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Mathematics
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Network Biology and Machine Learning in Bioinformatics
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Network Biology and Machine Learning in Bioinformatics

A special issue of Mathematics (ISSN 2227-7390). This special issue belongs to the section "E3: Mathematical Biology".

Deadline for manuscript submissions: 30 September 2025 | Viewed by 7605

Special Issue Editors

Dr. Harun Pirim

E-Mail Website
Guest Editor
Industrial and Manufacturing Engineering, North Dakota State University, Fargo, ND 58105, USA
Interests: network clustering; network biology; mathematical programming; computational social science; network neuroscience
Dr. Mingao Yuan

E-Mail Website
Guest Editor
Department of Statistics, North Dakota State University, Fargo, ND 58105, USA
Interests: network data analysis; statistical inference; statistical machine learning
Prof. Dr. Kambiz Farahmand

E-Mail Website
Guest Editor
Industrial and Manufacturing Engineering, North Dakota State University, Fargo, ND 58105, USA
Interests: ergonomics design; healthcare; facilities and production layout (planning and management); human exposure and physiology simulation; ISO 9001 quality management system; productivity analysis and waste management; respiratory and life support system; lean manufacturing; safety and human factors engineering; manufacturing systems; simulation and modeling; operation and material management and strategic planning; nano-technology; computer network management

Special Issue Information

Dear Colleagues,

Machine learning (ML) applications in bioinformatics and network biology are two rapidly growing fields that are having a significant impact on informing and inferring biological phenomena. By combining the power of network biology with the analytical tools provided by ML, researchers are becoming more knowledgeable in the organization and function of biological systems. The applications of ML and network science in biology are numerous, including the prediction of protein–protein interactions, the identification of disease-associated genes, and the development of new therapies for diseases. As these fields continue to advance, we can expect to see many more exciting applications in the future.

This Special Issue aims to consolidate diverse usages of ML in the field of bioinformatics, with a particular focus on the implementation of network biology. We are seeking submissions on a variety of subjects, including (but not limited to) network biology, ML applications in bioinformatics, statistical inference in bioinformatics, protein–protein interaction networks, biological network visualization, network clustering, and multi-layered networks.

Dr. Harun Pirim
Dr. Mingao Yuan
Prof. Dr. Kambiz Farahmand
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 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

  • network biology
  • biostatistics
  • bioinformatics
  • machine learning applications in bioinformatics
  • statistical inference in bioinformatics
  • network clustering
  • network neuroscience
  • network data analysis
  • statistical machine learning
  • artificial intelligence
  • biological network
  • computational biology
  • systems biology
  • biological systems
  • network analysis
  • biological computing
  • biological simulation
  • mathematical biology

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Published Papers (5 papers)

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Research

21 pages, 3301 KiB  
Article
Decoding Colon Cancer Heterogeneity Through Integrated miRNA–Gene Network Analysis
by Qingcai He, Zhilong Mi, Tianyue Liu, Taihang Huang, Mao Li, Binghui Guo and Zhiming Zheng
Mathematics 2025, 13(6), 1020; https://doi.org/10.3390/math13061020 - 20 Mar 2025
Viewed by 247
Abstract
Colon adenocarcinoma (COAD) demonstrates significant clinical heterogeneity across disease stages, gender, and age groups, posing challenges for unified therapeutic strategies. This study establishes a multi-dimensional stratification framework through integrative analysis of miRNA–gene co-expression networks, employing the MRNETB algorithm coupled with Markov flow entropy [...] Read more.
Colon adenocarcinoma (COAD) demonstrates significant clinical heterogeneity across disease stages, gender, and age groups, posing challenges for unified therapeutic strategies. This study establishes a multi-dimensional stratification framework through integrative analysis of miRNA–gene co-expression networks, employing the MRNETB algorithm coupled with Markov flow entropy (MFE) centrality quantification. Analysis of TCGA-COAD cohorts revealed stage-dependent regulatory patterns centered on CDX2-hsa-miR-22-3p-MUC13 interactions, with progressive dysregulation mirroring tumor progression. Gender-specific molecular landscapes have emerged, characterized by predominant SLC26A3 expression in males and GPA33 enrichment in females, suggesting divergent pathogenic mechanisms between genders. Striking age-related disparities were observed, where early-onset cases exhibited molecular signatures distinct from conventional COAD, highlighted by marked XIST expression variations. Drug-target network analysis identified actionable candidates including CEACAM5-directed therapies and differentiation-modulating agents. Our findings underscore the critical need for heterogeneity-aware clinical decision-making, providing a roadmap for stratified intervention paradigms in precision oncology. Full article
(This article belongs to the Special Issue Network Biology and Machine Learning in Bioinformatics)
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15 pages, 297 KiB  
Article
Weighted Graph-Based Two-Sample Test via Empirical Likelihood
by Xiaofeng Zhao and Mingao Yuan
Mathematics 2024, 12(17), 2745; https://doi.org/10.3390/math12172745 - 4 Sep 2024
Viewed by 758
Abstract
In network data analysis, one of the important problems is determining if two collections of networks are drawn from the same distribution. This problem can be modeled in the framework of two-sample hypothesis testing. Several graph-based two-sample tests have been studied. However, the [...] Read more.
In network data analysis, one of the important problems is determining if two collections of networks are drawn from the same distribution. This problem can be modeled in the framework of two-sample hypothesis testing. Several graph-based two-sample tests have been studied. However, the methods mainly focus on binary graphs, and many real-world networks are weighted. In this paper, we apply empirical likelihood to test the difference in two populations of weighted networks. We derive the limiting distribution of the test statistic under the null hypothesis. We use simulation experiments to evaluate the power of the proposed method. The results show that the proposed test has satisfactory performance. Then, we apply the proposed method to a biological dataset. Full article
(This article belongs to the Special Issue Network Biology and Machine Learning in Bioinformatics)
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22 pages, 3394 KiB  
Article
Multi-View and Multimodal Graph Convolutional Neural Network for Autism Spectrum Disorder Diagnosis
by Tianming Song, Zhe Ren, Jian Zhang and Mingzhi Wang
Mathematics 2024, 12(11), 1648; https://doi.org/10.3390/math12111648 - 24 May 2024
Cited by 1 | Viewed by 1701
Abstract
Autism Spectrum Disorder (ASD) presents significant diagnostic challenges due to its complex, heterogeneous nature. This study explores a novel approach to enhance the accuracy and reliability of ASD diagnosis by integrating resting-state functional magnetic resonance imaging with demographic data (age, gender, and IQ). [...] Read more.
Autism Spectrum Disorder (ASD) presents significant diagnostic challenges due to its complex, heterogeneous nature. This study explores a novel approach to enhance the accuracy and reliability of ASD diagnosis by integrating resting-state functional magnetic resonance imaging with demographic data (age, gender, and IQ). This study is based on improving the spectral graph convolutional neural network (GCN). It introduces a multi-view attention fusion module to extract useful information from different views. The graph’s edges are informed by demographic data, wherein an edge-building network computes weights grounded in demographic information, thereby bolstering inter-subject correlation. To tackle the challenges of oversmoothing and neighborhood explosion inherent in deep GCNs, this study introduces DropEdge regularization and residual connections, thus augmenting feature diversity and model generalization. The proposed method is trained and evaluated on the ABIDE-I and ABIDE-II datasets. The experimental results underscore the potential of integrating multi-view and multimodal data to advance the diagnostic capabilities of GCNs for ASD. Full article
(This article belongs to the Special Issue Network Biology and Machine Learning in Bioinformatics)
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9 pages, 320 KiB  
Article
Using Physics-Informed Neural Networks (PINNs) for Tumor Cell Growth Modeling
by José Alberto Rodrigues
Mathematics 2024, 12(8), 1195; https://doi.org/10.3390/math12081195 - 16 Apr 2024
Cited by 4 | Viewed by 2638
Abstract
This paper presents a comprehensive investigation into the applicability and performance of two prominent growth models, namely, the Verhulst model and the Montroll model, in the context of modeling tumor cell growth dynamics. Leveraging the power of Physics-Informed Neural Networks (PINNs), we aim [...] Read more.
This paper presents a comprehensive investigation into the applicability and performance of two prominent growth models, namely, the Verhulst model and the Montroll model, in the context of modeling tumor cell growth dynamics. Leveraging the power of Physics-Informed Neural Networks (PINNs), we aim to assess and compare the predictive capabilities of these models against experimental data obtained from the growth patterns of tumor cells. We employed a dataset comprising detailed measurements of tumor cell growth to train and evaluate the Verhulst and Montroll models. By integrating PINNs, we not only account for experimental noise but also embed physical insights into the learning process, enabling the models to capture the underlying mechanisms governing tumor cell growth. Our findings reveal the strengths and limitations of each growth model in accurately representing tumor cell proliferation dynamics. Furthermore, the study sheds light on the impact of incorporating physics-informed constraints on the model predictions. The insights gained from this comparative analysis contribute to advancing our understanding of growth models and their applications in predicting complex biological phenomena, particularly in the realm of tumor cell proliferation. Full article
(This article belongs to the Special Issue Network Biology and Machine Learning in Bioinformatics)
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15 pages, 3779 KiB  
Article
CNVbd: A Method for Copy Number Variation Detection and Boundary Search
by Jingfen Lan, Ziheng Liao, A. K. Alvi Haque, Qiang Yu, Kun Xie and Yang Guo
Mathematics 2024, 12(3), 420; https://doi.org/10.3390/math12030420 - 27 Jan 2024
Cited by 1 | Viewed by 1725
Abstract
Copy number variation (CNV) has been increasingly recognized as a type of genomic/genetic variation that plays a critical role in driving human diseases and genomic diversity. CNV detection and analysis from cancer genomes could provide crucial information for cancer diagnosis and treatment. There [...] Read more.
Copy number variation (CNV) has been increasingly recognized as a type of genomic/genetic variation that plays a critical role in driving human diseases and genomic diversity. CNV detection and analysis from cancer genomes could provide crucial information for cancer diagnosis and treatment. There still remain considerable challenges in the control-free calling of CNVs accurately in cancer analysis, although advances in next-generation sequencing (NGS) technology have been inspiring the development of various computational methods. Herein, we propose a new read-depth (RD)-based approach, called CNVbd, to explore CNVs from single tumor samples of NGS data. CNVbd assembles three statistics drawn from the density peak clustering algorithm and isolation forest algorithm based on the denoised RD profile and establishes a back propagation neural network model to predict CNV bins. In addition, we designed a revision process and a boundary search algorithm to correct the false-negative predictions and refine the CNV boundaries. The performance of the proposed method is assessed on both simulation data and real sequencing datasets. The analysis shows that CNVbd is a very competitive method and can become a robust and reliable tool for analyzing CNVs in the tumor genome. Full article
(This article belongs to the Special Issue Network Biology and Machine Learning in Bioinformatics)
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