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Biology
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Artificial Intelligence Research for Complex Biological Systems
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Artificial Intelligence Research for Complex Biological Systems

A special issue of Biology (ISSN 2079-7737). This special issue belongs to the section "Bioinformatics".

Deadline for manuscript submissions: 30 September 2025 | Viewed by 2840

Special Issue Editors

Dr. Yong Chen

E-Mail Website
Guest Editor
Department of Biology and Biomedical Sciences, Rowan University, Glassboro, NJ 08028, USA
Interests: single-cell omics data analysis; deep learning; mathematical modeling; cancer epigenetics; neuroscience
Dr. Milana Frenkel-Morgenstern

E-Mail Website
Guest Editor
Scojen Institute of Synthetic Biology, Reichman University, Hertsliya 4610101, Israel
Interests: chimeric RNAs; fusion proteins and liquid biopsy in complex diseases; machine learning and genomics algorithms
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Advancements in intelligent computing technology have significantly propelled cutting-edge research within the realms of bioinformatics, computational systems biology, and related fields. Intelligent computing methodologies have assumed a progressively pivotal role in both biological and medical research to investigate complex biological systems. Notably, intelligent computational approaches have demonstrated their efficacy in analyzing single-cell and bulk omics data, elucidating dynamic mechanisms pertinent to cancer and neuroscience and proficiently modeling and optimizing intricate biological systems. Single-cell omics data, characterized by their complex format, voluminous nature, high data dimensionality, suboptimal data quality, and pronounced levels of noise, have become a focal point in contemporary computational biology research, underscoring the importance of employing intelligent computing techniques for their analysis and interpretation. The anticipated emergence of generative artificial intelligence offers promise in establishing foundational computational frameworks, thereby fostering systematic advancements in biological and biomedical research. Consequently, we are pleased to announce an upcoming Special Issue titled "Artificial Intelligence-driven Discovery Research for Complex Biological Systems" in the Biology journal [MDPI]. The overarching objective of this Special Issue is to showcase the latest breakthroughs in the fields of bioinformatics, computational systems biology, and modern genomics, and we welcome submissions of technical papers focusing on data-driven studies leveraging intelligent computing technologies.

Dr. Yong Chen
Dr. Milana Frenkel-Morgenstern
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. Biology is an international peer-reviewed open access monthly 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 2700 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

  • deep learning
  • generative artificial intelligence
  • bioinformatics
  • complex biological systems
  • cancer
  • neuroscience
  • next-generation sequencing
  • single-cell and bulk omics
  • genomics

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Published Papers (3 papers)

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Research

18 pages, 3483 KiB  
Article
Enhancing Single-Cell and Bulk Hi-C Data Using a Generative Transformer Model
by Ruoying Gao, Thomas N. Ferraro, Liang Chen, Shaoqiang Zhang and Yong Chen
Biology 2025, 14(3), 288; https://doi.org/10.3390/biology14030288 - 12 Mar 2025
Viewed by 756
Abstract
The 3D organization of chromatin in the nucleus plays a critical role in regulating gene expression and maintaining cellular functions in eukaryotic cells. High-throughput chromosome conformation capture (Hi-C) and its derivative technologies have been developed to map genome-wide chromatin interactions at the population [...] Read more.
The 3D organization of chromatin in the nucleus plays a critical role in regulating gene expression and maintaining cellular functions in eukaryotic cells. High-throughput chromosome conformation capture (Hi-C) and its derivative technologies have been developed to map genome-wide chromatin interactions at the population and single-cell levels. However, insufficient sequencing depth and high noise levels in bulk Hi-C data, particularly in single-cell Hi-C (scHi-C) data, result in low-resolution contact matrices, thereby limiting diverse downstream computational analyses in identifying complex chromosomal organizations. To address these challenges, we developed a transformer-based deep learning model, HiCENT, to impute and enhance both scHi-C and Hi-C contact matrices. Validation experiments on large-scale bulk Hi-C and scHi-C datasets demonstrated that HiCENT achieves superior enhancement effects compared to five popular methods. When applied to real Hi-C data from the GM12878 cell line, HiCENT effectively enhanced 3D structural features at the scales of topologically associated domains and chromosomal loops. Furthermore, when applied to scHi-C data from five human cell lines, it significantly improved clustering performance, outperforming five widely used methods. The adaptability of HiCENT across different datasets and its capacity to improve the quality of chromatin interaction data will facilitate diverse downstream computational analyses in 3D genome research, single-cell studies and other large-scale omics investigations. Full article
(This article belongs to the Special Issue Artificial Intelligence Research for Complex Biological Systems)
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25 pages, 1564 KiB  
Article
K-Volume Clustering Algorithms for scRNA-Seq Data Analysis
by Yong Chen and Fei Li
Biology 2025, 14(3), 283; https://doi.org/10.3390/biology14030283 - 11 Mar 2025
Viewed by 479
Abstract
Clustering high-dimensional and structural data remains a key challenge in computational biology, especially for complex single-cell and multi-omics datasets. In this study, we present K-volume clustering, a novel algorithm that uses the total convex volume defined by points within a cluster as [...] Read more.
Clustering high-dimensional and structural data remains a key challenge in computational biology, especially for complex single-cell and multi-omics datasets. In this study, we present K-volume clustering, a novel algorithm that uses the total convex volume defined by points within a cluster as a biologically relevant and geometrically interpretable criterion. This method simultaneously optimizes both the hierarchical structure and the number of clusters at each level through nonlinear optimization. Validation on real datasets shows that K-volume clustering outperforms traditional methods across a range of biological applications. With its theoretical foundation and broad applicability, K-volume clustering holds great promise as a core tool for diverse data analysis tasks. Full article
(This article belongs to the Special Issue Artificial Intelligence Research for Complex Biological Systems)
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20 pages, 5366 KiB  
Article
Sequence-Only Prediction of Super-Enhancers in Human Cell Lines Using Transformer Models
by Ekaterina V. Kravchuk, German A. Ashniev, Marina G. Gladkova, Alexey V. Orlov, Zoia G. Zaitseva, Juri A. Malkerov and Natalia N. Orlova
Biology 2025, 14(2), 172; https://doi.org/10.3390/biology14020172 - 7 Feb 2025
Viewed by 1124
Abstract
The study discloses the application of transformer-based deep learning models for the task of super-enhancers prediction in human tumor cell lines with a specific focus on sequence-only features within studied entities of super-enhancer and enhancer elements in the human genome. The proposed SE-prediction [...] Read more.
The study discloses the application of transformer-based deep learning models for the task of super-enhancers prediction in human tumor cell lines with a specific focus on sequence-only features within studied entities of super-enhancer and enhancer elements in the human genome. The proposed SE-prediction method included the GENA-LM application at handling long DNA sequences with the classification task, distinguishing super-enhancers from enhancers using H3K36me, H3K4me1, H3K4me3 and H3K27ac landscape datasets from HeLa, HEK293, H2171, Jurkat, K562, MM1S and U87 cell lines. The model was fine-tuned on relevant sequence data, allowing for the analysis of extended genomic sequences without the need for epigenetic markers as proposed in early approaches. The study achieved balanced accuracy metrics, surpassing previous models like SENet, particularly in HEK293 and K562 cell lines. Also, it was shown that super-enhancers frequently co-localize with epigenetic marks such as H3K4me3 and H3K27ac. Therefore, the attention mechanism of the model provided insights into the sequence features contributing to SE classification, indicating a correlation between sequence-only features and mentioned epigenetic landscapes. These findings support the potential transformer models use in further genomic sequence analysis for bioinformatics applications in enhancer/super-enhancer characterization and gene regulation studies. Full article
(This article belongs to the Special Issue Artificial Intelligence Research for Complex Biological Systems)
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