Bioinformatics and Mathematical Modelling

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

Deadline for manuscript submissions: 31 March 2025 | Viewed by 623

Special Issue Editors


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Guest Editor
School of Mathematics and Statistics, Shandong University, Weihai 264209, China
Interests: bioinformatics; algorithm design; PPI prediction

E-Mail Website
Guest Editor
Research Center for Mathematics and Interdisciplinary Sciences, Shandong University, Qingdao 266237, China
Interests: combinatorial optimization; bioinformatics algorithms; transcriptomics; genome assembly

Special Issue Information

Dear Colleagues,

As common biomolecules within living organisms, proteins play pivotal roles in nearly all biological processes through interactions with other biomolecules. Algorithms in bioinformatics, by integrating mathematical algorithms, machine learning, and biotechnology can predict various properties such as binding sites, affinity, complex structures, and peptide functions. These multifaceted approaches explore the mechanisms of protein functions in biological processes from different perspectives, potentially offering new insights for disease detection and drug development. We are pleased to invite you to participate in research on proteins.

With the rapid advancement of biotechnology, researchers can now utilize experimental methods to systematically unravel the intricate structures of biomolecules on a large scale, necessitating the urgent need to develop new algorithms for the effective analysis. This Special Issue aims to predict properties of proteins, such as binding sites and complex structures, based on large-scale primary sequence and tertiary structure data. This initiative will contribute to enhancing prediction accuracy and improving the scalability of biomedical datasets. By gaining a more comprehensive understanding of proteins involved in biological processes from different ligands and various properties, this endeavor aims to provide more accurate, efficient, and direct guidance for clinical medicine.

With the interdisciplinary nature of this Special Issue, we invite researchers from different areas of bioinformatics, mathematics, statistics, computer science, machine learning, and sub-disciplines of bioinformatics such as computational biology to submit manuscripts. In this Special Issue, original research articles and reviews are welcome. Research areas may include (but not limited to) the following:

  • Prediction of protein-ligand binding site
  • Protein-ligand binding affinity prediction
  • Protein-ligand complex structure prediction
  • Peptide function prediction

I look forward to receiving your contributions.

Dr. Juntao Liu
Dr. Ting Yu
Guest Editors

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Keywords

  • protein
  • primary sequence
  • tertiary structure
  • binding sites
  • binding affinity
  • complex structure
  • peptide function

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Published Papers (1 paper)

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Research

14 pages, 26460 KiB  
Article
TF-BAPred: A Universal Bioactive Peptide Predictor Integrating Multiple Feature Representations
by Zhenming Wu, Xiaoyu Guo, Yangyang Sun, Xiaoquan Su and Jin Zhao
Mathematics 2024, 12(22), 3618; https://doi.org/10.3390/math12223618 - 20 Nov 2024
Viewed by 208
Abstract
Bioactive peptides play essential roles in various biological processes and hold significant therapeutic potential. However, predicting the functions of these peptides is challenging due to their diversity and complexity. Here, we develop TF-BAPred, a framework for universal peptide prediction incorporating multiple feature representations. [...] Read more.
Bioactive peptides play essential roles in various biological processes and hold significant therapeutic potential. However, predicting the functions of these peptides is challenging due to their diversity and complexity. Here, we develop TF-BAPred, a framework for universal peptide prediction incorporating multiple feature representations. TF-BAPred feeds original peptide sequences into three parallel modules: a novel feature proposed in this study called FVG extracts the global features of each peptide sequence; an automatic feature recognition module based on a temporal convolutional network extracts the temporal features; and a module integrates multiple widely used features such as AAC, DPC, BPF, RSM, and CKSAAGP. In particular, FVG constructs a fixed-size vector graph to represent the global pattern by capturing the topological structure between amino acids. We evaluated the performance of TF-BAPred and other peptide predictors on different types of peptides, including anticancer peptides, antimicrobial peptides, and cell-penetrating peptides. The benchmarking tests demonstrate that TF-BAPred displays strong generalization and robustness in predicting various types of peptide sequences, highlighting its potential for applications in biomedical engineering. Full article
(This article belongs to the Special Issue Bioinformatics and Mathematical Modelling)
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