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Personalized Medicine with Biomedical and Health Informatics
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Personalized Medicine with Biomedical and Health Informatics

A special issue of Journal of Personalized Medicine (ISSN 2075-4426). This special issue belongs to the section "Omics/Informatics".

Deadline for manuscript submissions: closed (20 January 2023) | Viewed by 22969

Special Issue Editors

Prof. Dr. Shang-Ming Zhou

E-Mail Website
Guest Editor
Centre for Health Technology, University of Plymouth, Devon PL4 8AA, UK
Interests: e-health; AI for healthcare and medicine; health data science; health informatics
Special Issues, Collections and Topics in MDPI journals
Dr. Ian Overton

E-Mail Website
Guest Editor
Patrick G Johnston Centre for Cancer Research, School of Medicine, Dentistry and Biomedical Sciences, Queen's University Belfast, Belfast, UK
Interests: understanding molecular control and consequences of cell phenotypic plasticity in metastasis and drug resistance; developing more effective approaches for cancer patient stratification; generation of novel algorithms, techniques and computational workflows to advance the above
Dr. Haider Raza

E-Mail Website
Guest Editor
School of Computer Science and Electronic Engineering, University of Essex, Colchester CO4 3SQ, UK
Interests: big data and analytics; brain–computer interface; deep learning; transfer learning; non-stationary learning and domain adaptation; artificial intelligence (AI) and eXplainable AI (XAI); EEG and MEG signal processing; AI in decision making for healthcare
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. Jie Zheng

E-Mail Website
Guest Editor
School of Information Science and Technology, ShanghaiTech University, 313 Middle Huaxia Road, Pudong District, Shanghai 201210, China
Interests: bioinformatics, computational and systems biology; data science for biomedicine and healthcare; machine learning in life sciences; dynamical modeling and simulation of cell fate; algorithm design and analysis

Special Issue Information

Dear Colleagues,

Personalised medicine (PM) is becoming increasingly important in the future of healthcare. PM can be considered a data-driven approach, integrating data from multiple sources on the biological makeup of each individual, the environmental and lifestyle factors, and using such combined information to predict outcomes of treatments/preventions, likelihood of disease, and history of subtypes.

This Special Issue aims to investigate how biomedical and health informatics technologies fulfill the promises of PM in improving patient care and disease prevention, reducing healthcare costs alongside improvements in the efficacy and safety of interventions, improving new medical product developments and so on.

Prof. Dr. Shang-Ming Zhou
Dr. Ian Overton
Dr. Haider Raza
Prof. Dr. Jie Zheng
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. Journal of Personalized Medicine 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

  • personalized medicine
  • precision medicine
  • biomedical informatics
  • health informatics
  • -omic data
  • molecular diagnostics
  • pharmacogenomics
  • toxicogenomics
  • electronic health records
  • big data analytics

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Further information on MDPI's Special Issue polices can be found here.

Published Papers (6 papers)

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Research

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20 pages, 2158 KiB  
Article
Immune Cell Networks Uncover Candidate Biomarkers of Melanoma Immunotherapy Response
by Duong H. T. Vo, Gerard McGleave and Ian M. Overton
J. Pers. Med. 2022, 12(6), 958; https://doi.org/10.3390/jpm12060958 - 11 Jun 2022
Cited by 1 | Viewed by 2688
Abstract
The therapeutic activation of antitumour immunity by immune checkpoint inhibitors (ICIs) is a significant advance in cancer medicine, not least due to the prospect of long-term remission. However, many patients are unresponsive to ICI therapy and may experience serious side effects; companion biomarkers [...] Read more.
The therapeutic activation of antitumour immunity by immune checkpoint inhibitors (ICIs) is a significant advance in cancer medicine, not least due to the prospect of long-term remission. However, many patients are unresponsive to ICI therapy and may experience serious side effects; companion biomarkers are urgently needed to help inform ICI prescribing decisions. We present the IMMUNETS networks of gene coregulation in five key immune cell types and their application to interrogate control of nivolumab response in advanced melanoma cohorts. The results evidence a role for each of the IMMUNETS cell types in ICI response and in driving tumour clearance with independent cohorts from TCGA. As expected, ‘immune hot’ status, including T cell proliferation, correlates with response to first-line ICI therapy. Genes regulated in NK, dendritic, and B cells are the most prominent discriminators of nivolumab response in patients that had previously progressed on another ICI. Multivariate analysis controlling for tumour stage and age highlights CIITA and IKZF3 as candidate prognostic biomarkers. IMMUNETS provide a resource for network biology, enabling context-specific analysis of immune components in orthogonal datasets. Overall, our results illuminate the relationship between the tumour microenvironment and clinical trajectories, with potential implications for precision medicine. Full article
(This article belongs to the Special Issue Personalized Medicine with Biomedical and Health Informatics)
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13 pages, 1067 KiB  
Article
Obesity-Associated Differentially Methylated Regions in Colon Cancer
by John J. Milner, Zhao-Feng Chen, James Grayson and Shyang-Yun Pamela Koong Shiao
J. Pers. Med. 2022, 12(5), 660; https://doi.org/10.3390/jpm12050660 - 20 Apr 2022
Cited by 4 | Viewed by 2772
Abstract
Obesity with adiposity is a common disorder in modern days, influenced by environmental factors such as eating and lifestyle habits and affecting the epigenetics of adipose-based gene regulations and metabolic pathways in colorectal cancer (CRC). We compared epigenetic changes of differentially methylated regions [...] Read more.
Obesity with adiposity is a common disorder in modern days, influenced by environmental factors such as eating and lifestyle habits and affecting the epigenetics of adipose-based gene regulations and metabolic pathways in colorectal cancer (CRC). We compared epigenetic changes of differentially methylated regions (DMR) of genes in colon tissues of 225 colon cancer cases (154 non-obese and 71 obese) and 15 healthy non-obese controls by accessing The Cancer Genome Atlas (TCGA) data. We applied machine-learning-based analytics including generalized regression (GR) as a confirmatory validation model to identify the factors that could contribute to DMRs impacting colon cancer to enhance prediction accuracy. We found that age was a significant predictor in obese cancer patients, both alone (p = 0.003) and interacting with hypomethylated DMRs of ZBTB46, a tumor suppressor gene (p = 0.008). DMRs of three additional genes: HIST1H3I (p = 0.001), an oncogene with a hypomethylated DMR in the promoter region; SRGAP2C (p = 0.006), a tumor suppressor gene with a hypermethylated DMR in the promoter region; and NFATC4 (p = 0.006), an adipocyte differentiating oncogene with a hypermethylated DMR in an intron region, are also significant predictors of cancer in obese patients, independent of age. The genes affected by these DMR could be potential novel biomarkers of colon cancer in obese patients for cancer prevention and progression. Full article
(This article belongs to the Special Issue Personalized Medicine with Biomedical and Health Informatics)
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14 pages, 638 KiB  
Article
A Clinical Decision Support System for the Prediction of Quality of Life in ALS
by Anna Markella Antoniadi, Miriam Galvin, Mark Heverin, Lan Wei, Orla Hardiman and Catherine Mooney
J. Pers. Med. 2022, 12(3), 435; https://doi.org/10.3390/jpm12030435 - 10 Mar 2022
Cited by 8 | Viewed by 4368
Abstract
Amyotrophic Lateral Sclerosis (ALS), also known as Motor Neuron Disease (MND), is a rare and fatal neurodegenerative disease. As ALS is currently incurable, the aim of the treatment is mainly to alleviate symptoms and improve quality of life (QoL). We designed a prototype [...] Read more.
Amyotrophic Lateral Sclerosis (ALS), also known as Motor Neuron Disease (MND), is a rare and fatal neurodegenerative disease. As ALS is currently incurable, the aim of the treatment is mainly to alleviate symptoms and improve quality of life (QoL). We designed a prototype Clinical Decision Support System (CDSS) to alert clinicians when a person with ALS is experiencing low QoL in order to inform and personalise the support they receive. Explainability is important for the success of a CDSS and its acceptance by healthcare professionals. The aim of this work isto announce our prototype (C-ALS), supported by a first short evaluation of its explainability. Given the lack of similar studies and systems, this work is a valid proof-of-concept that will lead to future work. We developed a CDSS that was evaluated by members of the team of healthcare professionals that provide care to people with ALS in the ALS/MND Multidisciplinary Clinic in Dublin, Ireland. We conducted a user study where participants were asked to review the CDSS and complete a short survey with a focus on explainability. Healthcare professionals demonstrated some uncertainty in understanding the system’s output. Based on their feedback, we altered the explanation provided in the updated version of our CDSS. C-ALS provides local explanations of its predictions in a post-hoc manner, using SHAP (SHapley Additive exPlanations). The CDSS predicts the risk of low QoL in the form of a probability, a bar plot shows the feature importance for the specific prediction, along with some verbal guidelines on how to interpret the results. Additionally, we provide the option of a global explanation of the system’s function in the form of a bar plot showing the average importance of each feature. C-ALS is available online for academic use. Full article
(This article belongs to the Special Issue Personalized Medicine with Biomedical and Health Informatics)
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15 pages, 1760 KiB  
Article
Predicting Hospital Readmission for Campylobacteriosis from Electronic Health Records: A Machine Learning and Text Mining Perspective
by Shang-Ming Zhou, Ronan A. Lyons, Muhammad A. Rahman, Alexander Holborow and Sinead Brophy
J. Pers. Med. 2022, 12(1), 86; https://doi.org/10.3390/jpm12010086 - 10 Jan 2022
Cited by 7 | Viewed by 3171
Abstract
(1) Background: This study investigates influential risk factors for predicting 30-day readmission to hospital for Campylobacter infections (CI). (2) Methods: We linked general practitioner and hospital admission records of 13,006 patients with CI in Wales (1990–2015). An approach called TF-zR (term frequency-zRelevance) technique [...] Read more.
(1) Background: This study investigates influential risk factors for predicting 30-day readmission to hospital for Campylobacter infections (CI). (2) Methods: We linked general practitioner and hospital admission records of 13,006 patients with CI in Wales (1990–2015). An approach called TF-zR (term frequency-zRelevance) technique was presented to evaluates how relevant a clinical term is to a patient in a cohort characterized by coded health records. The zR is a supervised term-weighting metric to assign weight to a term based on relative frequencies of the term across different classes. Cost-sensitive classifier with swarm optimization and weighted subset learning was integrated to identify influential clinical signals as predictors and optimal model for readmission prediction. (3) Results: From a pool of up to 17,506 variables, 33 most predictive factors were identified, including age, gender, Townsend deprivation quintiles, comorbidities, medications, and procedures. The predictive model predicted readmission with 73% sensitivity and 54% specificity. Variables associated with readmission included male gender, recurrent tonsillitis, non-healing open wounds, operation for in-gown toenails. Cystitis, paracetamol/codeine use, age (21–25), and heliclear triple pack use, were associated with a lower risk of readmission. (4) Conclusions: This study gives a profile of clustered variables that are predictive of readmission associated with campylobacteriosis. Full article
(This article belongs to the Special Issue Personalized Medicine with Biomedical and Health Informatics)
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21 pages, 6055 KiB  
Article
Feature Explanations in Recurrent Neural Networks for Predicting Risk of Mortality in Intensive Care Patients
by Thanakron Na Pattalung, Thammasin Ingviya and Sitthichok Chaichulee
J. Pers. Med. 2021, 11(9), 934; https://doi.org/10.3390/jpm11090934 - 19 Sep 2021
Cited by 14 | Viewed by 4188
Abstract
Critical care staff are presented with a large amount of data, which made it difficult to systematically evaluate. Early detection of patients whose condition is deteriorating could reduce mortality, improve treatment outcomes, and allow a better use of healthcare resources. In this study, [...] Read more.
Critical care staff are presented with a large amount of data, which made it difficult to systematically evaluate. Early detection of patients whose condition is deteriorating could reduce mortality, improve treatment outcomes, and allow a better use of healthcare resources. In this study, we propose a data-driven framework for predicting the risk of mortality that combines high-accuracy recurrent neural networks with interpretable explanations. Our model processes time-series of vital signs and laboratory observations to predict the probability of a patient’s mortality in the intensive care unit (ICU). We investigated our approach on three public critical care databases: Multiparameter Intelligent Monitoring in Intensive Care III (MIMIC-III), MIMIC-IV, and eICU. Our models achieved an area under the receiver operating characteristic curve (AUC) of 0.87–0.91. Our approach was not only able to provide the predicted mortality risk but also to recognize and explain the historical contributions of the associated factors to the prediction. The explanations provided by our model were consistent with the literature. Patients may benefit from early intervention if their clinical observations in the ICU are continuously monitored in real time. Full article
(This article belongs to the Special Issue Personalized Medicine with Biomedical and Health Informatics)
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Review

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24 pages, 1913 KiB  
Review
Biobanking as a Tool for Genomic Research: From Allele Frequencies to Cross-Ancestry Association Studies
by Tatyana E. Lazareva, Yury A. Barbitoff, Anton I. Changalidis, Alexander A. Tkachenko, Evgeniia M. Maksiutenko, Yulia A. Nasykhova and Andrey S. Glotov
J. Pers. Med. 2022, 12(12), 2040; https://doi.org/10.3390/jpm12122040 - 9 Dec 2022
Cited by 10 | Viewed by 3665
Abstract
In recent years, great advances have been made in the field of collection, storage, and analysis of biological samples. Large collections of samples, biobanks, have been established in many countries. Biobanks typically collect large amounts of biological samples and associated clinical information; the [...] Read more.
In recent years, great advances have been made in the field of collection, storage, and analysis of biological samples. Large collections of samples, biobanks, have been established in many countries. Biobanks typically collect large amounts of biological samples and associated clinical information; the largest collections include over a million samples. In this review, we summarize the main directions in which biobanks aid medical genetics and genomic research, from providing reference allele frequency information to allowing large-scale cross-ancestry meta-analyses. The largest biobanks greatly vary in the size of the collection, and the amount of available phenotype and genotype data. Nevertheless, all of them are extensively used in genomics, providing a rich resource for genome-wide association analysis, genetic epidemiology, and statistical research into the structure, function, and evolution of the human genome. Recently, multiple research efforts were based on trans-biobank data integration, which increases sample size and allows for the identification of robust genetic associations. We provide prominent examples of such data integration and discuss important caveats which have to be taken into account in trans-biobank research. Full article
(This article belongs to the Special Issue Personalized Medicine with Biomedical and Health Informatics)
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