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A special issue of Applied Sciences (ISSN 2076-3417). This special issue belongs to the section "Applied Biosciences and Bioengineering".

Deadline for manuscript submissions: 30 May 2026 | Viewed by 3297

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Special Issue Editors

Dr. Kai Hu

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Guest Editor

Department of Molecular, Cell and Cancer Biology, University of Massachusetts Chan Medical School, 364 Plantation Street, Worcester, MA 01605, USA
Interests: applied bioinformatics; analytical pipeline development; single-cell data analysis; applied machine learning

Dr. Haibo Liu

E-Mail Website
Guest Editor

Department of Molecular, Cell and Cancer Biology, University of Massachusetts Chan Medical School, 364 Plantation Street, Worcester, MA 01605, USA
Interests: bioinformatics in animals

Special Issue Information

Dear Colleagues,

We invite you to contribute a paper to the Special Issue “Research on Computational Biology and Bioinformatics” of Applied Sciences (https://www.mdpi.com/journal/applsci, Impact Factor: 2.5, CiteScore: 5.3).

In recent decades, we have witnessed an unprecedented surge in the volume, diversity, and complexity of biological data, driven by significant advancements in high-throughput technologies such as next-generation sequencing, long-read sequencing, and single-cell techniques. This rapid expansion of data presents both challenges and opportunities to the broad research community; thus, easy-to-use, efficient, and novel computational biological and bioinformatics tools are in great need to manage, analyze, and interpret this wealth of data.

This Special Issue will highlight cutting-edge research across all aspects of Computational Biology and Bioinformatics. We welcome original contributions on topics including, but not limited to, the following:

(1) Development of novel algorithms and tools for analyzing existing or emerging biological data;

(2) Application of innovative computational biology methods or bioinformatics strategies to address specific biological questions;

(3) Benchmarking studies or comprehensive reviews of contemporary computational biology tools and algorithms.

We look forward to receiving your submissions.

Dr. Kai Hu
Dr. Haibo Liu
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. Applied Sciences 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 2400 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

computational biology
bioinformatics
biological data analysis
high-throughput technologies

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Published Papers (5 papers)

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Research

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19 pages, 728 KB

Open AccessArticle

Deterministic Modeling of Muller’s Ratchet Effect in Populations Evolving in an Environment of Finite Capacity

by Wojciech Łabaj, Jarosław Gil, Mateusz Kania, Ewa Lach, Agnieszka Szczęsna and Andrzej Polański

Appl. Sci. 2025, 15(20), 11090; https://doi.org/10.3390/app152011090 - 16 Oct 2025

Abstract

We study how small harmful mutations spread in populations that reproduce asexually. This process is known as Muller’s ratchet—it means that even though these mutations are damaging, they can still build up over generations. To explore this, we use a mathematical model that describes how such mutations move through a population living in an environment with limited resources. We model Muller’s ratchet deterministically using differential equations, incorporating modifications that account for extinction risk of small mutation classes. We analyze two modifications: a published cutoff modification and a more flexible exponential modification. We show that the exponential modification better matches stochastic simulations over specific parameter ranges. Full article

(This article belongs to the Special Issue Research on Computational Biology and Bioinformatics)

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11 pages, 415 KB

Open AccessArticle

Optimal Cutoff Value of the Tumor Mutation Burden for Immune Checkpoint Inhibitors: A Lesson from 175 Pembrolizumab-Treated Cases Among 6403 Breast Cancer Patients

by Hinano Nishikubo, Kyoka Kawabata, Dongheng Ma, Tomoya Sano, Daiki Imanishi, Takashi Sakuma, Koji Maruo, Canfeng Fan, Yurie Yamamoto and Masakazu Yashiro

Appl. Sci. 2025, 15(18), 10173; https://doi.org/10.3390/app151810173 - 18 Sep 2025

Viewed by 445

Abstract

The immune checkpoint inhibitor pembrolizumab is effective for the treatment of recurrent cancer with a tumor mutation burden-high (TMB-high) status. Globally, the cutoff value of TMB-high has been set as ≥10 mut/Mb, but the optimal cutoff value of TMB for treating breast cancer (BC) with pembrolizumab has not been identified. We re-evaluated the optimal cutoff value of TMB-high status in BC by using the clinical dataset from Japan’s Center for Cancer Genomics and Advanced Therapeutics (C-CAT) profiling database. We extracted 6403 BC cases that had been enrolled from the C-CAT database of 101,231 cases of various types of cancers. Of all 6403 BC cases, 683 (10.7%) showed TMB ≥ 10 mut/Mb as TMB-high. Of the 683 TMB-high cases, 175 were administered pembrolizumab. The receiver operating characteristic curve indicated that for treating BC with pembrolizumab, a TMB ≥ 18.5 mut/Mb was an adequate cutoff regarding sensitivity and specificity. The BC patients’ overall response rate was 21.4%. The disease control rate was 42.9%. The probability of time-to-treatment failure was significantly better in the BC cases with TMB ≥ 18.5 mut/Mb versus those with TMB < 18.5 mut/Mb (p = 0.007). These findings suggested that the optimal cutoff value of the TMB for treating breast cancer with pembrolizumab might be ≥18.5 mut/Mb. Full article

(This article belongs to the Special Issue Research on Computational Biology and Bioinformatics)

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19 pages, 2679 KB

Open AccessArticle

Enrichment of Z-DNA-Forming Sequences Within Super-Enhancers: A Computational and Population-Based Study

by Yulia V. Makus, German A. Ashniev, Alexey V. Orlov, Petr I. Nikitin, Zoia G. Zaitseva and Natalia N. Orlova

Appl. Sci. 2025, 15(9), 5113; https://doi.org/10.3390/app15095113 - 4 May 2025

Viewed by 1373

Abstract

Super-enhancers (SEs) orchestrate high-level transcription by integrating multiple regulatory elements and signals. Although chromatin accessibility and transcription factor binding within SEs are extensively studied, the role of non-canonical DNA structures, particularly Z-DNA, remains underexplored. In this study, genome-wide predictions of Z-DNA-forming sequences (generated by the Z-DNA-BERT model) were applied to systematically investigate their distribution within typical enhancers and SEs across multiple human cancer cell lines. Statistically significant enrichment of Z-DNA sequences within SE regions, compared to random genomic controls, was observed. Furthermore, genetic variants overlapping these Z-DNA regions, identified using data from the 1000 Genomes Project, were found to alter binding motifs of the SP/KLF transcription factor family. These mutations exhibited population-specific clustering and overlapped previously reported pathogenic copy-number variations (CNVs) associated with neurodevelopmental disorders, potentially affecting transcription factor binding motifs related to neuronal growth and differentiation pathways. Population-level phylogenetic analysis revealed distinct clustering patterns of these variants, suggesting frequency-specific genetic architecture. Overall, the computational findings indicate that Z-DNA structures within super-enhancers might play regulatory roles and potentially influence population-specific genetic variation, highlighting specific genomic targets and providing new avenues for future experimental research. Full article

(This article belongs to the Special Issue Research on Computational Biology and Bioinformatics)

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31 pages, 16368 KB

Open AccessArticle

Bioinformatics-Based Management of Vitellogenin-like Protein’s Role in Pathogen Defense in Nicotiana tabacum L.

by Hanan Maoz, Amir Elalouf and Amit Yaniv Rosenfeld

Appl. Sci. 2025, 15(8), 4463; https://doi.org/10.3390/app15084463 - 18 Apr 2025

Viewed by 856

Abstract

The primary objective of this study was to identify and characterize pathogen defense proteins in the Nicotiana tabacum L. proteome, focusing on their structural, functional, and evolutionary properties, as well as their interactions with pathogen-derived molecules. Specifically, we aimed to comprehensively analyze the proteome to pinpoint potential uncharacterized defense-related protein that has emerging roles in immune responses and antioxidant activity across plants and animals. Through integrated computational approaches, we determined evolutionary relationships, and structural modeling of the selected protein was performed using different modeling software, followed by validation through multiple metrics, including stereochemical checks (Ramachandran plot), MolProbity analysis, and Z-scores. We further investigated the functional binding regions or interaction sites. We performed molecular docking to investigate the molecular interactions between selected proteins and pathogen-associated molecular patterns (PAMPs), specifically β-glucan and peptidoglycan (PGN), to elucidate their defensive mechanisms. Last, normal mode analysis (NMA), molecular dynamics simulation (MDS), and post-simulation analyses were employed to evaluate the stability and mobility of the protein–ligand complexes. Uncharacterized vitellogenin-like protein (VLP: ID A0A1S4CXB2) with the potential defense domain chosen because of its predicted immune-related features, stress response patterns, and unknown pathogen role at new immunity functions. Phylogenetic analysis revealed significant sequence homology with VLPs from other members of the Solanaceae family. Structural modeling showed a high-quality model, with docking studies indicating a stronger affinity for PGN (−10.16 kcal/mol) and β-glucan (−7.19 kcal/mol), highlighting its potential involvement in pathogen defense. NMA, MDS, and post-simulation analyses revealed that PGN exhibits more substantial binding stability and more extensive interactions with VLP than β-glucan. Our findings confirmed that VLPs in N. tabacum may function as pattern recognition receptors (PRRs), capable of recognizing and responding to pathogens by activating immune signaling pathways. Future experimental validation of these interactions could further elucidate the role of VLPs in plant defense and their potential application in biotechnological approaches for sustainable agriculture. Full article

(This article belongs to the Special Issue Research on Computational Biology and Bioinformatics)

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Review

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29 pages, 1198 KB

Open AccessReview

Artificial Intelligence and Bioinformatics in the Malignant Progression of Gastric Cancer

by Tasuku Matsuoka and Masakazu Yashiro

Appl. Sci. 2025, 15(20), 11092; https://doi.org/10.3390/app152011092 - 16 Oct 2025

Abstract

Gastric cancer (GC) is characterized by heterogeneity and complexity and remains one of the leading causes of cancer-related deaths. The molecular mechanisms underlying carcinogenesis and the progression of GC have been central to scientific research and urgently need to be elucidated. With the potent development of next-generation sequencing technologies, a vast amount of bioinformatic data—including genomics, epigenomics, transcriptomics, proteomics, and metabolomics—has been accumulated, providing an extraordinary prospect to explore the heterogeneity and intricacy of GC. Nevertheless, the enormous amount of data created by bioinformatics analyses presents considerable analytical challenges. The application of artificial intelligence (AI), including machine learning and deep learning, has emerged as a powerful resolution to these challenges, obtaining useful information from exponential omics data, particularly in GC. The integration of AI with multi-omics approaches in GC research offers novel insights and powerful tools for gaining a deeper understanding of cancer’s complexities. This article reviews the latest research and progress of AI and bioinformatics analysis in GC oncology over the past several years, focusing on the landscape of GC carcinogenesis, progression, and metastasis. We also discuss the current challenges for improving performance and highlight future directions for more precise and effective treatments for GC patients. Full article

(This article belongs to the Special Issue Research on Computational Biology and Bioinformatics)

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