Search for Articles:
Title / Keyword
Author / Affiliation / Email
Journal
Article Type
 
 
Advanced Search
 
Section
Special Issue
Volume
Issue
Number
Page
 
5.2
2.8
Journals
Genes
Special Issues
Advances and Applications of Machine Learning in Biomedical Genomics
share announcement

Advances and Applications of Machine Learning in Biomedical Genomics

A special issue of Genes (ISSN 2073-4425). This special issue belongs to the section "Bioinformatics".

Deadline for manuscript submissions: 20 December 2024 | Viewed by 1947

Special Issue Editor

Dr. Konstantinos Krampis

E-Mail Website
Guest Editor
Department of Biological Sciences, Hunter College, City University of New York, New York, NY 10065, USA
Interests: machine learning; artificial intelligence; deep learning; bioinformatics; biomedical genomics; cloud computing; LLMs; generative AI; neural networks; algorithms

Special Issue Information

Dear Colleagues,

This call invites researchers and practitioners in the field of bioinformatics to contribute to a Special Issue entitled “Advances and Applications of Machine Learning in Biomedical Genomics”. As the intersection of machine learning and genomics continues to catalyze transformative breakthroughs, this Special Issue aims to showcase cutting-edge research and applications that harness the power of machine learning techniques for elucidating complex biological mechanisms, predictive modeling of disease outcomes, and the interpretation of large-scale genomics data. We welcome manuscripts covering a spectrum of topics, including, but not limited to, predictive modeling, feature selection, deep learning applications, interpretability, and integrative analyses in the context of biomedical genomics. Submissions may span a range of species and address challenges in functional genomics, precision medicine, and systems biology. This Special Issue provides a platform for researchers to disseminate their novel methodologies, insights, and applications, fostering the exchange of knowledge and driving the continued advancement of the field.

Dr. Konstantinos Krampis
Guest Editor

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. Genes 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 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

  • machine learning
  • artificial intelligence
  • deep learning
  • bioinformatics
  • biomedical genomics
  • cloud computing
  • LLMs
  • generative AI
  • neural networks
  • algorithms

Benefits of Publishing in a Special Issue

  • Ease of navigation: Grouping papers by topic helps scholars navigate broad scope journals more efficiently.
  • Greater discoverability: Special Issues support the reach and impact of scientific research. Articles in Special Issues are more discoverable and cited more frequently.
  • Expansion of research network: Special Issues facilitate connections among authors, fostering scientific collaborations.
  • External promotion: Articles in Special Issues are often promoted through the journal's social media, increasing their visibility.
  • e-Book format: Special Issues with more than 10 articles can be published as dedicated e-books, ensuring wide and rapid dissemination.

Further information on MDPI's Special Issue polices can be found here.

Published Papers (2 papers)

Download All Papers
Order results
Result details
Show export options expand_more Show export options expand_less
Select all
Export citation of selected articles as:

Research

20 pages, 3184 KiB  
Article
GATv2EPI: Predicting Enhancer–Promoter Interactions with a Dynamic Graph Attention Network
by Tianjiao Zhang, Xingjie Zhao, Hao Sun, Bo Gao and Xiaoqi Liu
Genes 2024, 15(12), 1511; https://doi.org/10.3390/genes15121511 - 25 Nov 2024
Viewed by 187
Abstract
Background: The enhancer–promoter interaction (EPI) is a critical component of gene regulatory networks, playing a significant role in understanding the complexity of gene expression. Traditional EPI prediction methods focus on one-to-one interactions, neglecting more complex one-to-many and many-to-many patterns. To address this gap, [...] Read more.
Background: The enhancer–promoter interaction (EPI) is a critical component of gene regulatory networks, playing a significant role in understanding the complexity of gene expression. Traditional EPI prediction methods focus on one-to-one interactions, neglecting more complex one-to-many and many-to-many patterns. To address this gap, we utilize graph neural networks to comprehensively explore all interaction patterns between enhancers and promoters, capturing complex regulatory relationships for more accurate predictions. Methods: In this study, we introduce a novel EPI prediction framework, GATv2EPI, based on dynamic graph attention neural networks. GATv2EPI leverages epigenetic information from enhancers, promoters, and their surrounding regions and organizes interactions into a network to comprehensively explore complex EPI regulatory patterns, including one-to-one, one-to-many, and many-to-many relationships. To avoid overfitting and ensure diverse data representation, we implemented a connectivity-based sampling method for dataset partitioning, which constructs graphs for each chromosome and assigns entire connected subgraphs to training or test sets, thereby preventing information leakage and ensuring comprehensive chromosomal representation. Results: In experiments conducted on four cell lines—NHEK, IMR90, HMEC, and K562—GATv2EPI demonstrated superior EPI recognition accuracy compared to existing similar methods, with a training time improvement of 95.29% over TransEPI. Conclusions: GATv2EPI enhances EPI prediction accuracy by capturing complex topological structure information from gene regulatory networks through graph neural networks. Additionally, our results emphasize the importance of epigenetic features surrounding enhancers and promoters in EPI prediction. Full article
(This article belongs to the Special Issue Advances and Applications of Machine Learning in Biomedical Genomics)
►▼ Show Figures

Graphical abstract

19 pages, 3099 KiB  
Article
DRANetSplicer: A Splice Site Prediction Model Based on Deep Residual Attention Networks
by Xueyan Liu, Hongyan Zhang, Ying Zeng, Xinghui Zhu, Lei Zhu and Jiahui Fu
Genes 2024, 15(4), 404; https://doi.org/10.3390/genes15040404 - 26 Mar 2024
Viewed by 1457
Abstract
The precise identification of splice sites is essential for unraveling the structure and function of genes, constituting a pivotal step in the gene annotation process. In this study, we developed a novel deep learning model, DRANetSplicer, that integrates residual learning and attention mechanisms [...] Read more.
The precise identification of splice sites is essential for unraveling the structure and function of genes, constituting a pivotal step in the gene annotation process. In this study, we developed a novel deep learning model, DRANetSplicer, that integrates residual learning and attention mechanisms for enhanced accuracy in capturing the intricate features of splice sites. We constructed multiple datasets using the most recent versions of genomic data from three different organisms, Oryza sativa japonica, Arabidopsis thaliana and Homo sapiens. This approach allows us to train models with a richer set of high-quality data. DRANetSplicer outperformed benchmark methods on donor and acceptor splice site datasets, achieving an average accuracy of (96.57%, 95.82%) across the three organisms. Comparative analyses with benchmark methods, including SpliceFinder, Splice2Deep, Deep Splicer, EnsembleSplice, and DNABERT, revealed DRANetSplicer’s superior predictive performance, resulting in at least a (4.2%, 11.6%) relative reduction in average error rate. We utilized the DRANetSplicer model trained on O. sativa japonica data to predict splice sites in A. thaliana, achieving accuracies for donor and acceptor sites of (94.89%, 94.25%). These results indicate that DRANetSplicer possesses excellent cross-organism predictive capabilities, with its performance in cross-organism predictions even surpassing that of benchmark methods in non-cross-organism predictions. Cross-organism validation showcased DRANetSplicer’s excellence in predicting splice sites across similar organisms, supporting its applicability in gene annotation for understudied organisms. We employed multiple methods to visualize the decision-making process of the model. The visualization results indicate that DRANetSplicer can learn and interpret well-known biological features, further validating its overall performance. Our study systematically examined and confirmed the predictive ability of DRANetSplicer from various levels and perspectives, indicating that its practical application in gene annotation is justified. Full article
(This article belongs to the Special Issue Advances and Applications of Machine Learning in Biomedical Genomics)
►▼ Show Figures

Figure 1

Show export options expand_more Show export options expand_less
Select all
Export citation of selected articles as:
 
Displaying articles 1-2
Back to TopTop