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Emerging Trends in Computational Biology and Bioinformatics

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

Deadline for manuscript submissions: closed (31 July 2025) | Viewed by 1486

Special Issue Editor


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Guest Editor
Department of Computer Science, Florida State University, Tallahassee, FL 32306, USA
Interests: single-cell DNA sequencing; mathematical modeling of a biological problem; phylogeny; spatial transcriptome

Special Issue Information

Dear Colleagues,

In the era of massively parallel sequencing, single-cell sequencing, spatial transcriptome, as well as multi-omics data integration, there are a myriad of opportunities in computational biology and bioinformatics, although computational challenges still exist. In this Special Issue, we invite you to contribute your latest work on one or several of the following computational/mathematical approaches:

  • Graph Theory;
  • Statistical Learning;
  • Bayesian Inference ;
  • Optimization Parameter Estimate;
  • Deep learning.

Your work may address one or several of the following areas:

  1. Single-cell DNA/RNA sequencing analysis;
  2. Spatial transcriptome;
  3. Multi-omics;
  4. Mutations calling on single-cell DNA/RNA sequencing data and sc-ATAC data;
  5. Personalized medicine.

In conclusion, any contribution that utilizes mathematical and computational approaches to gain a more comprehensive understanding of the biological systems or proposes a new computational tool to manipulate the biological data produced by the new technologies will be appreciated. We look forward to seeing your proposed work.

Dr. Xian F. Mallory
Guest Editor

Liting Zhang
Guest Editor Assistant

Manuscript Submission Information

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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

  • graph theory
  • statistical learning
  • Bayesian inference
  • optimization parameter estimate
  • deep learning
  • single-cell sequencing
  • spatial transcriptome
  • multi-omics
  • personalized medicine
  • mutation calling

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Published Papers (1 paper)

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Research

19 pages, 20375 KB  
Article
SCGclust: Single-Cell Graph Clustering Using Graph Autoencoders That Integrate SNVs and CNAs
by Teja Potu, Yunfei Hu, Judy Wang, Hongmei Chi, Rituparna Khan, Srinija Dharani, Jingchao Ni, Liting Zhang, Xin Maizie Zhou and Xian Mallory
Mathematics 2026, 14(1), 46; https://doi.org/10.3390/math14010046 - 23 Dec 2025
Viewed by 731
Abstract
Intra-tumor heterogeneity (ITH) is a compounding factor for cancer prognoses and treatment. Single-cell DNA sequencing (scDNA-seq) provides cellular resolution of the variations in a cell and has been widely used to study cancer progression and the responses to drugs and treatments. While low-coverage [...] Read more.
Intra-tumor heterogeneity (ITH) is a compounding factor for cancer prognoses and treatment. Single-cell DNA sequencing (scDNA-seq) provides cellular resolution of the variations in a cell and has been widely used to study cancer progression and the responses to drugs and treatments. While low-coverage scDNA-seq technologies typically provide a large number of cells, accurate cell clustering is essential for effectively characterizing the ITH. The existing cell clustering methods are typically based on either single-nucleotide variations (SNV) or copy number alterations (CNA), without leveraging both signals together. Since both SNVs and CNAs are indicative of cell subclonality, in this paper, we designed a robust cell-clustering tool that integrates both signals using a graph autoencoder. Our model co-trains the graph autoencoder and a graph convolutional network (GCN) to guarantee meaningful clustering results and to prevent all cells from collapsing into a single cluster. Given the low-dimensional embedding generated by the autoencoder, we adopted a Gaussian mixture model (GMM) to further cluster the cells. We evaluated our method on eight simulated datasets and a real cancer sample. Our results demonstrate that our method consistently achieved higher V-measure scores compared to SBMClone, an SNV-based method, and a K-means method that relies solely on CNA signals. These findings highlight the advantage of integrating both SNV and CNA signals within a graph autoencoder framework for accurate cell clustering. Full article
(This article belongs to the Special Issue Emerging Trends in Computational Biology and Bioinformatics)
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