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Biology
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2nd Edition of Computational Methods in Biology
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2nd Edition of Computational Methods in Biology

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

Deadline for manuscript submissions: closed (1 February 2025) | Viewed by 6045

Special Issue Editors

Dr. Milan Toma

E-Mail Website
Guest Editor
Department of Osteopathic Manipulative Medicine, College of Osteopathic Medicine, New York Institute of Technology, Old Westbury Campus, Northern Boulevard, Old Westbury, NY 11568-8000, USA
Interests: image processing; high-performance computing; computational mechanics/biomechanics; biomechanical/biomedical engineering
Special Issues, Collections and Topics in MDPI journals
Dr. Chi Wei Ong

E-Mail Website
Guest Editor
Agency for Science, Technology and Research (A*STAR), Singapore, Singapore
Interests: cardiovascular mechanics; fluid structure interaction; computational fluid dynamics; finite element analysis
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

This Special Issue delves into the ways in which computational methods and artificial intelligence (AI) are revolutionizing the field of biological research, analysis, and discovery. By exploring the potential of computational methods and AI, we are able to unlock new insights, identify intricate patterns, and drive progress in various branches of biology. These technologies enable us to analyze vast amounts of biological data, leading to the discovery of new knowledge. Advanced algorithms and machine learning techniques predict protein structures, provide the analysis of gene expressions, and explore complex biological networks. Their ability to handle complex datasets effectively allows us to gain a deeper understanding of disease mechanisms, facilitate drug discovery, and personalize medicine. Furthermore, AI-driven and other computational methods facilitate the integration of diverse sources of biological data, enabling the revelation of hidden relationships and the development of comprehensive perspectives on complex biological systems. The articles featured in this Special Issue highlight the transformative impact of computational methods and AI algorithms across various areas including genomics, systems biology, drug discovery, and cancer research. This has opened new possibilities in biology and has paved the way for a more efficient and accurate understanding of complex biological systems. Through these advancements, biology is heading toward an innovative future shaped by new medical findings and transformative advancements in healthcare.

Dr. Milan Toma
Dr. Chi Wei Ong
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

  • computational biology
  • artificial intelligence
  • machine learning
  • bioinformatics
  • data analysis
  • genomics
  • proteomics
  • drug discovery systems biology
  • cancer research

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Published Papers (4 papers)

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21 pages, 7637 KiB  
Article
An In Silico Investigation of the Pathogenic G151R G Protein-Gated Inwardly Rectifying K+ Channel 4 Variant to Identify Small Molecule Modulators
by Eleni Pitsillou, Julia J. Liang, Noa Kino, Jessica L. Lockwood, Andrew Hung, Assam El-Osta, Asmaa S. AbuMaziad and Tom C. Karagiannis
Biology 2024, 13(12), 992; https://doi.org/10.3390/biology13120992 - 29 Nov 2024
Cited by 1 | Viewed by 991
Abstract
Primary aldosteronism is characterised by the excessive production of aldosterone, which is a key regulator of salt metabolism, and is the most common cause of secondary hypertension. Studies have investigated the association between primary aldosteronism and genetic alterations, with pathogenic mutations being identified. [...] Read more.
Primary aldosteronism is characterised by the excessive production of aldosterone, which is a key regulator of salt metabolism, and is the most common cause of secondary hypertension. Studies have investigated the association between primary aldosteronism and genetic alterations, with pathogenic mutations being identified. This includes a glycine-to-arginine substitution at position 151 (G151R) of the G protein-activated inward rectifier potassium (K+) channel 4 (GIRK4), which is encoded by the KCNJ5 gene. Mutations in GIRK4 have been found to reduce the selectivity for K+ ions, resulting in membrane depolarisation, the activation of voltage-gated Ca2+ channels, and an increase in aldosterone secretion. As a result, there is an interest in identifying and exploring the mechanisms of action of small molecule modulators of wildtype (WT) and mutant channels. In order to investigate the potential modulation of homotetrameric GIRK4WT and GIRK4G151R channels, homology models were generated. Molecular dynamics (MD) simulations were performed, followed by a cluster analysis to extract starting structures for molecular docking. The central cavity has been previously identified as a binding site for small molecules, including natural compounds. The OliveNetTM database, which consists of over 600 compounds from Olea europaea, was subsequently screened against the central cavity. The binding affinities and interactions of the docked ligands against the GIRK4WT and GIRK4G151R channels were then examined. Based on the results, luteolin-7-O-rutinoside, pheophorbide a, and corosolic acid were identified as potential lead compounds. The modulatory activity of olive-derived compounds against the WT and mutated forms of the GIRK4 channel can be evaluated further in vitro. Full article
(This article belongs to the Special Issue 2nd Edition of Computational Methods in Biology)
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16 pages, 2463 KiB  
Article
Binning Metagenomic Contigs Using Contig Embedding and Decomposed Tetranucleotide Frequency
by Long Fu, Jiabin Shi and Baohua Huang
Biology 2024, 13(10), 755; https://doi.org/10.3390/biology13100755 - 24 Sep 2024
Viewed by 1638
Abstract
Metagenomic binning is a crucial step in metagenomic research. It can aggregate the genome sequences belonging to the same microbial species into independent bins. Most existing methods ignore the semantic information of contigs and lack effective processing of tetranucleotide frequency, resulting in insufficient [...] Read more.
Metagenomic binning is a crucial step in metagenomic research. It can aggregate the genome sequences belonging to the same microbial species into independent bins. Most existing methods ignore the semantic information of contigs and lack effective processing of tetranucleotide frequency, resulting in insufficient and complex feature information extracted for binning and poor binning results. To address the above problems, we propose CedtBin, a metagenomic binning method based on contig embedding and decomposed tetranucleotide frequency. First, the improved BERT model is used to learn the contigs to obtain their embedding representation. Secondly, the tetranucleotide frequencies are decomposed using a non-negative matrix factorization (NMF) algorithm. After that, the two features are spliced and input into the clustering algorithm for binning. Considering the sensitivity of the DBSCAN clustering algorithm to input parameters, in order to solve the drawbacks of manual parameter input, we also propose an Annoy-DBSCAN algorithm that can adaptively determine the parameters of the DBSCAN algorithm. This algorithm uses Approximate Nearest Neighbors Oh Yeah (Annoy) and combines it with a grid search strategy to find the optimal parameters of the DBSCAN algorithm. On simulated and real datasets, CedtBin achieves better binning results than mainstream methods and can reconstruct more genomes, indicating that the proposed method is effective. Full article
(This article belongs to the Special Issue 2nd Edition of Computational Methods in Biology)
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15 pages, 2970 KiB  
Article
scVGATAE: A Variational Graph Attentional Autoencoder Model for Clustering Single-Cell RNA-seq Data
by Lijun Liu, Xiaoyang Wu, Jun Yu, Yuduo Zhang, Kaixing Niu and Anli Yu
Biology 2024, 13(9), 713; https://doi.org/10.3390/biology13090713 - 11 Sep 2024
Viewed by 1616
Abstract
Single-cell RNA sequencing (scRNA-seq) is now a successful technology for identifying cell heterogeneity, revealing new cell subpopulations, and predicting developmental trajectories. A crucial component in scRNA-seq is the precise identification of cell subsets. Although many unsupervised clustering methods have been developed for clustering [...] Read more.
Single-cell RNA sequencing (scRNA-seq) is now a successful technology for identifying cell heterogeneity, revealing new cell subpopulations, and predicting developmental trajectories. A crucial component in scRNA-seq is the precise identification of cell subsets. Although many unsupervised clustering methods have been developed for clustering cell subpopulations, the performance of these methods is prone to be affected by dropout, high dimensionality, and technical noise. Additionally, most existing methods are time-consuming and fail to fully consider the potential correlations between cells. In this paper, we propose a novel unsupervised clustering method called scVGATAE (Single-cell Variational Graph Attention Autoencoder) for scRNA-seq data. This method constructs a reliable cell graph through network denoising, utilizes a novel variational graph autoencoder model integrated with graph attention networks to aggregate neighbor information and learn the distribution of the low-dimensional representations of cells, and adaptively determines the model training iterations for various datasets. Finally, the obtained low-dimensional representations of cells are clustered using kmeans. Experiments on nine public datasets show that scVGATAE outperforms classical and state-of-the-art clustering methods. Full article
(This article belongs to the Special Issue 2nd Edition of Computational Methods in Biology)
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11 pages, 3500 KiB  
Technical Note
MeStanG—Resource for High-Throughput Sequencing Standard Data Sets Generation for Bioinformatic Methods Evaluation and Validation
by Daniel Ramos Lopez, Francisco J. Flores and Andres S. Espindola
Biology 2025, 14(1), 69; https://doi.org/10.3390/biology14010069 - 14 Jan 2025
Viewed by 1081
Abstract
Metagenomics analysis has enabled the measurement of the microbiome diversity in environmental samples without prior targeted enrichment. Functional and phylogenetic studies based on microbial diversity retrieved using HTS platforms have advanced from detecting known organisms and discovering unknown species to applications in disease [...] Read more.
Metagenomics analysis has enabled the measurement of the microbiome diversity in environmental samples without prior targeted enrichment. Functional and phylogenetic studies based on microbial diversity retrieved using HTS platforms have advanced from detecting known organisms and discovering unknown species to applications in disease diagnostics. Robust validation processes are essential for test reliability, requiring standard samples and databases deriving from real samples and in silico generated artificial controls. We propose a MeStanG as a resource for generating HTS Nanopore data sets to evaluate present and emerging bioinformatics pipelines. MeStanG allows samples to be designed with user-defined organism abundances expressed as number of reads, reference sequences, and predetermined or custom errors by sequencing profiles. The simulator pipeline was evaluated by analyzing its output mock metagenomic samples containing known read abundances using read mapping, genome assembly, and taxonomic classification on three scenarios: a bacterial community composed of nine different organisms, samples resembling pathogen-infected wheat plants, and a viral pathogen serial dilution sampling. The evaluation was able to report consistently the same organisms, and their read abundances as provided in the mock metagenomic sample design. Based on this performance and its novel capacity of generating exact number of reads, MeStanG can be used by scientists to develop mock metagenomic samples (artificial HTS data sets) to assess the diagnostic performance metrics of bioinformatic pipelines, allowing the user to choose predetermined or customized models for research and training. Full article
(This article belongs to the Special Issue 2nd Edition of Computational Methods in Biology)
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