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Mathematics
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Mathematical Models and Computer Science Applied to Biology
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Mathematical Models and Computer Science Applied to Biology

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

Deadline for manuscript submissions: 10 April 2025 | Viewed by 5207

Special Issue Editor

Prof. Dr. Xiaoping Liu

E-Mail Website
Guest Editor
Hangzhou Institute for Advanced Study, University of Chinese Academy of Sciences, Hangzhou 310012, China
Interests: biostatistics; machine learning; computational biology; bioinformatics; system biology

Special Issue Information

Dear Colleagues,

High-throughput data techniques bring a large amount of biological data, such as gene expression, chromatin accessibility profiles, etc. Various mathematical methods and computer science techniques can be used to interpret and analyze these biological data, which can provide a more reliable and holistic depiction of biological mechanisms. Though related research has been done, due to the explosive growth of biomedical data at different scales, the research in related fields is still insufficient.

This Special Issue aims to provide a forum for exchanging ideas and tools among scientists, mathematicians, statisticians, biologists, computer scientists, and other domain experts. We invite original and review papers dedicated to network medicine, multi-layered network analysis, multiscale analysis, and other methods to contribute to analyzing the correlation between multi-scale data and provide a better explanation for complex diseases or biological processes. We would like to invite original research articles that provide new results on this subject.

Prof. Dr. Xiaoping Liu
Guest Editor

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 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

  • high-throughput data
  • network medicine
  • multi-layered network
  • multi-scale analysis

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

Published Papers (4 papers)

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Research

20 pages, 4580 KiB  
Article
Using Machine Learning and Natural Language Processing for Unveiling Similarities between Microbial Data
by Lucija Brezočnik, Tanja Žlender, Maja Rupnik and Vili Podgorelec
Mathematics 2024, 12(17), 2717; https://doi.org/10.3390/math12172717 - 30 Aug 2024
Viewed by 752
Abstract
Microbiota analysis can provide valuable insights in various fields, including diet and nutrition, understanding health and disease, and in environmental contexts, such as understanding the role of microorganisms in different ecosystems. Based on the results, we can provide targeted therapies, personalized medicine, or [...] Read more.
Microbiota analysis can provide valuable insights in various fields, including diet and nutrition, understanding health and disease, and in environmental contexts, such as understanding the role of microorganisms in different ecosystems. Based on the results, we can provide targeted therapies, personalized medicine, or detect environmental contaminants. In our research, we examined the gut microbiota of 16 animal taxa, including humans, as well as the microbiota of cattle and pig manure, where we focused on 16S rRNA V3-V4 hypervariable regions. Analyzing these regions is common in microbiome studies but can be challenging since the results are high-dimensional. Thus, we utilized machine learning techniques and demonstrated their applicability in processing microbial sequence data. Moreover, we showed that techniques commonly employed in natural language processing can be adapted for analyzing microbial text vectors. We obtained the latter through frequency analyses and utilized the proposed hierarchical clustering method over them. All steps in this study were gathered in a proposed microbial sequence data processing pipeline. The results demonstrate that we not only found similarities between samples but also sorted groups’ samples into semantically related clusters. We also tested our method against other known algorithms like the Kmeans and Spectral Clustering algorithms using clustering evaluation metrics. The results demonstrate the superiority of the proposed method over them. Moreover, the proposed microbial sequence data pipeline can be utilized for different types of microbiota, such as oral, gut, and skin, demonstrating its reusability and robustness. Full article
(This article belongs to the Special Issue Mathematical Models and Computer Science Applied to Biology)
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16 pages, 4538 KiB  
Article
Hidden Variable Discovery Based on Regression and Entropy
by Xingyu Liao and Xiaoping Liu
Mathematics 2024, 12(9), 1375; https://doi.org/10.3390/math12091375 - 30 Apr 2024
Viewed by 1141
Abstract
Inferring causality from observed data is crucial in many scientific fields, but this process is often hindered by incomplete data. The incomplete data can lead to mistakes in understanding how variables affect each other, especially when some influencing factors are not directly observed. [...] Read more.
Inferring causality from observed data is crucial in many scientific fields, but this process is often hindered by incomplete data. The incomplete data can lead to mistakes in understanding how variables affect each other, especially when some influencing factors are not directly observed. To tackle this problem, we’ve developed a new algorithm called Regression Loss-increased with Causal Intensity (RLCI). This approach uses regression and entropy analysis to uncover hidden variables. Through tests on various real-world datasets, RLCI has been proven to be effective. It can help spot hidden factors that may affect the relationship between variables and determine the direction of causal relationships. Full article
(This article belongs to the Special Issue Mathematical Models and Computer Science Applied to Biology)
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13 pages, 3213 KiB  
Article
A Joint Batch Correction and Adaptive Clustering Method of Single-Cell Transcriptomic Data
by Sijing An, Jinhui Shi, Runyan Liu, Jing Wang, Shuofeng Hu, Guohua Dong, Xiaomin Ying and Zhen He
Mathematics 2023, 11(24), 4901; https://doi.org/10.3390/math11244901 - 7 Dec 2023
Viewed by 1364
Abstract
Clustering analysis for single-cell RNA sequencing (scRNA-seq) data is essential for characterizing cellular heterogeneity. However, batch information caused by batch effects is often confused with the intrinsic biological information in scRNA-seq data, which makes accurate clustering quite challenging. A Deep Adaptive Clustering with [...] Read more.
Clustering analysis for single-cell RNA sequencing (scRNA-seq) data is essential for characterizing cellular heterogeneity. However, batch information caused by batch effects is often confused with the intrinsic biological information in scRNA-seq data, which makes accurate clustering quite challenging. A Deep Adaptive Clustering with Adversarial Learning method (DACAL) is proposed here. DACAL jointly optimizes the batch correcting and clustering processes to remove batch effects while retaining biological information. DACAL achieves batch correction and adaptive clustering without requiring manually specified cell types or resolution parameters. DACAL is compared with other widely used batch correction and clustering methods on human pancreas datasets from different sequencing platforms and mouse mammary datasets from different laboratories. The results demonstrate that DACAL can correct batch effects efficiently and adaptively find accurate cell types, outperforming competing methods. Moreover, it can obtain cell subtypes with biological meanings. Full article
(This article belongs to the Special Issue Mathematical Models and Computer Science Applied to Biology)
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15 pages, 3839 KiB  
Article
SCM Enables Improved Single-Cell Clustering by Scoring Consensus Matrices
by Yilin Yu and Juntao Liu
Mathematics 2023, 11(17), 3785; https://doi.org/10.3390/math11173785 - 3 Sep 2023
Cited by 2 | Viewed by 1214
Abstract
Single-cell clustering facilitates the identification of different cell types, especially the identification of rare cells. Preprocessing and dimensionality reduction are the two most commonly used data-processing methods and are very important for single-cell clustering. However, we found that different preprocessing and dimensionality reduction [...] Read more.
Single-cell clustering facilitates the identification of different cell types, especially the identification of rare cells. Preprocessing and dimensionality reduction are the two most commonly used data-processing methods and are very important for single-cell clustering. However, we found that different preprocessing and dimensionality reduction methods have very different effects on single-cell clustering. In addition, there seems to be no specific combination of preprocessing and dimensionality reduction methods that is applicable to all datasets. In this study, we developed a new algorithm for improving single-cell clustering results, called SCM. It first automatically searched for an optimal combination that corresponds to the best cell type clustering of a given dataset. It then defined a flexible cell-to-cell distance measure with data specificity for cell-type clustering. Experiments on ten benchmark datasets showed that SCM performed better than almost all the other seven popular clustering algorithms. For example, the average ARI improvement of SCM over the second best method SC3 even reached 29.31% on the ten datasets, which demonstrated its great potential in revealing cellular heterogeneity, identifying cell types, depicting cell functional states, inferring cellular dynamics, and other related research areas. Full article
(This article belongs to the Special Issue Mathematical Models and Computer Science Applied to Biology)
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