibility of synchronizing these platforms with specialized medical software to promptly detect important variations in user symptoms. This is opening opportunities to use these platforms as support for monitoring disease symptoms and, in general, for monitoring the health of users. In this work, the characteristics and possibilities of use of four popular platforms currently available in the market are explored, which are Apple Health, Google Fit, Samsung Health, and Fitbit. Full article
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