Submit to Applied Sciences Review for Applied Sciences Propose a Special Issue

Journal Menu

Journal Browser

Applied Biostatistics & Statistical Computing

Print Special Issue Flyer
Special Issue Editors
Special Issue Information
Benefits of Publishing in a Special Issue
Related Special Issue
Published Papers

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 (31 March 2024) | Viewed by 21556

Share This Special Issue

Special Issue Editors

Dr. Ana Cristina Braga

E-Mail Website1 Website2
Guest Editor

Department of Production and Systems, ALGORITMI Center, University of Minho, Campus de Gualtar, 4710-057 Braga, Portugal
Interests: applied statistics; biostatistics; computational statistics; ROC analysis; multivariate statistics
Special Issues, Collections and Topics in MDPI journals

Dr. Cecília Castro

E-Mail Website
Guest Editor

Centre of Mathematics, University of Minho, 4710-057 Braga, Portugal
Interests: bayesian modeling; epidemiological models; fuzzy logic and decision-making; machine learning and data analytics; nonparametric inference; optimization techniques
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Due to the high complexity and dimension of the real world, the processing of data requires tools and methods capable of helping decision making.
Nowadays, health information is easier to capture and analyse than ever before given the developed data analytics. In the health sciences, biostatistics plays a central role in evaluating this data to better understand and tackle the health challenges that individuals and populations face across the globe.
This Special Issue aims to promote research works in computational statistics, scientific computation and applications in all areas of science involving a great volume of data or special datasets.
This Special Issue will focus on computational statistics, namely on new issues in the design of computational algorithms for implementing statistical methods, development in R, etc.; and applied biostatistics such as statistical case studies in all areas of science, including, medicine, biology, earth sciences and social sciences.

Topics of interest include (but are not limited to):

Statistical inference;
Statistical computing;
Biostatistics;
Reliability;
Survival analysis;
Decision theory;
Design of experiments;
Multivariate analysis;
Nonparametric inference;
Statistical genetics;
Statistical quality control;
Survey sampling;
Computational bayesian methods

Dr. Ana Cristina Braga
Dr. Cecília Castro
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. Applied Sciences 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 2400 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.

Benefits of Publishing in a Special Issue

Ease of navigation: Grouping papers by topic helps scholars navigate broad scope journals more efficiently.
Greater discoverability: Special Issues support the reach and impact of scientific research. Articles in Special Issues are more discoverable and cited more frequently.
Expansion of research network: Special Issues facilitate connections among authors, fostering scientific collaborations.
External promotion: Articles in Special Issues are often promoted through the journal's social media, increasing their visibility.
e-Book format: Special Issues with more than 10 articles can be published as dedicated e-books, ensuring wide and rapid dissemination.

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

Related Special Issue

Applied Biostatistics: Challenges and Opportunities in Applied Sciences (3 articles)

Published Papers (11 papers)

Download All Papers

Order results

Result details

Show export options Show export options

Select all

Export citation of selected articles as:

Research

Jump to: Review, Other

17 pages, 368 KiB

Open AccessArticle

Pointwise Nonparametric Estimation of Odds Ratio Curves with R: Introducing the flexOR Package

by Marta Azevedo, Luís Meira-Machado, Francisco Gude and Artur Araújo

Appl. Sci. 2024, 14(9), 3897; https://doi.org/10.3390/app14093897 - 2 May 2024

Viewed by 908

Abstract

The analysis of odds ratio curves is a valuable tool in understanding the relationship between continuous predictors and binary outcomes. Traditional parametric regression approaches often assume specific functional forms, limiting their flexibility and applicability to complex data. To address this limitation and introduce more flexibility, several smoothing methods may be applied, and approaches based on splines are the most frequently considered in this context. To better understand the effects that each continuous covariate has on the outcome, results can be expressed in terms of splines-based odds ratio (OR) curves, taking a specific covariate value as reference. In this paper, we introduce an R package, flexOR, which provides a comprehensive framework for pointwise nonparametric estimation of odds ratio curves for continuous predictors. The package can be used to estimate odds ratio curves without imposing rigid assumptions about their underlying functional form while considering a reference value for the continuous covariate. The package offers various options for automatically choosing the degrees of freedom in multivariable models. It also includes visualization functions to aid in the interpretation and presentation of the estimated odds ratio curves. flexOR offers a user-friendly interface, making it accessible to researchers and practitioners without extensive statistical backgrounds. Full article

(This article belongs to the Special Issue Applied Biostatistics & Statistical Computing)

► Show Figures

Figure 1

11 pages, 1214 KiB

Open AccessArticle

Stable Variable Selection Method with Shrinkage Regression Applied to the Selection of Genetic Variants Associated with Alzheimer’s Disease

by Vera Afreixo, Ana Helena Tavares, Vera Enes, Miguel Pinheiro, Leonor Rodrigues and Gabriela Moura

Appl. Sci. 2024, 14(6), 2572; https://doi.org/10.3390/app14062572 - 19 Mar 2024

Viewed by 1003

Abstract

In this work, we aimed to establish a stable and accurate procedure with which to perform feature selection in datasets with a much higher number of predictors than individuals, as in genome-wide association studies. Due to the instability of feature selection where many potential predictors are measured, a variable selection procedure is proposed that combines several replications of shrinkage regression models. A weighted formulation is used to define the final predictors. The procedure is applied for the investigation of single nucleotide polymorphism (SNP) predictors associated with Alzheimer’s disease in the Alzheimer’s Disease Neuroimaging Initiative (ADNI) dataset. Furthermore, the two following data scenarios are investigated: one that solely considers the set of SNPs, and another with the covariates of age, sex, educational level, and ε4 allele of the Apolipoprotein E (APOE4) genotype. The SNP rs2075650 and the APOE4 genotype are provided as risk factors for Alzheimer’s disease, which is in line with the literature, and another four new SNPs are indicated, thus cultivating new hypotheses for in vivo analyses. These experiments demonstrate the potential of the new method for stable feature selection. Full article

(This article belongs to the Special Issue Applied Biostatistics & Statistical Computing)

► Show Figures

Figure 1

25 pages, 6212 KiB

Open AccessArticle

Reliability Inference of Multicomponent Stress–Strength System Based on Chen Distribution Using Progressively Censored Data

by Chaoen Hu and Wenhao Gui

Appl. Sci. 2023, 13(11), 6509; https://doi.org/10.3390/app13116509 - 26 May 2023

Cited by 1 | Viewed by 1370

Abstract

In this paper, we study the inference of the multicomponent stress–strength reliability (MSSR) based on the Chen distribution using progressively Type-II censored data. Both the stress and strength variables follow the Chen distribution with a common second shape parameter. The maximum likelihood estimates and the asymptotic confidence intervals of the MSSR are developed. The bootstrap confidence interval of the MSSR is also constructed. The Bayesian estimation of the MSSR is obtained under the generalized entropy loss function using the Markov Chain Monte Carlo method. To check the effectiveness of the proposed approach, simulation studies are performed. Finally, a real data set is analyzed. Full article

(This article belongs to the Special Issue Applied Biostatistics & Statistical Computing)

► Show Figures

Figure 1

14 pages, 2934 KiB

Open AccessArticle

Predicting Astrocytic Nuclear Morphology with Machine Learning: A Tree Ensemble Classifier Study

by Piercesare Grimaldi, Martina Lorenzati, Marta Ribodino, Elena Signorino, Annalisa Buffo and Paola Berchialla

Appl. Sci. 2023, 13(7), 4289; https://doi.org/10.3390/app13074289 - 28 Mar 2023

Cited by 2 | Viewed by 1629

Abstract

Machine learning is usually associated with big data; however, experimental or clinical data are usually limited in size. The aim of this study was to describe how supervised machine learning can be used to classify astrocytes from a small sample into different morphological classes. Our dataset was composed of only 193 cells, with unbalanced morphological classes and missing observations. We combined classification trees and ensemble algorithms (boosting and bagging) with under sampling to classify the nuclear morphology (homogeneous, dotted, wrinkled, forming crumples, and forming micronuclei) of astrocytes stained with anti-LMNB1 antibody. Accuracy, sensitivity (recall), specificity, and F1 score were assessed with bootstrapping, leave one-out (LOOCV) and stratified cross-validation. We found that our algorithm performed at rates above chance in predicting the morphological classes of astrocytes based on the nuclear expression of LMNB1. Boosting algorithms (tree ensemble) yielded better classifications over bagging ones (tree bagger). Moreover leave-one-out and bootstrapping yielded better predictions than the more commonly used k-fold cross-validation. Finally, we could identify four important predictors: the intensity of LMNB1 expression, nuclear area, cellular area, and soma area. Our results show that a tree ensemble can be optimized, in order to classify morphological data from a small sample, even in the presence of highly unbalanced classes and numerous missing data. Full article

(This article belongs to the Special Issue Applied Biostatistics & Statistical Computing)

► Show Figures

Figure 1

19 pages, 1034 KiB

Open AccessArticle

Drowsiness Transitions Detection Using a Wearable Device

by Ana Rita Antunes, Ana Cristina Braga and Joaquim Gonçalves

Appl. Sci. 2023, 13(4), 2651; https://doi.org/10.3390/app13042651 - 18 Feb 2023

Cited by 4 | Viewed by 1703

Abstract

Due to a reduction in reaction time and, consequently, the driver’s concentration, driving when fatigued has become an issue throughout time. Consequently, the likelihood of having an accident and it being fatal increases. In this work, we aim to identify an automatic method capable of detecting drowsiness transitions by considering the time, frequency, and nonlinear domains of heart rate variability. Therefore, the methodology proposed considers the multivariate statistical process control, using principal components analysis, with accelerometer and time, frequency, and nonlinear domains of the heart rate variability extracted by a wearable device. Applying the proposed approach, it was possible to improve the results achieved in the previous studies, where it was able to remove points out-of-control due to signal noise, identify the drowsy transitions, and, consequently, improve the drowsiness classification. It is important to note that the out-of-control points of the heart rate variability are not influenced by external noise. In terms of limitations, this method was not able to detect all drowsiness transitions, and in some individuals, it falls far short of expectations. Regarding this, is essential to understand if there is any pattern or similarity among the participants in which it fails. Full article

(This article belongs to the Special Issue Applied Biostatistics & Statistical Computing)

► Show Figures

Figure 1

16 pages, 540 KiB

Open AccessArticle

Characterisation of Physiological Responses to Odours in Autism Spectrum Disorders: A Preliminary Study

by Lara Pereira, Joana Grave, Janina Noll, Birgit Derntl, Sandra C. Soares, Susana Brás and Raquel Sebastião

Appl. Sci. 2023, 13(3), 1970; https://doi.org/10.3390/app13031970 - 2 Feb 2023

Viewed by 2106

Abstract

Abnormal sensory perception is among the earliest symptoms of autism spectrum disorders (ASD). Despite mixed findings, olfactory perception seems to be altered in ASD. There is also evidence that automatic responses to odours can serve as biomarkers of ASD. However, this potential use of odour-based biomarkers for ASD is still underexplored. In this study, we aimed to investigate whether physiological responses to social and non-social odours, measured with electrocardiography (ECG) and facial electromyography (EMG), can be used to characterise and predict ASD in adults. For that, we extracted 32 signal features from a previously collected database of 11 adults with ASD and 48 adults with typical development (TD). Firstly, non-parametric tests were performed, showing significant differences between the ASD and the TD groups in 10 features. Secondly, a k-nearest-neighbour classifier with a leave-one-out strategy was employed, obtaining an F1-score of 67%. Although caution is needed due to the small sample size, this study provides preliminary evidence supporting the use of physiological responses to social and non-social odours as a potential diagnostic tool for ASD in adults. Full article

(This article belongs to the Special Issue Applied Biostatistics & Statistical Computing)

► Show Figures

Figure 1

23 pages, 3559 KiB

Open AccessArticle

The Power Zeghdoudi Distribution: Properties, Estimation, and Applications to Real Right-Censored Data

by Khaoula Aidi, Amer Ibrahim Al-Omari and Rehab Alsultan

Appl. Sci. 2022, 12(23), 12081; https://doi.org/10.3390/app122312081 - 25 Nov 2022

Cited by 3 | Viewed by 1358

Abstract

A new two-parameter power Zeghdoudi distribution (PZD) is suggested as a modification of the Zeghdoudi distribution using the power transformation method. As a result, the PZD may have increasing, decreasing, and unimodal probability density function and decreasing mean residual life function. In addition, other properties are presented, such as moments, order statistics, reliability measures, Bonferroni and Lorenz curves, Gini index, stochastic ordering, mean and median deviations, and quantile function. Following this, a section is devoted to the related model parameters which are estimated using the maximum likelihood estimation method, the weighted least squares and least squares methods, the maximum product of spacing method, the Cramer–von Mises method, and the right-tail and left-tail Anderson–Darling methods, and the Nikulin–Rao–Robson test statistic is considered. A simulation study is conducted to assess these methods and to investigate the distribution properties with right-censored data. The applicability of the proposed model is studied based on three real data sets of failure times, bladder cancer patients, and glass fiber data with a comparison with such competitors as the gamma, xgamma, Lomax, Darna, power Darna, power Lindley, and exponentiated power Lindley models. According to several established criteria, the comparative findings are overwhelmingly favorable to the suggested model. Full article

(This article belongs to the Special Issue Applied Biostatistics & Statistical Computing)

► Show Figures

Figure 1

19 pages, 2108 KiB

Open AccessArticle

A Genetic Programming Approach for Economic Forecasting with Survey Expectations

by Oscar Claveria, Enric Monte and Salvador Torra

Appl. Sci. 2022, 12(13), 6661; https://doi.org/10.3390/app12136661 - 30 Jun 2022

Cited by 3 | Viewed by 2173

Abstract

We apply a soft computing method to generate country-specific economic sentiment indicators that provide estimates of year-on-year GDP growth rates for 19 European economies. First, genetic programming is used to evolve business and consumer economic expectations to derive sentiment indicators for each country. To assess the performance of the proposed indicators, we first design a nowcasting experiment in which we recursively generate estimates of GDP at the end of each quarter, using the latest business and consumer survey data available. Second, we design a forecasting exercise in which we iteratively re-compute the sentiment indicators in each out-of-sample period. When evaluating the accuracy of the predictions obtained for different forecast horizons, we find that the evolved sentiment indicators outperform the time-series models used as a benchmark. These results show the potential of the proposed approach for prediction purposes. Full article

(This article belongs to the Special Issue Applied Biostatistics & Statistical Computing)

► Show Figures

Figure 1

19 pages, 439 KiB

Open AccessArticle

Application of Continuous Non-Gaussian Mortality Models with Markov Switchings to Forecast Mortality Rates

by Piotr Sliwka and Leslaw Socha

Appl. Sci. 2022, 12(12), 6203; https://doi.org/10.3390/app12126203 - 18 Jun 2022

Viewed by 1524

Abstract

The ongoing pandemic has resulted in the development of models dealing with the rate of virus spread and the modelling of mortality rates

μ_{x, t}

. A new method of modelling the mortality rates

μ_{x, t}

with different time [...] Read more.

The ongoing pandemic has resulted in the development of models dealing with the rate of virus spread and the modelling of mortality rates

μ_{x, t}

. A new method of modelling the mortality rates

μ_{x, t}

with different time intervals of higher and lower dispersion has been proposed. The modelling was based on the Milevski–Promislov class of stochastic mortality models with Markov switches, in which excitations are modelled by second-order polynomials of results from a linear non-Gaussian filter. In contrast to literature models where switches are deterministic, the Markov switches are proposed in this approach, which seems to be a new idea. The obtained results confirm that in the time intervals with a higher dispersion of

μ_{x, t}

, the proposed method approximates the empirical data more accurately than the commonly used the Lee–Carter model. Full article

(This article belongs to the Special Issue Applied Biostatistics & Statistical Computing)

► Show Figures

Figure 1

Review

Jump to: Research, Other

13 pages, 935 KiB

Open AccessReview

Computational Flow Dynamic Analysis in Left Atrial Appendage Thrombus Formation Risk: A Review

by Sara Valvez, Manuel Oliveira-Santos, Ana P. Piedade, Lino Gonçalves and Ana M. Amaro

Appl. Sci. 2023, 13(14), 8201; https://doi.org/10.3390/app13148201 - 14 Jul 2023

Cited by 4 | Viewed by 1905

Abstract

Atrial fibrillation (AF) is a common cardiac arrhythmia characterized by irregular and rapid electrical activity in the atria, leading to ineffective contraction and poor blood flow. More than 90% of the left atrial (LA) thrombi that cause thromboembolic events during atrial fibrillation (AF) develop in the left atrial appendage (LAA). AF modifies the hemodynamics of the left atrium, which can result in thrombosis of the LAA, systemic embolism, and stroke. The current options to reduce thromboembolic events are oral anticoagulation, surgical LAA exclusion, or percutaneous LAA occlusion. However, the mechanism underlying thrombus development in the LAA remains poorly understood. Computational fluid dynamics (CFD) analysis can be used to better understand the risk of thrombus formation and subsequent embolic events. CFD enables the simulation and visualization of blood flow patterns within the heart, including complex structures such as the LAA. Using CFD, researchers can analyze the hemodynamics of blood flow, identify areas of stagnation or turbulence, and predict the risk of thrombus formation. The correlation between blood flow dynamics, atrial fibrillation, and the risk of stroke has been highlighted by CFD studies investigating the underlying mechanism of thrombus formation in the LAA. This review study intends to provide a comprehensive overview of the factors involved in thrombus formation and their implications for clinical practice by synthesizing the insights acquired from these CFD studies. Full article

(This article belongs to the Special Issue Applied Biostatistics & Statistical Computing)

► Show Figures

Figure 1

Other

Jump to: Research, Review

14 pages, 2041 KiB

Open AccessSystematic Review

Association between Vitamin D Levels and Dental Caries: A Systematic Review and Dose-Response Meta-Analysis of Cross-Sectional Studies

by Mohammed Khalid Mahmood, Herve Tassery, Delphine Tardivo and Romain Lan

Appl. Sci. 2023, 13(17), 9883; https://doi.org/10.3390/app13179883 - 31 Aug 2023

Cited by 6 | Viewed by 3485

Abstract

Background and Aims: Previous observational studies found inconsistent associations between serum vitamin D levels and dental caries risk. A dose-response meta-analysis of cross-sectional studies was performed to investigate the association. Methods: To April 2023, the ISI Web of Science, PubMed, Scopus, and Google Scholar databases were searched for published papers. Finally, 13 cross-sectional studies were considered that provided odds ratios (ORs) with 95% confidence intervals (CIs) for dental caries in relation to serum vitamin D levels across all age groups. Two reviewers conducted a thorough screening of the studies, data extraction, bias risk assessment, and evidence quality. A random-effect model was used to assess the pooled estimated odd ratios (with 95% confidence intervals). A weighted mixed-effects dose-response meta-analysis in one stage was carried out. Results: Dental caries was significantly more likely to occur when serum vitamin D levels were low compared to high (OR: 1.41; 95% CI: 1.18, 1.68; GRADE = poor confidence). With a 10 nmol/L increase in serum vitamin D level, linear dose-response analysis showed a significant 3% (OR: 0.97; 95% CI: 0.96, 0.99) decrease in the likelihood of dental caries. Serum vitamin D levels and dental caries were found to be inversely correlated, with a significant dose-response relationship at levels greater than 78 nmol/L. Conclusion: This meta-analysis showed that vitamin D insufficiency was strongly associated with dental caries, and that a 10 nmol/L increase in blood 25(OH)D levels was linked to a 3% decrease in dental caries. However, the findings may have less clinical significance due to the uncertainty of the evidence. Full article

(This article belongs to the Special Issue Applied Biostatistics & Statistical Computing)

► Show Figures

Journal Menu

Journal Browser

Applied Biostatistics & Statistical Computing

Share This Special Issue

Special Issue Editors

Special Issue Information

Benefits of Publishing in a Special Issue

Related Special Issue

Published Papers (11 papers)

Research

Review

Other

Further Information

Guidelines

MDPI Initiatives

Follow MDPI