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Bioinformatics & Computational Biology

A special issue of Applied Sciences (ISSN 2076-3417). This special issue belongs to the section "Biomedical Engineering".

Deadline for manuscript submissions: 20 January 2025 | Viewed by 3136

Special Issue Editor

Dr. Luca Cattelani

E-Mail Website
Guest Editor
School of Medicine, Institute of Biomedicine, University of Eastern Finland, Kuopio, Finland
Interests: artificial intelligence; bioinformatics; health risk assessment; particle filtering; pedestrian mobility simulation

Special Issue Information

Dear Colleagues,

Bioinformatics and computational biology have seen significant advancements in recent years, particularly with the integration of concepts from biological systems, computer science, big data, and artificial intelligence.

Techniques like machine learning, deep learning, and genome-wide association studies have been increasingly applied to extract valuable knowledge from multi-omics big data, showing unprecedented performances in various applications, like drug discovery, computational pharmacology, biomarker discovery, risk prediction models, understanding the toxicology of substances, and extrapolating in vitro results to in vivo scenarios.

The interdisciplinary nature of bioinformatics and computational biology, combined with the power of modern computational techniques and the availability of big biological data, biological ontologies, and the network analysis of biological networks has led to significant advancements in our understanding of the inherent complexity in biological systems and our ability to predict and understand human health and diseases.

We invite authors to submit original research articles and review articles concerning applications of computer science and artificial intelligence to biological data.

Dr. Luca Cattelani
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. Applied Sciences 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 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

  • bioinformatics
  • computational biology
  • machine learning
  • artificial intelligence
  • biological big data
  • multi-omics

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Published Papers (4 papers)

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Research

15 pages, 1102 KiB  
Article
Optimal Paradigms for Quantitative Modeling in Systems Biology Demonstrated for Spinal Motor Neuron Synthesis
by Gülbahar Akgün and Rza Bashirov
Appl. Sci. 2024, 14(22), 10696; https://doi.org/10.3390/app142210696 - 19 Nov 2024
Viewed by 388
Abstract
Since the 1990s, Petri nets have been used in systems biology for quantitative modeling. Despite the increasing number of models developed during this period, doubts remain about their biological relevance. Although biological systems predominantly exhibit intracellular or cellular structures, the models rely largely [...] Read more.
Since the 1990s, Petri nets have been used in systems biology for quantitative modeling. Despite the increasing number of models developed during this period, doubts remain about their biological relevance. Although biological systems predominantly exhibit intracellular or cellular structures, the models rely largely on deterministic predictions, failing to capture the inherent randomness and uncertainties of such systems. The question arises whether these models accurately describe the dynamic behavior of biological systems. This paper introduces a methodology for selecting the appropriate modeling paradigms in systems biology. Initially, we construct a Petri net model and perform deterministic, stochastic, and fuzzy stochastic simulations. Then we perform various statistical tests to measure the discrepancies between the simulation results. Based on scale-density analysis, we determine the modeling approach that best approximates the biological system. Finally, we compare the results of the statistical tests and the scale-density analysis to identify the optimal modeling approach. We applied the proposed methodology to the synthesis of spinal motor neuron protein from the spinal motor neuron-2 gene. Analysis revealed significant discrepancies between the simulation results of different modeling paradigms. Due to the sparse nature of the underlying drug-disease network, we conclude that the fuzzy stochastic paradigm provides the most biologically relevant results. We predict drug combinations that could lead to an up to 149-fold increase in spinal motor neuron protein levels, indicating a promising treatment for the disease. This methodology has the potential for application to other gene-drug-disease networks and broader biological systems. Full article
(This article belongs to the Special Issue Bioinformatics & Computational Biology)
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16 pages, 295 KiB  
Article
Regressive Machine Learning for Real-Time Monitoring of Bed-Based Patients
by Paul Joseph, Husnain Ali, Daniel Matthew, Anvin Thomas, Rejath Jose, Jonathan Mayer, Molly Bekbolatova, Timothy Devine and Milan Toma
Appl. Sci. 2024, 14(21), 9978; https://doi.org/10.3390/app14219978 - 31 Oct 2024
Viewed by 558
Abstract
This study introduces an ensemble model designed for real-time monitoring of bedridden patients. The model was developed using a unique dataset, specifically acquired for this study, that captures six typical movements. The dataset was balanced using the Synthetic Minority Over-sampling Technique, resulting in [...] Read more.
This study introduces an ensemble model designed for real-time monitoring of bedridden patients. The model was developed using a unique dataset, specifically acquired for this study, that captures six typical movements. The dataset was balanced using the Synthetic Minority Over-sampling Technique, resulting in a diverse distribution of movement types. Three models were evaluated: a Decision Tree Regressor, a Gradient Boosting Regressor, and a Bagging Regressor. The Decision Tree Regressor achieved an accuracy of 0.892 and an R2 score of 1.0 on the training dataset, and 0.939 on the test dataset. The Boosting Regressor achieved an accuracy of 0.908 and an R2 score of 0.99 on the training dataset, and 0.943 on the test dataset. The Bagging Regressor was selected due to its superior performance and trade-offs such as computational cost and scalability. It achieved an accuracy of 0.950, an R2 score of 0.996 for the training data, and an R2 score of 0.959 for the test data. This study also employs K-Fold cross-validation and learning curves to validate the robustness of the Bagging Regressor model. The proposed system addresses practical implementation challenges in real-time monitoring, such as data latency and false positives/negatives, and is designed for seamless integration with hospital IT infrastructure. This research demonstrates the potential of machine learning to enhance patient safety in healthcare settings. Full article
(This article belongs to the Special Issue Bioinformatics & Computational Biology)
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22 pages, 4098 KiB  
Article
Pharmacoinformatics, Molecular Dynamics Simulation, and Quantum Mechanics Calculation Based Phytochemical Screening of Croton bonplandianum Against Breast Cancer by Targeting Estrogen Receptor-α (ERα)
by Shuvo Saha, Partha Biswas, Mohaimenul Islam Tareq, Musfiqur Rahman Sakib, Suraia Akter Rakhi, Md. Nazmul Hasan Zilani, Abdel Halim Harrath, Md. Ataur Rahman and Md. Nazmul Hasan
Appl. Sci. 2024, 14(21), 9878; https://doi.org/10.3390/app14219878 - 29 Oct 2024
Viewed by 707
Abstract
Breast cancer progression is strongly influenced by estrogen receptor-α (ERα), a ligand-activated transcription factor that regulates hormone binding, DNA interaction, and transcriptional activation. ERα plays a key role in promoting cell proliferation in breast tissue, and its overexpression is associated with the advancement [...] Read more.
Breast cancer progression is strongly influenced by estrogen receptor-α (ERα), a ligand-activated transcription factor that regulates hormone binding, DNA interaction, and transcriptional activation. ERα plays a key role in promoting cell proliferation in breast tissue, and its overexpression is associated with the advancement of breast cancer through estrogen-mediated signaling pathways. Targeting ERα is, therefore, a promising therapeutic strategy for breast cancer. However, there are currently no phytochemical-based drug candidates approved for effectively inhibiting breast cancer progression driven by elevated ERα expression. This study aims to identify phytochemical inhibitors from Croton bonplandianum against ERα using pharmacoinformatics approaches. Eighty-three bioactive compounds from C. bonplandianum were retrieved from the IMPPAT (Indian Medicinal Plants, Phytochemistry, and Therapeutics) database and screened through molecular docking for their binding affinity to ERα. The top candidates were further evaluated through molecular dynamics simulations, ADME analysis, toxicity assessment, and quantum mechanics-based DFT calculations. The thermodynamic properties and HOMO-LUMO energy gap values indicated that the selected compounds were both stable and active. Among them, 2,3-oxidosqualene (CID-5366020) and 5,8,11-eicosatriynoic acid, trimethylsilyl ester (CID-91696396) demonstrated the most potent inhibitory activity against ERα. These findings suggest that these compounds have significant potential as therapeutic agents for breast cancer treatment by targeting ERα. Full article
(This article belongs to the Special Issue Bioinformatics & Computational Biology)
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22 pages, 1373 KiB  
Article
Perturbation Theory Machine Learning Model for Phenotypic Early Antineoplastic Drug Discovery: Design of Virtual Anti-Lung-Cancer Agents
by Valeria V. Kleandrova, M. Natália D. S. Cordeiro and Alejandro Speck-Planche
Appl. Sci. 2024, 14(20), 9344; https://doi.org/10.3390/app14209344 - 14 Oct 2024
Viewed by 738
Abstract
Lung cancer is the most diagnosed malignant neoplasm worldwide and it is associated with great mortality. Currently, developing antineoplastic agents is a challenging, time-consuming, and costly process. Computational methods can speed up the early discovery of anti-lung-cancer chemicals. Here, we report a perturbation [...] Read more.
Lung cancer is the most diagnosed malignant neoplasm worldwide and it is associated with great mortality. Currently, developing antineoplastic agents is a challenging, time-consuming, and costly process. Computational methods can speed up the early discovery of anti-lung-cancer chemicals. Here, we report a perturbation theory machine learning model based on a multilayer perceptron (PTML-MLP) model for phenotypic early antineoplastic drug discovery, enabling the rational design and prediction of new molecules as virtual versatile inhibitors of multiple lung cancer cell lines. The PTML-MLP model achieved an accuracy above 80%. We applied the fragment-based topological design (FBTD) approach to physicochemically and structurally interpret the PTML-MLP model. This enabled the extraction of suitable fragments with a positive influence on anti-lung-cancer activity against the different lung cancer cell lines. By following the aforementioned interpretations, we could assemble several suitable fragments to design four novel molecules, which were predicted by the PTML-MLP model as versatile anti-lung-cancer agents. Such predictions of potent multi-cellular anticancer activity against diverse lung cancer cell lines were rigorously confirmed by a well-established virtual screening tool reported in the literature. The present work envisages new opportunities for the application of PTML models to accelerate early antineoplastic discovery from phenotypic assays. Full article
(This article belongs to the Special Issue Bioinformatics & Computational Biology)
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