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Mathematics
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Computational Intelligence for Bioinformatics
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Computational Intelligence for Bioinformatics

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

Deadline for manuscript submissions: 31 January 2027 | Viewed by 3082

Special Issue Editors

Dr. Xiaoquan Su

E-Mail Website
Guest Editor
College of Computer Science and Technology, Qingdao University, Qingdao, China
Interests: microbiome; metagenome; machine learning; algorithm
Dr. Min Li

E-Mail Website
Guest Editor
School of Software, Xinjiang University, Urumqi, China
Interests: medical image processing; bioinformatics; algorithm

Special Issue Information

Dear Colleagues,

Rapid advancements in computational intelligence are transforming the field of bioinformatics, enabling researchers to analyze complex biological data with unprecedented accuracy and efficiency. This Mathematics Special Issue is dedicated to exploring novel computational approaches—including machine learning, deep learning, evolutionary algorithms and mathematical modeling—that drive innovation in bioinformatics. We welcome contributions that address key challenges in genomics, proteomics, systems biology and medical informatics, with a focus on algorithm development, data integration and predictive analytics. By bringing together cutting-edge research, this issue aims to highlight the synergy between computational intelligence and life sciences, fostering new insights and technological breakthroughs in biomedical research.

Dr. Xiaoquan Su
Dr. Min Li
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 250 words) can be sent to the Editorial Office for assessment.

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

  • computational intelligence
  • bioinformatics
  • machine learning
  • mathematical modeling
  • biomedical data analysis

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Published Papers (3 papers)

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Research

28 pages, 12832 KB  
Article
PLB-GPT: Potato Late Blight Prediction with Generative Pretrained Transformer and Optimizing
by Peisen Yuan, Ye Xia, Mengjian Dong, Cheng He, Dingfei Liu, Yixi Tan and Suomeng Dong
Mathematics 2026, 14(2), 225; https://doi.org/10.3390/math14020225 - 7 Jan 2026
Viewed by 377
Abstract
Potato late blight is a devastating disease and threatening global potato production, necessitating accurate early prediction for effective management and yield enhancement.This paper presents the PLB-GPT, a novel generative pre-trained transformer-based model built on GPT-2 architecture, designed to forecast late blight outbreaks using [...] Read more.
Potato late blight is a devastating disease and threatening global potato production, necessitating accurate early prediction for effective management and yield enhancement.This paper presents the PLB-GPT, a novel generative pre-trained transformer-based model built on GPT-2 architecture, designed to forecast late blight outbreaks using meteorological data. Our method is trained and evaluated on a real-world dataset encompassing temperature, humidity, atmospheric pressure, and other climatic variables from diverse regions of China; PLB-GPT demonstrates state-of-the-art performance. The framework of PLB-GPT employs advanced fine-tuning strategies, including Linear Probing, Full Fine-Tuning, and a novel two-stage method, effectively applied across different time windows (1-day, 3-day, 5-day, 7-day). The model achieves an accuracy of 0.8746, a precision of 0.8915, and an F1 score of 0.8472 in the 5-day prediction window, surpassing baseline methods such as CARAH, ARIMA, LSTM, and Informer. These results highlight PLB-GPT as a robust tool for early disease outbreak prediction, with significant implications for agricultural disease management. Full article
(This article belongs to the Special Issue Computational Intelligence for Bioinformatics)
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26 pages, 2625 KB  
Article
De Novo Single-Cell Biological Analysis of Drug Resistance in Human Melanoma Through a Novel Deep Learning-Powered Approach
by Sumaya Alghamdi, Turki Turki and Y-h. Taguchi
Mathematics 2025, 13(20), 3334; https://doi.org/10.3390/math13203334 - 20 Oct 2025
Viewed by 1334
Abstract
Elucidating drug response mechanisms in human melanoma is crucial for improving treatment outcomes. Moreover, the existing tools intended to provide deeper insight into melanoma drug resistance have notable limitations. Therefore, we propose a deep learning (DL)-based approach that works as follows. First, we [...] Read more.
Elucidating drug response mechanisms in human melanoma is crucial for improving treatment outcomes. Moreover, the existing tools intended to provide deeper insight into melanoma drug resistance have notable limitations. Therefore, we propose a deep learning (DL)-based approach that works as follows. First, we processed two single-cell datasets related to human melanoma from the GEO (GSE108383_A375 and GSE108383_451Lu) database and trained a fully connected neural network with five adapted methods (L1-Regularization, DeepLIFT, SHAP, IG, and LRP). We then identified 100 genes by ranking all genes from the highest to the lowest based on the sum of absolute values for corresponding weights across all neurons in the first hidden layer. From a biological perspective, compared to existing bioinformatics tools, the presented DL-based methods identified a higher number of expressed genes in four well-established melanoma cell lines: MALME-3M, MDA-MB435, SK-MEL-28, and SK-MEL-5. Furthermore, we identified FDA-approved melanoma drugs (e.g., Vemurafenib and Dabrafenib), critical genes such as ARAF, SOX10, DCT, and AXL, and key TFs including MITF and TFAP2A. From a classification perspective, we utilized five-fold cross-validation and provided gene sets using all the abovementioned methods to three randomly selected machine learning algorithms, namely, support vector machines, random forests, and neural networks with different hyperparameters. The results demonstrate that the integrated gradients (IG) method adapted in our DL approach contributed to 2.2% and 0.5% overall performance improvements over the best-performing baselines when using A375 and 451Lu cell line datasets. Additional comparison against no gene selection demonstrated that IG is the only method to generate statistically significant results, with 14.4% and 11.7% overall performance improvements. Full article
(This article belongs to the Special Issue Computational Intelligence for Bioinformatics)
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17 pages, 4524 KB  
Article
MT-Tracker: A Phylogeny-Aware Algorithm for Quantifying Microbiome Transitions Across Scales and Habitats
by Wenjie Zhu, Yangyang Sun, Weiwen Luo, Guosen Hou, Hao Gao and Xiaoquan Su
Mathematics 2025, 13(12), 1982; https://doi.org/10.3390/math13121982 - 16 Jun 2025
Viewed by 887
Abstract
The structural diversity of microbial communities plays a pivotal role in microbiological research and applications. However, the study of microbial transitions has remained challenging due to a lack of effective methods, limiting our understanding of microbial dynamics and their underlying mechanisms. To address [...] Read more.
The structural diversity of microbial communities plays a pivotal role in microbiological research and applications. However, the study of microbial transitions has remained challenging due to a lack of effective methods, limiting our understanding of microbial dynamics and their underlying mechanisms. To address this gap, we introduce MT-tracker (microbiome transition tracker), a novel algorithm designed to capture the transitional trajectories of microbial communities. Grounded in diversity and phylogenetic principles, MT-tracker reconstructs the virtual common ancestors of microbiomes at the community level. By calculating distances between microbiomes and their ancestors, MT-tracker deduces their transitional directions and probabilities, achieving a substantial speed advantage over conventional approaches. The accuracy and robustness of MT-tracker were first validated by a phylosymbiosis analysis using samples from 28 mammals and 24 nonmammal animals, describing the co-evolutionary pattern between hosts and their associated microbiomes. We then expanded the usage of MT-tracker to 456,702 microbiomes sampled world-wide, uncovering the global transitional directions among 21 ecosystems for the first time. This effort provides new insights into the macro-scale dynamic patterns of microbial communities. Additionally, MT-tracker revealed intricate longitudinal transition trends in human microbiomes over a sampling period exceeding 400 days, capturing temporal dynamics often overlooked by normal diversity analyses. In summary, MT-tracker offers robust support for the qualitative and quantitative analysis of microbial community diversity, offering significant potential for studying and utilizing the macrobiome variation. Full article
(This article belongs to the Special Issue Computational Intelligence for Bioinformatics)
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