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Data-Driven Biomedical Research and Applications
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Data-Driven Biomedical Research and Applications

A special issue of Applied Sciences (ISSN 2076-3417). This special issue belongs to the section "Applied Biosciences and Bioengineering".

Deadline for manuscript submissions: closed (20 January 2022) | Viewed by 13698

Special Issue Editor

Dr. Marco Manfrini

E-Mail Website
Guest Editor
Maria Cecilia Hospital, GVM Care & Research, 48033 Cotignola, Italy
Interests: biostatistics; causal inference; predictive analytics; clinical models; bioinformatics; personalized medicine; epidemiology

Special Issue Information

Dear Colleagues,
In the last decade, biomedical scientists gained more knowledge of physiopathological mechanisms thanks to two key factors: the advent of high-throughput technologies and the introduction of high-performance computing into routine research activities. The former led to the capability to analyze at the genomic and proteomic level a multitude of organisms, from bacteria to human beings. The second led to the possibility of modeling systems to discover unknown patterns of interactions and introduced artificial intelligence tools and techniques in the laboratory and clinical research. To exploit the huge amount of data routinely collected in these settings, the scientific community is called to action. Indeed, in this landscape, development of new data management and analysis methods and of new translational and clinical applications is required in order to build reliable biomedical models and to obtain more efficacy in personalized medical therapies.

Dr. Marco Manfrini
Guest Editor

Manuscript Submission Information

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Keywords

  • biostatistics
  • causal inference
  • predictive analytics
  • clinical models
  • bioinformatics
  • personalized medicine
  • epidemiology

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Published Papers (5 papers)

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Research

15 pages, 1208 KiB  
Article
Causal Models for the Result of Percutaneous Coronary Intervention in Coronary Chronic Total Occlusions
by Maria Ganopoulou, Ioannis Kangelidis, Georgios Sianos and Lefteris Angelis
Appl. Sci. 2021, 11(19), 9258; https://doi.org/10.3390/app11199258 - 5 Oct 2021
Cited by 3 | Viewed by 1613
Abstract
Background: Patients undergoing coronary angiography very frequently exhibit coronary chronic total occlusions (CTOs). Over the last decade, there has been an increasing acceptance of the percutaneous coronary interventions (PCI) in CTOs due to, among else, rising operator experience and advances in technology. This [...] Read more.
Background: Patients undergoing coronary angiography very frequently exhibit coronary chronic total occlusions (CTOs). Over the last decade, there has been an increasing acceptance of the percutaneous coronary interventions (PCI) in CTOs due to, among else, rising operator experience and advances in technology. This study is an effort to address the problem of identifying important factors related to the success or failure of the PCI. Methods: The analysis is based on the EuroCTO Registry, which is the largest database available worldwide, consisting of 164 variables and 29,995 cases for the period 2008–2018. The aim is to assess the dynamics of causal models and causal discovery, using observational data, in predicting the result of the PCI. Causal models use graph structure to assess the cause–effect relationships between variables. In this study, the constrained-based algorithm PC was employed. The focus was to find the local causal structure around the PCI result and use it as a feature selection tool for building a predictive model. Results: The model developed was compared with other modeling approaches from the literature, and it was found to perform equally well or better. Conclusions: The analysis showcased the potential of employing local causal structure in predictive model development. Full article
(This article belongs to the Special Issue Data-Driven Biomedical Research and Applications)
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19 pages, 1417 KiB  
Article
A Variable Ranking Method for Machine Learning Models with Correlated Features: In-Silico Validation and Application for Diabetes Prediction
by Martina Vettoretti and Barbara Di Camillo
Appl. Sci. 2021, 11(16), 7740; https://doi.org/10.3390/app11167740 - 23 Aug 2021
Cited by 8 | Viewed by 4025
Abstract
When building a predictive model for predicting a clinical outcome using machine learning techniques, the model developers are often interested in ranking the features according to their predictive ability. A commonly used approach to obtain a robust variable ranking is to apply recursive [...] Read more.
When building a predictive model for predicting a clinical outcome using machine learning techniques, the model developers are often interested in ranking the features according to their predictive ability. A commonly used approach to obtain a robust variable ranking is to apply recursive feature elimination (RFE) on multiple resamplings of the training set and then to aggregate the ranking results using the Borda count method. However, the presence of highly correlated features in the training set can deteriorate the ranking performance. In this work, we propose a variant of the method based on RFE and Borda count that takes into account the correlation between variables during the ranking procedure in order to improve the ranking performance in the presence of highly correlated features. The proposed algorithm is tested on simulated datasets in which the true variable importance is known and compared to the standard RFE-Borda count method. According to the root mean square error between the estimated rank and the true (i.e., simulated) feature importance, the proposed algorithm overcomes the standard RFE-Borda count method. Finally, the proposed algorithm is applied to a case study related to the development of a predictive model of type 2 diabetes onset. Full article
(This article belongs to the Special Issue Data-Driven Biomedical Research and Applications)
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10 pages, 336 KiB  
Article
Does the Integration of Pre-Coded Information with Narratives Improve in-Hospital Falls’ Surveillance?
by Giulia Lorenzoni, Roberta Rampazzo, Alessia Buratin, Paola Berchialla and Dario Gregori
Appl. Sci. 2021, 11(10), 4406; https://doi.org/10.3390/app11104406 - 13 May 2021
Cited by 1 | Viewed by 1672
Abstract
To evaluate the value added by information reported in narratives (extracted through text mining techniques) in enhancing the characterization of falls patterns. Data on falls notified to the Risk Management Service of a Local Health Authority in Italy were considered in the analysis. [...] Read more.
To evaluate the value added by information reported in narratives (extracted through text mining techniques) in enhancing the characterization of falls patterns. Data on falls notified to the Risk Management Service of a Local Health Authority in Italy were considered in the analysis. Each record reported detailed pre-coded information about patient and fall’s characteristics, together with a narrative description of the fall. At first, multiple correspondence analysis (MCA) was performed on pre-coded information only. Then, it was re-run on the pre-coded data augmented with a variable representing the output analysis of the narrative records. This second analysis required a pre-processing of the narratives followed by text mining. Finally, a Hierarchical Clustering on the two MCA was carried out to identify distinct fall patterns. The dataset included 202 falls’ records. Three clusters corresponding to three distinct profiles of falls were identified through the Hierarchical Clustering performed using only pre-coded information. Hierarchical Clustering with the topic variable provided overlapping results. The present findings showed that the cluster analysis is effective in characterizing fall patterns; however, they do not sustain the hypothesis that the analysis of free-text information improves our understanding of such phenomenon. Full article
(This article belongs to the Special Issue Data-Driven Biomedical Research and Applications)
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6 pages, 362 KiB  
Article
To Swab or Not to Swab? The Lesson Learned in Italy in the Early Stage of the COVID-19 Pandemic
by Paola Berchialla, Maria Teresa Giraudo, Carmen Fava, Andrea Ricotti, Giuseppe Saglio, Giulia Lorenzoni, Veronica Sciannameo, Sara Urru, Ilaria Prosepe, Corrado Lanera, Danila Azzolina and Dario Gregori
Appl. Sci. 2021, 11(9), 4042; https://doi.org/10.3390/app11094042 - 29 Apr 2021
Cited by 3 | Viewed by 2036
Abstract
Testing for the SARS-CoV-2 infection is critical for tracking the spread of the virus and controlling the transmission dynamics. In the early phase of the pandemic in Italy, the decentralized healthcare system allowed regions to adopt different testing strategies. The objective of this [...] Read more.
Testing for the SARS-CoV-2 infection is critical for tracking the spread of the virus and controlling the transmission dynamics. In the early phase of the pandemic in Italy, the decentralized healthcare system allowed regions to adopt different testing strategies. The objective of this paper is to assess the impact of the extensive testing of symptomatic individuals and their contacts on the number of hospitalizations against a more stringent testing strategy limited to suspected cases with severe respiratory illness and an epidemiological link to a COVID-19 case. A Poisson regression modelling approach was adopted. In the first model developed, the cumulative daily number of positive cases and a temporal trend were considered as explanatory variables. In the second, the cumulative daily number of swabs was further added. The explanatory variable, given by the number of swabs over time, explained most of the observed differences in the number of hospitalizations between the two strategies. The percentage of the expected error dropped from 70% of the first, simpler model to 15%. Increasing testing to detect and isolate infected individuals in the early phase of an outbreak improves the capability to reduce the spread of serious infections, lessening the burden of hospitals. Full article
(This article belongs to the Special Issue Data-Driven Biomedical Research and Applications)
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11 pages, 1251 KiB  
Article
Chromosome Walking: A Novel Approach to Analyse Amino Acid Content of Human Proteins Ordered by Gene Position
by Annamaria Vernone, Chiara Ricca, Gianpiero Pescarmona and Francesca Silvagno
Appl. Sci. 2021, 11(8), 3511; https://doi.org/10.3390/app11083511 - 14 Apr 2021
Cited by 1 | Viewed by 3139
Abstract
Notwithstanding the huge amount of detailed information available in protein databases, it is not possible to automatically download a list of proteins ordered by the position of their codifying gene. This order becomes crucial when analyzing common features of proteins produced by loci [...] Read more.
Notwithstanding the huge amount of detailed information available in protein databases, it is not possible to automatically download a list of proteins ordered by the position of their codifying gene. This order becomes crucial when analyzing common features of proteins produced by loci or other specific regions of human chromosomes. In this study, we developed a new procedure that interrogates two human databases (genomic and protein) and produces a novel dataset of ordered proteins following the mapping of the corresponding genes. We validated and implemented the procedure to create a user-friendly web application. This novel data mining was used to evaluate the distribution of critical amino acid content in proteins codified by a human chromosome. For this purpose, we designed a new methodological approach called chromosome walking, which scanned the whole chromosome and found the regions producing proteins enriched in a selected amino acid. As an example of biomedical application, we investigated the human chromosome 15, which contains the locus DYX1 linked to developmental dyslexia, and we found three additional putative gene clusters whose expression could be driven by the environmental availability of glutamate. The novel data mining procedure and analysis could be exploited in the study of several human pathologies. Full article
(This article belongs to the Special Issue Data-Driven Biomedical Research and Applications)
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