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Processes
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Computational Biology Approaches to Genome and Protein Analyzes
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Computational Biology Approaches to Genome and Protein Analyzes

A special issue of Processes (ISSN 2227-9717). This special issue belongs to the section "Biological Processes and Systems".

Deadline for manuscript submissions: closed (31 March 2025) | Viewed by 8918

Special Issue Editor

Prof. Dr. Andrzej Kasperski

E-Mail Website
Guest Editor
Institute of Biological Sciences, Department of Biotechnology, Laboratory of Bioinformatics and Control of Bioprocesses, University of Zielona Góra, ul. Szafrana 1, 65‑516 Zielona Góra, Poland
Interests: bioinformatics; bioprocesses; computational biology; informatics; organism evolution; programming

Special Issue Information

Dear Colleagues,

The aim of this Special Issue is to collect research articles on, among other topics, methods (including artificial intelligence, machine learning, mathematical, statistical methods), algorithms and their implementations in the field of computational biology.

Nowadays, the amount of experimental biological data is continuing to increase. This is connected with the necessity of developing new, more effective methods, algorithms and their implementations as computer programs, which will allow for an in-depth understanding of the meaning of the data. New approaches are especially important during the analysis of genomes (including sequences) and proteins, and striving to establish a complex sequence-function relationship. These approaches can rely on the "computing power" needed, for example, when generating phylogenetic trees by ever faster computers. New approaches can also use artificial intelligence methods, especially artificial neural networks, fuzzy logic, and expert systems. Using artificial neural networks, for example, makes it possible to replace computation with recognition. Moreover, regardless of the method used, the interpretation of the results always plays a key role, and in this area, computer methods can also play an important role by effectively supporting scientists. New approaches are particularly important when analyzing genome changes during the evolution of normal organisms and the development of transformed cells, as these areas are relatively poorly understood.

Potential topics covered by this Special Issue include, but are not limited to, ideas, methods (including artificial intelligence, machine learning, mathematical and statistical methods), algorithms, implementations, and computer programs used to analyze genomes (including sequences) and proteins, and establishing a sequence-function relationship. Original articles and reviews addressing these topics are welcomed. 

Prof. Dr. Andrzej Kasperski
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. Processes 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 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

  • artificial neural networks
  • artificial intelligence methods
  • cancer development analysis
  • cell-fate and genome attractors
  • computational biology
  • computerized analysis of genomes (including sequences) and proteins
  • computerized identification and recognition of evolution
  • dot-matrix method
  • genetic semihomology
  • expert systems
  • evolutionary distances and trees
  • machine learning
  • multiple alignment
  • navigating cancer network attractors
  • neural networks
  • pattern recognition
  • phylogenetic trees
  • sequence–function relationship
  • sequence similarity
  • protein contact networks (PCNs)

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Published Papers (5 papers)

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Research

17 pages, 3197 KiB  
Article
Prediction of circRNA–miRNA Interaction Using Graph Attention Network Based on Molecular Attributes and Biological Networks
by Abdullah Almotilag, Murtada K. Elbashir, Mahmood A. Mahmood and Mohanad Mohammed
Processes 2025, 13(5), 1318; https://doi.org/10.3390/pr13051318 - 25 Apr 2025
Viewed by 117
Abstract
(1) Background: Circular RNAs (circRNAs) are covalently closed single-stranded molecules that play crucial roles in gene regulation, while microRNAs (miRNAs), specifically mature microRNAs, are naturally occurring small molecules of non-coding RNA with 17-25-nucleotide sizes. Understanding circRNA–miRNA interactions (CMIs) can reveal new approaches for [...] Read more.
(1) Background: Circular RNAs (circRNAs) are covalently closed single-stranded molecules that play crucial roles in gene regulation, while microRNAs (miRNAs), specifically mature microRNAs, are naturally occurring small molecules of non-coding RNA with 17-25-nucleotide sizes. Understanding circRNA–miRNA interactions (CMIs) can reveal new approaches for diagnosing and treating complex human diseases. (2) Methods: In this paper, we propose a novel approach for predicting CMIs based on a graph attention network (GAT). We utilized DNABERT to extract molecular features of the circRNA and miRNA sequences and role-based graph embeddings generated by Role2Vec to extract the CMI features. The GAT’s ability to learn complex node dependencies in biological networks provided enhanced performance over the existing methods and the traditional deep neural network models. (3) Results: Our simulation studies showed that our GAT model achieved accuracies of 0.8762 and 0.8837 on the CMI-9905 and CMI-9589, respectively. These accuracies were the highest among the other existing CMI prediction methods. Our GAT method also achieved the highest performance as measured by the precision, recall, F1-score, area under the receiver operating characteristic (AUROC) curve, and area under the precision–recall curve (AUPR). (4) Conclusions: These results reflect the GAT’s ability to capture the intricate relationships between circRNAs and miRNAs, thus offering an efficient computational approach for prioritizing potential interactions for experimental validation. Full article
(This article belongs to the Special Issue Computational Biology Approaches to Genome and Protein Analyzes)
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12 pages, 7957 KiB  
Article
Comparative Genomic Analysis of Extracellular Electron Transfer in Bacteria
by Daniel Liu, Jimmy Kuo and Chorng-Horng Lin
Processes 2024, 12(12), 2636; https://doi.org/10.3390/pr12122636 - 22 Nov 2024
Cited by 1 | Viewed by 1113
Abstract
Certain bacteria can transfer extracellular electrons and are applied in microbial fuel cells (MFCs). In this study, we compared the extracellular electron transfer characteristics of 85 genomes from nine genera, namely Blautia, Bradyrhizobium, Desulfuromonas, Dialister, Geobacter, Geothrix, [...] Read more.
Certain bacteria can transfer extracellular electrons and are applied in microbial fuel cells (MFCs). In this study, we compared the extracellular electron transfer characteristics of 85 genomes from nine genera, namely Blautia, Bradyrhizobium, Desulfuromonas, Dialister, Geobacter, Geothrix, Shewanella, Sphingomonas, and Phascolarctobacterium, using the bioinformatic tools Prokka 1.14.6, Roary 3.13.0, Panaroo 1.3.4, PEPPAN 1.0.6, and Twilight. The unweighted pair-group method with arithmetic mean (UPGMA) clustering of genes related to extracellular electron transfer revealed a good genus-level structure. The relative abundance and hierarchical clustering analyses performed in this study suggest that the bacteria Desulfuromonas, Geobacter, Geothrix, and Shewanella have more extracellular electron transfer genes and cluster together. Further functional differences among the genomes showed that 66 genes in these bacteria were significantly higher in abundance than in the other five bacteria (p < 0.01) based on PEPPAN followed by a Twilight analysis. Our work provides new potential insights into extracellular electron transfer in microorganisms. Full article
(This article belongs to the Special Issue Computational Biology Approaches to Genome and Protein Analyzes)
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12 pages, 760 KiB  
Article
Genetic Association of Diagnostic Traits of Metabolic Syndrome with Lysosomal Pathways: Insights from Target Gene Enrichment Analysis
by Yeeun An, Yunji Seo and Chaeyoung Lee
Processes 2023, 11(11), 3221; https://doi.org/10.3390/pr11113221 - 13 Nov 2023
Viewed by 1405
Abstract
Genome-wide association studies (GWAS) identified many association signals for metabolic syndrome (MetS). However, the understanding of its pathophysiology may be limited because of the complexity of the intertwined genetic factors that underlie diagnostic condition traits. We conducted an enrichment analysis of spatial expression [...] Read more.
Genome-wide association studies (GWAS) identified many association signals for metabolic syndrome (MetS). However, the understanding of its pathophysiology may be limited because of the complexity of the intertwined genetic factors that underlie diagnostic condition traits. We conducted an enrichment analysis of spatial expression genes (eGenes) associated with GWAS signals for MetS and its diagnostic condition traits. Consequently, eGenes associated with MetS were significantly enriched in 14 biological pathways (PBH < 0.05, where PBH is the p-value adjusted for Benjamini–Hochberg multiple testing). Moreover, 38 biological pathways were additionally identified in the enrichment analysis of the individual diagnostic traits (PBH < 0.05). In particular, the lysosomal pathway was revealed for waist-to-hip ratio, glucose measurement, and high-density lipoprotein cholesterol (PBH < 0.05), but not for MetS (PBH > 0.05). It was inferred that lysosomal pathway-based control of cellular lipid metabolism and insulin secretion/resistance could result in eGene enrichment for these diagnostic traits. In conclusion, this target gene enrichment analysis of diagnostic traits of MetS uncovered a lysosomal pathway that may dilute its effects on the MetS. We propose that lysosomal dysfunction should be a priority for research on the underlying pathogenic mechanisms of MetS and its diagnostic traits. Experimental studies are needed to elucidate causal relationships of ribosomal pathways with metabolic syndrome and its diagnostic traits. Full article
(This article belongs to the Special Issue Computational Biology Approaches to Genome and Protein Analyzes)
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25 pages, 2069 KiB  
Article
Recognition of Timestamps and Reconstruction of the Line of Organism Development
by Andrzej Kasperski
Processes 2023, 11(5), 1316; https://doi.org/10.3390/pr11051316 - 24 Apr 2023
Cited by 2 | Viewed by 1778
Abstract
In this work, an artificial neural network is used to recognize timestamps of evolution. Timestamps are associated with outliers determined during the recognition of the genome attractors of organisms. The aim of this work is to present a new method of penetrating deep [...] Read more.
In this work, an artificial neural network is used to recognize timestamps of evolution. Timestamps are associated with outliers determined during the recognition of the genome attractors of organisms. The aim of this work is to present a new method of penetrating deep into evolution using the recognized timestamps. To achieve this aim, the neural networks of different number of layers were implemented in order to check the influence of the number of layers on the visibility of the timestamps. Moreover, the teaching process was repeated 10 times for each implemented neural network. The recognition of each organism evolution was also repeated 10 times for each taught neural network to increase the reliability of the results. It is presented, among other findings, that during the recognition of the timestamps of evolution not only the number of homologous comparisons and the lengths of compared sequences are important but also the distribution of similarities between sequences. It is also presented that the recognized timestamps allow for travel between genome attractors and reconstruct the line of organism development from the most advanced to the most primitive organisms. The results were validated by determining timestamps for exemplary sets of organisms and also in relation to semihomology approach and by phylogenetic tree generation. Full article
(This article belongs to the Special Issue Computational Biology Approaches to Genome and Protein Analyzes)
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25 pages, 2729 KiB  
Article
Estimation of Small-Scale Kinetic Parameters of Escherichia coli (E. coli) Model by Enhanced Segment Particle Swarm Optimization Algorithm ESe-PSO
by Mohammed Adam Kunna Azrag, Jasni Mohamad Zain, Tuty Asmawaty Abdul Kadir, Marina Yusoff, Aqeel Sakhy Jaber, Hybat Salih Mohamed Abdlrhman, Yasmeen Hafiz Zaki Ahmed and Mohamed Saad Bala Husain
Processes 2023, 11(1), 126; https://doi.org/10.3390/pr11010126 - 1 Jan 2023
Cited by 5 | Viewed by 2573
Abstract
The ability to create “structured models” of biological simulations is becoming more and more commonplace. Although computer simulations can be used to estimate the model, they are restricted by the lack of experimentally available parameter values, which must be approximated. In this study, [...] Read more.
The ability to create “structured models” of biological simulations is becoming more and more commonplace. Although computer simulations can be used to estimate the model, they are restricted by the lack of experimentally available parameter values, which must be approximated. In this study, an Enhanced Segment Particle Swarm Optimization (ESe-PSO) algorithm that can estimate the values of small-scale kinetic parameters is described and applied to E. coli’s main metabolic network as a model system. The glycolysis, phosphotransferase system, pentose phosphate, the TCA cycle, gluconeogenesis, glyoxylate pathways, and acetate formation pathways of Escherichia coli are represented by the Differential Algebraic Equations (DAE) system for the metabolic network. However, this algorithm uses segments to organize particle movements and the dynamic inertia weight (ω) to increase the algorithm’s exploration and exploitation potential. As an alternative to the state-of-the-art algorithm, this adjustment improves estimation accuracy. The numerical findings indicate a good agreement between the observed and predicted data. In this regard, the result of the ESe-PSO algorithm achieved superior accuracy compared with the Segment Particle Swarm Optimization (Se-PSO), Particle Swarm Optimization (PSO), Genetic Algorithm (GA), and Differential Evolution (DE) algorithms. As a result of this innovative approach, it was concluded that small-scale and even entire cell kinetic model parameters can be developed. Full article
(This article belongs to the Special Issue Computational Biology Approaches to Genome and Protein Analyzes)
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