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Special Issue "Current Research in Biostatistics"

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A special issue of Mathematics (ISSN 2227-7390). This special issue belongs to the section "Mathematical Biology".

Deadline for manuscript submissions: 30 September 2023 | Viewed by 4013

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Special Issue Editors

Dr. Miguel Ángel Montero-Alonso

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Guest Editor

Department of Statistic and Operational Research, University of Granada, 18016 Granada, Spain
Interests: inference in diagnostic models; biostatistics; statistic elearning; data science; medical statistics

Prof. Dr. Juan De Dios Luna del Castillo

E-Mail Website
Guest Editor

Department of Biostatistic, University of Granada, 18071 Granada, Spain
Interests: predictive models; inference in diagnostic models; biostatistics; scale validation; teaching biostatistics

Special Issue Information

Dear Colleagues,

Biostatistics involves the application of statistics in the biomedical and health sciences. Statistics provides rigorous methodology with which to address typical medical or health problems.

This Special Issue aims to present the developing applications of biostatistics in various areas. Such areas include, but are not limited to:

Diagnostic or prognostic models.
Causal inference.
Analysis of large databases: data fusion.
Missing data.
Teaching statistics in the health sciences.
Stochastic models in biology and health sciences.
Spatio-temporal distribution of disease.
Measures of association in 2x2 tables.
Development of computer packages for biostatistics.
Experimental designs.
Controlled clinical trails.
Information fusion.
Combination of observational databases.
Estimation of prevalence and incidence under different sampling schemes.

All articles should present a problem statement, the methodology used to solve it and a resolution.

Dr. Miguel Ángel Montero-Alonso
Prof. Dr. Juan De Dios Luna del Castillo
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. Mathematics 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 2100 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

diagnostic models
causal inference
computational biostatistics
measures of association
prognostic models

Published Papers (5 papers)

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Research

Open AccessArticle

Scrambling Reports: New Estimators for Estimating the Population Mean of Sensitive Variables

Pablo O. Juárez-Moreno

Agustín Santiago-Moreno

José M. Sautto-Vallejo

and

Carlos N. Bouza-Herrera

Mathematics 2023, 11(11), 2572; https://doi.org/10.3390/math11112572 - 04 Jun 2023

Viewed by 200

Abstract

Warner proposed a methodology called randomized response techniques, which, through the random scrambling of sensitive variables, allows the non-response rate to be reduced and the response bias to be diminished. In this document, we present a randomized response technique using simple random sampling. The scrambling of the sensitive variable is performed through the selection of a report R_i, i = 1,2,3. In order to evaluate the accuracy and efficiency of the proposed estimators, a simulation was carried out with two databases, where the sensitive variables are the destruction of poppy crops in Guerrero, Mexico, and the age at first sexual intercourse. The results show that more accurate estimates are obtained with the proposed model. Full article

(This article belongs to the Special Issue Current Research in Biostatistics)

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Open AccessArticle

Diagnosing Vascular Aging Based on Macro and Micronutrients Using Ensemble Machine Learning

Carmen Patino-Alonso

Marta Gómez-Sánchez

Leticia Gómez-Sánchez

Emiliano Rodríguez-Sánchez

Cristina Agudo-Conde

Luis García-Ortiz

and

Manuel A Gómez-Marcos

Mathematics 2023, 11(7), 1645; https://doi.org/10.3390/math11071645 - 29 Mar 2023

Viewed by 470

Abstract

The influence of dietary components on vascular dysfunction and aging is unclear. This study therefore aims to propose a model to predict the influence of macro and micronutrients on accelerated vascular aging in a Spanish population without previous cardiovascular disease. This cross-sectional study involved a total of 501 individuals aged between 35 and 75 years. Carotid-femoral pulse wave velocity (cfPWV) was measured using a Sphygmo Cor^® device. Carotid intima-media thickness (IMTc) was measured using a Sonosite Micromax^® ultrasound machine. The Vascular Aging Index (VAI) was estimated according to VAI = (LN (1.09) × 10 cIMT + LN (1.14) × cfPWV) 39.1 + 4.76. Vascular aging was defined considering the presence of a vascular lesion and the p75 by age and sex of VAI following two steps: Step 1: subjects were labelled as early vascular aging (EVA) if they had a peripheral arterial disease or carotid artery lesion. Step 2: they were classified as EVA if the VAI value was >p75 and as normal vascular aging (NVA) if it was ≤p75. To predict the model, we used machine learning algorithms to analyse the association between macro and micronutrients and vascular aging. In this article, we proposed the AdXGRA model, a stacked ensemble learning model for diagnosing vascular aging from macro and micronutrients. The proposed model uses four classifiers, AdaBoost (ADB), extreme gradient boosting (XGB), generalized linear model (GLM), and random forest (RF) at the first level, and then combines their predictions by using a second-level multilayer perceptron (MLP) classifier to achieve better performance. The model obtained an accuracy of 68.75% in prediction, with a sensitivity of 66.67% and a specificity of 68.79%. The seven main variables related to EVA in the proposed model were sodium, waist circumference, polyunsaturated fatty acids (PUFA), monounsaturated fatty acids (MUFA), total protein, calcium, and potassium. These results suggest that total protein, PUFA, and MUFA are the macronutrients, and calcium and potassium are the micronutrients related to EVA. Full article

(This article belongs to the Special Issue Current Research in Biostatistics)

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Open AccessArticle

DeepLabv3+-Based Segmentation and Best Features Selection Using Slime Mould Algorithm for Multi-Class Skin Lesion Classification

and

Mathematics 2023, 11(2), 364; https://doi.org/10.3390/math11020364 - 10 Jan 2023

Cited by 2 | Viewed by 1008

Abstract

The development of abnormal cell growth is caused by different pathological alterations and some genetic disorders. This alteration in skin cells is very dangerous and life-threatening, and its timely identification is very essential for better treatment and safe cure. Therefore, in the present article, an approach is proposed for skin lesions’ segmentation and classification. So, in the proposed segmentation framework, pre-trained Mobilenetv2 is utilised in the act of the back pillar of the DeepLabv3+ model and trained on the optimum parameters that provide significant improvement for infected skin lesions’ segmentation. The multi-classification of the skin lesions is carried out through feature extraction from pre-trained DesneNet201 with N × 1000 dimension, out of which informative features are picked from the Slim Mould Algorithm (SMA) and input to SVM and KNN classifiers. The proposed method provided a mean ROC of 0.95 ± 0.03 on MED-Node, 0.97 ± 0.04 on PH2, 0.98 ± 0.02 on HAM-10000, and 0.97 ± 0.00 on ISIC-2019 datasets. Full article

(This article belongs to the Special Issue Current Research in Biostatistics)

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Open AccessArticle

DExMA: An R Package for Performing Gene Expression Meta-Analysis with Missing Genes

Juan Antonio Villatoro-García

Jordi Martorell-Marugán

Daniel Toro-Domínguez

Yolanda Román-Montoya

Pedro Femia

and

Pedro Carmona-Sáez

Mathematics 2022, 10(18), 3376; https://doi.org/10.3390/math10183376 - 17 Sep 2022

Cited by 1 | Viewed by 945

Abstract

Meta-analysis techniques allow researchers to jointly analyse different studies to determine common effects. In the field of transcriptomics, these methods have gained popularity in recent years due to the increasing number of datasets that are available in public repositories. Despite this, there is a limited number of statistical software packages that implement proper meta-analysis functionalities for this type of data. This article describes DExMA, an R package that provides a set of functions for performing gene expression meta-analyses, from data downloading to results visualization. Additionally, we implemented functions to control the number of missing genes, which can be a major issue when comparing studies generated with different analytical platforms. DExMA is freely available in the Bioconductor repository. Full article

(This article belongs to the Special Issue Current Research in Biostatistics)

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Open AccessArticle

Preliminary Results on the Preinduction Cervix Status by Shear Wave Elastography

Jorge Torres

María Muñoz

María Del Carmen Porcel

Sofía Contreras

Francisca Sonia Molina

Guillermo Rus

Olga Ocón-Hernández

and

Juan Melchor

Mathematics 2022, 10(17), 3164; https://doi.org/10.3390/math10173164 - 02 Sep 2022

Cited by 1 | Viewed by 751

Abstract

The mechanical status of the cervix is a key physiological element during pregnancy. By considering a successful induction when the active phase of labor is achieved, mapping the mechanical properties of the cervix could have predictive potential for the management of induction protocols. In this sense, we performed a preliminary assessment of the diagnostic value of using shear wave elastography before labor induction in 54 women, considering the pregnancy outcome and Cesarean indications. Three anatomical cervix regions and standard methods, such as cervical length and Bishop score, were compared. To study the discriminatory power of each diagnostic method, a receiver operating characteristic curve was generated. Differences were observed using the external os region and cervical length in the failure to enter the active phase group compared to the vaginal delivery group (p < 0.05). The area under the ROC curve resulted in 68.9%, 65.2% and 67.2% for external os, internal os and cervix box using elastography, respectively, compared to 69.5% for cervical length and 62.2% for Bishop score. External os elastography values have shown promise in predicting induction success. This a priori information could be used to prepare a study with a larger sample size, which would reduce the effect of any bias selection and increase the predictive power of elastography compared to other classical techniques. Full article

(This article belongs to the Special Issue Current Research in Biostatistics)

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