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29 pages, 10764 KiB

Open AccessArticle

In Silico Drug Screening for Hepatitis C Virus Using QSAR-ML and Molecular Docking with Rho-Associated Protein Kinase 1 (ROCK1) Inhibitors

by Joshua R. De Borja and Heherson S. Cabrera

Computation 2024, 12(9), 175; https://doi.org/10.3390/computation12090175 - 31 Aug 2024

Viewed by 1474

Abstract

The enzyme ROCK1 plays a pivotal role in the disruption of the tight junction protein CLDN1, a downstream effector influencing various cellular functions such as cell migration, adhesion, and polarity. Elevated levels of ROCK1 pose challenges in HCV, where CLDN1 serves as a [...] Read more.

The enzyme ROCK1 plays a pivotal role in the disruption of the tight junction protein CLDN1, a downstream effector influencing various cellular functions such as cell migration, adhesion, and polarity. Elevated levels of ROCK1 pose challenges in HCV, where CLDN1 serves as a crucial entry factor for viral infections. This study integrates a drug screening protocol, employing a combination of quantitative structure–activity relationship machine learning (QSAR-ML) techniques; absorption, distribution, metabolism, and excretion (ADME) predictions; and molecular docking. This integrated approach allows for the effective screening of specific compounds, using their calculated features and properties as guidelines for selecting drug-like candidates targeting ROCK1 inhibition in HCV treatment. The QSAR-ML model, validated with scores of 0.54 (R²), 0.15 (RMSE), and 0.71 (CCC), demonstrates its predictive capabilities. The ADME-Docking study’s final results highlight notable compounds from ZINC15, specifically ZINC000071318464, ZINC000073170040, ZINC000058568630, ZINC000058591055, and ZINC000058574949. These compounds exhibit the best ranking Vina scores for protein–ligand binding with the crystal structure of ROCK1 at the C2 pocket site. The generated features and calculated pIC₅₀ bioactivity of these compounds provide valuable insights, facilitating the identification of structurally similar candidates in the ongoing exploration of drugs for ROCK1 inhibition. Full article

(This article belongs to the Special Issue 10th Anniversary of Computation—Computational Biology)

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20 pages, 4861 KiB

Open AccessArticle

Evaluation of the Dynamics of Psychological Panic Factor, Glucose Risk and Estrogen Effects on Breast Cancer Model

by Zahraa Aamer, Shireen Jawad, Belal Batiha, Ali Hasan Ali, Firas Ghanim and Alina Alb Lupaş

Computation 2024, 12(8), 160; https://doi.org/10.3390/computation12080160 - 8 Aug 2024

Viewed by 773

Abstract

Contracting cancer typically induces a state of terror among the individuals who are affected. Exploring how glucose excess, estrogen excess, and anxiety work together to affect the speed at which breast cancer cells multiply and the immune system’s response model is necessary to [...] Read more.

Contracting cancer typically induces a state of terror among the individuals who are affected. Exploring how glucose excess, estrogen excess, and anxiety work together to affect the speed at which breast cancer cells multiply and the immune system’s response model is necessary to conceive of ways to stop the spread of cancer. This paper proposes a mathematical model to investigate the impact of psychological panic, glucose excess, and estrogen excess on the interaction of cancer and immunity. The proposed model is precisely described. The focus of the model’s dynamic analysis is to identify the potential equilibrium locations. According to the analysis, it is possible to establish four equilibrium positions. The stability analysis reveals that all equilibrium points consistently exhibit stability under the defined conditions. The transcritical bifurcation occurs when the glucose excess is taken as a bifurcation point. Numerical simulations are employed to validate the theoretical study, which shows that psychological panic, glucose excess, and estrogen excess could be significant contributors to the spread of tumors and weakness of immune function. Full article

(This article belongs to the Special Issue 10th Anniversary of Computation—Computational Biology)

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25 pages, 12360 KiB

Open AccessArticle

Identification and Dynamics Understanding of Novel Inhibitors of Peptidase Domain of Collagenase G from Clostridium histolyticum

by Farah Anjum, Ali Hazazi, Fouzeyyah Ali Alsaeedi, Maha Bakhuraysah, Alaa Shafie, Norah Ali Alshehri, Nahed Hawsawi, Amal Adnan Ashour, Hamsa Jameel Banjer, Afaf Alharthi and Maryam Ishrat Niaz

Computation 2024, 12(8), 153; https://doi.org/10.3390/computation12080153 - 25 Jul 2024

Viewed by 1016

Abstract

Clostridium histolyticum is a Gram-positive anaerobic bacterium belonging to the Clostridium genus. It produces collagenase, an enzyme involved in breaking down collagen which is a key component of connective tissues. However, antimicrobial resistance (AMR) poses a great challenge in combating infections caused by [...] Read more.

Clostridium histolyticum is a Gram-positive anaerobic bacterium belonging to the Clostridium genus. It produces collagenase, an enzyme involved in breaking down collagen which is a key component of connective tissues. However, antimicrobial resistance (AMR) poses a great challenge in combating infections caused by this bacteria. The lengthy nature of traditional drug development techniques has resulted in a shift to computer-aided drug design and other modern drug discovery approaches. The above method offers a cost-effective means for gathering comprehensive information about how ligands interact with their target proteins. The objective of this study is to create novel, explicit drugs that specifically inhibit the C. histolyticum collagenase enzyme. Through structure-based virtual screening, a library containing 1830 compounds was screened to identify potential drug candidates against collagenase enzymes. Following that, molecular dynamic (MD) simulation was performed in an aqueous solution to evaluate the behavior of protein and ligand in a dynamic environment while density functional theory (DFT) analysis was executed to predict the molecular properties and structure of lead compounds, and the WaterSwap technique was utilized to obtain insights into the drug–protein interaction with water molecules. Furthermore, principal component analysis (PCA) was performed to reveal conformational changes, salt bridges to express electrostatic interaction and protein stability, and absorption, distribution, metabolism, excretion, and toxicity (ADMET) to assess the pharmacokinetics profile of top compounds and control molecules. Three potent drug candidates were identified MSID000001, MSID000002, MSID000003, and the control with a binding score of −10.7 kcal/mol, −9.8 kcal/mol, −9.5 kcal/mol, and −8 kcal/mol, respectively. Furthermore, Molecular Mechanics Poisson–Boltzmann Surface Area (MMPBSA) analysis of the simulation trajectories revealed energy scores of −79.54 kcal/mol, −73.99 kcal/mol, −62.26 kcal/mol, and −70.66 kcal/mol, correspondingly. The pharmacokinetics properties exhibited were under the acceptable range. The compounds hold the potential to be novel drugs; therefore, further investigation needs to be conducted to find out their anti-collagenase action against C. histolyticum infections and antibiotic resistance. Full article

(This article belongs to the Special Issue 10th Anniversary of Computation—Computational Biology)

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19 pages, 8959 KiB

Open AccessArticle

Mathematical Modeling of the Drug Particles Deposition in the Human Respiratory System—Part 1: Development of Virtual Models of the Upper and Lower Respiratory Tract

by Natalia Menshutina, Elizaveta Mokhova and Andrey Abramov

Computation 2024, 12(7), 134; https://doi.org/10.3390/computation12070134 - 1 Jul 2024

Viewed by 861

Abstract

In order to carry out mathematical modeling of the drug particles or drop movement in the human respiratory system, an approach to reverse prototyping of the studied areas based on the medical data (computed tomography) results is presented. To adapt the computational grid, [...] Read more.

In order to carry out mathematical modeling of the drug particles or drop movement in the human respiratory system, an approach to reverse prototyping of the studied areas based on the medical data (computed tomography) results is presented. To adapt the computational grid, a mathematical model of airflow in channels of complex geometry (respiratory system) has been developed. Based on the data obtained, the results of computational experiments for a single-phase system are presented. Full article

(This article belongs to the Special Issue 10th Anniversary of Computation—Computational Biology)

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41 pages, 5073 KiB

Open AccessArticle

Structure-Based Discovery of Potential HPV E6 and EBNA1 Inhibitors: Implications for Cervical Cancer Treatment

by Emmanuel Broni, Carolyn N. Ashley, Miriam Velazquez, Patrick O. Sakyi, Samuel K. Kwofie and Whelton A. Miller III

Computation 2024, 12(6), 112; https://doi.org/10.3390/computation12060112 - 31 May 2024

Viewed by 1587

Abstract

Cervical cancer is the fourth most diagnosed cancer and the fourth leading cause of cancer death in women globally. Its onset and progression have been attributed to high-risk human papillomavirus (HPV) types, especially 16 and 18, while the Epstein–Barr virus (EBV) is believed [...] Read more.

Cervical cancer is the fourth most diagnosed cancer and the fourth leading cause of cancer death in women globally. Its onset and progression have been attributed to high-risk human papillomavirus (HPV) types, especially 16 and 18, while the Epstein–Barr virus (EBV) is believed to also significantly contribute to cervical cancer growth. The E6 protein associated with high-risk HPV strains, such as HPV16 and HPV18, is known for its role in promoting cervical cancer and other anogenital cancers. E6 proteins contribute to the malignant transformation of infected cells by targeting and degrading tumor suppressor proteins, especially p53. On the other hand, EBV nuclear antigen 1 (EBNA1) plays a crucial role in the maintenance and replication of the EBV genome in infected cells. EBNA1 is believed to increase HPV E6 and E7 levels, as well as c-MYC, and BIRC5 cellular genes in the HeLa cell line, implying that HPV/EBV co-infection accelerates cervical cancer onset and growth. Thus, the E6 and EBNA1 antigens of HPV and EBV, respectively, are attractive targets for cervical cancer immunotherapy. This study, therefore, virtually screened for potential drug candidates with good binding affinity to all three oncoviral proteins, HPV16 E6, HPV18 E6, and EBNA1. The compounds were further subjected to ADMET profiling, biological activity predictions, molecular dynamics (MD) simulations, and molecular mechanics Poisson–Boltzmann surface area (MM/PBSA) calculations. A total of six compounds comprising ZINC000013380012, ZINC000070454124, ZINC000014588133, ZINC000085568136, ZINC000095909247, and ZINC000085597263 demonstrated very strong affinity (≤−60 kJ/mol) to the three oncoviral proteins (EBNA1, HPV16 E6, and HPV18 E6) after being subjected to docking, MD, and MM/PBSA. These compounds demonstrated relatively stronger binding than the controls used, inhibitors of EBNA1 (VK-1727) and HPV E6 (baicalein and gossypetin). Biological activity predictions also corroborated their antineoplastic, p53-enhancing, Pin1 inhibitory, and JAK2 inhibitory activities. Further experimental testing is required to validate the ability of the shortlisted compounds to silence the insidious effects of HPV E6 and EBNA1 proteins in cervical cancers. Full article

(This article belongs to the Special Issue 10th Anniversary of Computation—Computational Biology)

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19 pages, 4992 KiB

Open AccessArticle

Intraplatelet Calcium Signaling Regulates Thrombus Growth under Flow: Insights from a Multiscale Model

by Anass Bouchnita and Vitaly Volpert

Computation 2024, 12(5), 99; https://doi.org/10.3390/computation12050099 - 12 May 2024

Cited by 1 | Viewed by 1134

Abstract

In injured arteries, platelets adhere to the subendothelium and initiate the coagulation process. They recruit other platelets and form a plug that stops blood leakage. The formation of the platelet plug depends on platelet activation, a process that is regulated by intracellular calcium [...] Read more.

In injured arteries, platelets adhere to the subendothelium and initiate the coagulation process. They recruit other platelets and form a plug that stops blood leakage. The formation of the platelet plug depends on platelet activation, a process that is regulated by intracellular calcium signaling. Using an improved version of a previous multiscale model, we study the effects of changes in calcium signaling on thrombus growth. This model utilizes the immersed boundary method to capture the interplay between platelets and the flow. Each platelet can attach to other platelets, become activated, express proteins on its surface, detach, and/or become non-adhesive. Platelet activation is captured through a specific calcium signaling model that is solved at the intracellular level, which considers calcium activation by agonists and contacts. Simulations reveal a contact-dependent activation threshold necessary for the formation of the thrombus core. Next, we evaluate the effect of knocking out the P2Y and PAR receptor families. Further, we show that blocking P2Y receptors reduces platelet numbers in the shell while slightly increasing the core size. An analysis of the contribution of P2Y and PAR activation to intraplatelet calcium signaling reveals that each of the ADP and thrombin agonists promotes the activation of platelets in different regions of the thrombus. Finally, the model predicts that the heterogeneity in platelet size reduces the overall number of platelets recruited by the thrombus. The presented framework can be readily used to study the effect of antiplatelet therapy under different physiological and pathological blood flow, platelet count, and activation conditions. Full article

(This article belongs to the Special Issue 10th Anniversary of Computation—Computational Biology)

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14 pages, 1733 KiB

Open AccessArticle

Physically Informed Deep Learning Technique for Estimating Blood Flow Parameters in Four-Vessel Junction after the Fontan Procedure

by Alexander Isaev, Tatiana Dobroserdova, Alexander Danilov and Sergey Simakov

Computation 2024, 12(3), 41; https://doi.org/10.3390/computation12030041 - 25 Feb 2024

Cited by 1 | Viewed by 2245

Abstract

This study introduces an innovative approach leveraging physics-informed neural networks (PINNs) for the efficient computation of blood flows at the boundaries of a four-vessel junction formed by a Fontan procedure. The methodology incorporates a 3D mesh generation technique based on the parameterization of [...] Read more.

This study introduces an innovative approach leveraging physics-informed neural networks (PINNs) for the efficient computation of blood flows at the boundaries of a four-vessel junction formed by a Fontan procedure. The methodology incorporates a 3D mesh generation technique based on the parameterization of the junction’s geometry, coupled with an advanced physically regularized neural network architecture. Synthetic datasets are generated through stationary 3D Navier–Stokes simulations within immobile boundaries, offering a precise alternative to resource-intensive computations. A comparative analysis of standard grid sampling and Latin hypercube sampling data generation methods is conducted, resulting in datasets comprising

1.1 \times 10^{4}

and

5 \times 10^{3}

samples, respectively. The following two families of feed-forward neural networks (FFNNs) are then compared: the conventional “black-box” approach using mean squared error (MSE) and a physically informed FFNN employing a physically regularized loss function (PRLF), incorporating mass conservation law. The study demonstrates that combining PRLF with Latin hypercube sampling enables the rapid minimization of relative error (RE) when using a smaller dataset, achieving a relative error value of

6 %

on the test set. This approach offers a viable alternative to resource-intensive simulations, showcasing potential applications in patient-specific 1D network models of hemodynamics. Full article

(This article belongs to the Special Issue 10th Anniversary of Computation—Computational Biology)

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13 pages, 4648 KiB

Open AccessArticle

Data-Driven Anisotropic Biomembrane Simulation Based on the Laplace Stretch

by Alexey Liogky and Victoria Salamatova

Computation 2024, 12(3), 39; https://doi.org/10.3390/computation12030039 - 22 Feb 2024

Viewed by 1563

Abstract

Data-driven simulations are gaining popularity in mechanics of biomaterials since they do not require explicit form of constitutive relations. Data-driven modeling based on neural networks lacks interpretability. In this study, we propose an interpretable data-driven finite element modeling for hyperelastic materials. This approach [...] Read more.

Data-driven simulations are gaining popularity in mechanics of biomaterials since they do not require explicit form of constitutive relations. Data-driven modeling based on neural networks lacks interpretability. In this study, we propose an interpretable data-driven finite element modeling for hyperelastic materials. This approach employs the Laplace stretch as the strain measure and utilizes response functions to define constitutive equations. To validate the proposed method, we apply it to inflation of anisotropic membranes on the basis of synthetic data for porcine skin represented by Holzapfel-Gasser-Ogden model. Our results demonstrate applicability of the method and show good agreement with reference displacements, although some discrepancies are observed in the stress calculations. Despite these discrepancies, the proposed method demonstrates its potential usefulness for simulation of hyperelastic biomaterials. Full article

(This article belongs to the Special Issue 10th Anniversary of Computation—Computational Biology)

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13 pages, 317 KiB

Open AccessArticle

Mathematical Modeling of Cell Growth via Inverse Problem and Computational Approach

by Ivanna Andrusyak, Oksana Brodyak, Petro Pukach and Myroslava Vovk

Computation 2024, 12(2), 26; https://doi.org/10.3390/computation12020026 - 3 Feb 2024

Viewed by 1940

Abstract

A simple cell population growth model is proposed, where cells are assumed to have a physiological structure (e.g., a model describing cancer cell maturation, where cells are structured by maturation stage, size, or mass). The main question is whether we can guarantee, using [...] Read more.

A simple cell population growth model is proposed, where cells are assumed to have a physiological structure (e.g., a model describing cancer cell maturation, where cells are structured by maturation stage, size, or mass). The main question is whether we can guarantee, using the death rate as a control mechanism, that the total number of cells or the total cell biomass has prescribed dynamics, which may be applied to modeling the effect of chemotherapeutic agents on malignant cells. Such types of models are usually described by partial differential equations (PDE). The population dynamics are modeled by an inverse problem for PDE in our paper. The main idea is to reduce this model to a simplified integral equation that can be more easily studied by various analytical and numerical methods. Our results were obtained using the characteristics method. Full article

(This article belongs to the Special Issue 10th Anniversary of Computation—Computational Biology)

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22 pages, 4834 KiB

Open AccessArticle

Computer Aided Structure-Based Drug Design of Novel SARS-CoV-2 Main Protease Inhibitors: Molecular Docking and Molecular Dynamics Study

by Dmitry S. Kolybalov, Evgenii D. Kadtsyn and Sergey G. Arkhipov

Computation 2024, 12(1), 18; https://doi.org/10.3390/computation12010018 - 20 Jan 2024

Cited by 1 | Viewed by 2490

Abstract

Severe acute respiratory syndrome Coronavirus 2 (SARS-CoV-2) virus syndrome caused the recent outbreak of COVID-19 disease, the most significant challenge to public health for decades. Despite the successful development of vaccines and promising therapies, the development of novel drugs is still in the [...] Read more.

Severe acute respiratory syndrome Coronavirus 2 (SARS-CoV-2) virus syndrome caused the recent outbreak of COVID-19 disease, the most significant challenge to public health for decades. Despite the successful development of vaccines and promising therapies, the development of novel drugs is still in the interests of scientific society. SARS-CoV-2 main protease M^pro is one of the key proteins for the lifecycle of the virus and is considered an intriguing target. We used a structure-based drug design approach as a part of the search of new inhibitors for SARS-CoV-2 M^pro and hence new potential drugs for treating COVID-19. Four structures of potential inhibitors of (4S)-2-(2-(1H-imidazol-5-yl)ethyl)-4-amino-2-(1,3-dihydroxypropyl)-3-hydroxy-5-(1H-imidazol-5-yl)pentanal (L¹), (2R,4S)-2-((1H-imidazol-4-yl)methyl)-4-chloro-8-hydroxy-7-(hydroxymethyl)octanoic acid (L²), 1,9-dihydroxy-6-(hydroxymethyl)-6-(((1S)-1,7,7-trimethylbicyclo [2.2.1]heptan-2-yl)amino)nonan-4-one (L³), and 2,4,6-tris((4H-1,2,4-triazol-3-yl)amino)benzonitrile (L⁴) were modeled. Three-dimensional structures of ligand–protein complexes were modeled and their potential binding efficiency proved. Docking and molecular dynamic simulations were performed for these compounds. Detailed trajectory analysis of the ligands’ binding conformation was carried out. Binding free energies were estimated by the MM/PBSA approach. Results suggest a high potential efficiency of the studied inhibitors. Full article

(This article belongs to the Special Issue 10th Anniversary of Computation—Computational Biology)

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28 pages, 3679 KiB

Open AccessArticle

Design of Inhibitors That Target the Menin–Mixed-Lineage Leukemia Interaction

by Moses N. Arthur, Kristeen Bebla, Emmanuel Broni, Carolyn Ashley, Miriam Velazquez, Xianin Hua, Ravi Radhakrishnan, Samuel K. Kwofie and Whelton A. Miller III

Computation 2024, 12(1), 3; https://doi.org/10.3390/computation12010003 - 27 Dec 2023

Viewed by 2411

Abstract

The prognosis of mixed-lineage leukemia (MLL) has remained a significant health concern, especially for infants. The minimal treatments available for this aggressive type of leukemia has been an ongoing problem. Chromosomal translocations of the KMT2A gene are known as MLL, which expresses MLL [...] Read more.

The prognosis of mixed-lineage leukemia (MLL) has remained a significant health concern, especially for infants. The minimal treatments available for this aggressive type of leukemia has been an ongoing problem. Chromosomal translocations of the KMT2A gene are known as MLL, which expresses MLL fusion proteins. A protein called menin is an important oncogenic cofactor for these MLL fusion proteins, thus providing a new avenue for treatments against this subset of acute leukemias. In this study, we report results using the structure-based drug design (SBDD) approach to discover potential novel MLL-mediated leukemia inhibitors from natural products against menin. The three-dimensional (3D) protein model was derived from Protein Databank (Protein ID: 4GQ4), and EasyModeller 4.0 and I-TASSER were used to fix missing residues during rebuilding. Out of the ten protein models generated (five from EasyModeller and I-TASSER each), one model was selected. The selected model demonstrated the most reasonable quality and had 75.5% of residues in the most favored regions, 18.3% of residues in additionally allowed regions, 3.3% of residues in generously allowed regions, and 2.9% of residues in disallowed regions. A ligand library containing 25,131 ligands from a Chinese database was virtually screened using AutoDock Vina, in addition to three known menin inhibitors. The top 10 compounds including ZINC000103526876, ZINC000095913861, ZINC000095912705, ZINC000085530497, ZINC000095912718, ZINC000070451048, ZINC000085530488, ZINC000095912706, ZINC000103580868, and ZINC000103584057 had binding energies of −11.0, −10.7, −10.6, −10.2, −10.2, −9.9, −9.9, −9.9, −9.9, and −9.9 kcal/mol, respectively. To confirm the stability of the menin–ligand complexes and the binding mechanisms, molecular dynamics simulations including molecular mechanics Poisson–Boltzmann surface area (MM/PBSA) computations were performed. The amino acid residues that were found to be potentially crucial in ligand binding included Phe243, Met283, Cys246, Tyr281, Ala247, Ser160, Asn287, Asp185, Ser183, Tyr328, Asn249, His186, Leu182, Ile248, and Pro250. MI-2-2 and PubChem CIDs 71777742 and 36294 were shown to possess anti-menin properties; thus, this justifies a need to experimentally determine the activity of the identified compounds. The compounds identified herein were found to have good pharmacological profiles and had negligible toxicity. Additionally, these compounds were predicted as antileukemic, antineoplastic, chemopreventive, and apoptotic agents. The 10 natural compounds can be further explored as potential novel agents for the effective treatment of MLL-mediated leukemia. Full article

(This article belongs to the Special Issue 10th Anniversary of Computation—Computational Biology)

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28 pages, 10033 KiB

Open AccessArticle

In Silico Identification of Natural Products and World-Approved Drugs Targeting the KEAP1/NRF2 Pathway Endowed with Potential Antioxidant Profile

by Simone Brogi, Ilaria Guarino, Lorenzo Flori, Hajar Sirous and Vincenzo Calderone

Computation 2023, 11(12), 255; https://doi.org/10.3390/computation11120255 - 16 Dec 2023

Cited by 1 | Viewed by 2594

Abstract

In this study, we applied a computer-based protocol to identify novel antioxidant agents that can reduce oxidative stress (OxS), which is one of the main hallmarks of several disorders, including cancer, cardiovascular disease, and neurodegenerative disorders. Accordingly, the identification of novel and safe [...] Read more.

In this study, we applied a computer-based protocol to identify novel antioxidant agents that can reduce oxidative stress (OxS), which is one of the main hallmarks of several disorders, including cancer, cardiovascular disease, and neurodegenerative disorders. Accordingly, the identification of novel and safe agents, particularly natural products, could represent a valuable strategy to prevent and slow down the cellular damage caused by OxS. Employing two chemical libraries that were properly prepared and enclosing both natural products and world-approved and investigational drugs, we performed a high-throughput docking campaign to identify potential compounds that were able to target the KEAP1 protein. This protein is the main cellular component, along with NRF2, that is involved in the activation of the antioxidant cellular pathway. Furthermore, several post-search filtering approaches were applied to improve the reliability of the computational protocol, such as the evaluation of ligand binding energies and the assessment of the ADMET profile, to provide a final set of compounds that were evaluated by molecular dynamics studies for their binding stability. By following the screening protocol mentioned above, we identified a few undisclosed natural products and drugs that showed great promise as antioxidant agents. Considering the natural products, isoxanthochymol, gingerenone A, and meranzin hydrate showed the best predicted profile for behaving as antioxidant agents, whereas, among the drugs, nedocromil, zopolrestat, and bempedoic acid could be considered for a repurposing approach to identify possible antioxidant agents. In addition, they showed satisfactory ADMET properties with a safe profile, suggesting possible long-term administration. In conclusion, the identified compounds represent a valuable starting point for the identification of novel, safe, and effective antioxidant agents to be employed in cell-based tests and in vivo studies to properly evaluate their action against OxS and the optimal dosage for exerting antioxidant effects. Full article

(This article belongs to the Special Issue 10th Anniversary of Computation—Computational Biology)

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37 pages, 2321 KiB

Open AccessArticle

Mathematical Model for Chemical Reactions in Electrolytes Applied to Cytochrome c Oxidase: An Electro-Osmotic Approach

by Shixin Xu, Robert Eisenberg, Zilong Song and Huaxiong Huang

Computation 2023, 11(12), 253; https://doi.org/10.3390/computation11120253 - 11 Dec 2023

Cited by 2 | Viewed by 2160

Abstract

This study introduces a mathematical model for electrolytic chemical reactions, employing an energy variation approach grounded in classical thermodynamics. Our model combines electrostatics and chemical reactions within well-defined energetic and dissipative functionals. Extending the energy variation method to open systems consisting of charge, [...] Read more.

This study introduces a mathematical model for electrolytic chemical reactions, employing an energy variation approach grounded in classical thermodynamics. Our model combines electrostatics and chemical reactions within well-defined energetic and dissipative functionals. Extending the energy variation method to open systems consisting of charge, mass, and energy inputs, this model explores energy transformation from one form to another. Electronic devices and biological channels and transporters are open systems. By applying this generalized approach, we investigate the conversion of an electrical current to a proton flow by cytochrome c oxidase, a vital mitochondrial enzyme contributing to ATP production, the ‘energetic currency of life’. This model shows how the enzyme’s structure directs currents and mass flows governed by energetic and dissipative functionals. The interplay between electron and proton flows, guided by Kirchhoff’s current law within the mitochondrial membrane and the mitochondria itself, determines the function of the systems, where electron flows are converted into proton flows and gradients. This important biological system serves as a practical example of the use of energy variation methods to deal with electrochemical reactions in open systems. We combine chemical reactions and Kirchhoff’s law in a model that is much simpler to implement than a full accounting of all the charges in a chemical system. Full article

(This article belongs to the Special Issue 10th Anniversary of Computation—Computational Biology)

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20 pages, 8244 KiB

Open AccessArticle

Deep Reinforcement Learning for Efficient Digital Pap Smear Analysis

by Carlos Macancela, Manuel Eugenio Morocho-Cayamcela and Oscar Chang

Computation 2023, 11(12), 252; https://doi.org/10.3390/computation11120252 - 10 Dec 2023

Viewed by 2133

Abstract

In August 2020, the World Health Assembly launched a global initiative to eliminate cervical cancer by 2030, setting three primary targets. One key goal is to achieve a 70% screening coverage rate for cervical cancer, primarily relying on the precise analysis of Papanicolaou [...] Read more.

In August 2020, the World Health Assembly launched a global initiative to eliminate cervical cancer by 2030, setting three primary targets. One key goal is to achieve a 70% screening coverage rate for cervical cancer, primarily relying on the precise analysis of Papanicolaou (Pap) or digital Pap smears. However, the responsibility of reviewing Pap smear samples to identify potentially cancerous cells primarily falls on pathologists—a task known to be exceptionally challenging and time-consuming. This paper proposes a solution to address the shortage of pathologists for cervical cancer screening. It leverages the OpenAI-GYM API to create a deep reinforcement learning environment utilizing liquid-based Pap smear images. By employing the Proximal Policy Optimization algorithm, autonomous agents navigate Pap smear images, identifying cells with the aid of rewards, penalties, and accumulated experiences. Furthermore, the use of a pre-trained convolutional neuronal network like Res-Net50 enhances the classification of detected cells based on their potential for malignancy. The ultimate goal of this study is to develop a highly efficient, automated Papanicolaou analysis system, ultimately reducing the need for human intervention in regions with limited pathologists. Full article

(This article belongs to the Special Issue 10th Anniversary of Computation—Computational Biology)

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17 pages, 1501 KiB

Open AccessArticle

Global Dynamics of a Within-Host Model for Usutu Virus

by Ibrahim Nali and Attila Dénes

Computation 2023, 11(11), 226; https://doi.org/10.3390/computation11110226 - 14 Nov 2023

Cited by 2 | Viewed by 1610

Abstract

We propose a within-host mathematical model for the dynamics of Usutu virus infection, incorporating Crowley–Martin functional response. The basic reproduction number

R_{0}

is found by applying the next-generation matrix approach. Depending on this threshold, parameter, global asymptotic stability of one of the [...] Read more.

We propose a within-host mathematical model for the dynamics of Usutu virus infection, incorporating Crowley–Martin functional response. The basic reproduction number

R_{0}

is found by applying the next-generation matrix approach. Depending on this threshold, parameter, global asymptotic stability of one of the two possible equilibria is also established via constructing appropriate Lyapunov functions and using LaSalle’s invariance principle. We present numerical simulations to illustrate the results and a sensitivity analysis of

R_{0}

was also completed. Finally, we fit the model to actual data on Usutu virus titers. Our study provides new insights into the dynamics of Usutu virus infection. Full article

(This article belongs to the Special Issue 10th Anniversary of Computation—Computational Biology)

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20 pages, 390 KiB

Open AccessArticle

Evaluating the Performance of Multiple Sequence Alignment Programs with Application to Genotyping SARS-CoV-2 in the Saudi Population

by Aminah Alqahtani and Meznah Almutairy

Computation 2023, 11(11), 212; https://doi.org/10.3390/computation11110212 - 1 Nov 2023

Cited by 2 | Viewed by 3085

Abstract

This study explores the accuracy and efficiency of multiple sequence alignment (MSA) programs, focusing on Clustal

Ω

, MAFFT, and MUSCLE in the context of genotyping SARS-CoV-2 for the Saudi population. Our results indicate that MAFFT outperforms the others, making it an ideal [...] Read more.

This study explores the accuracy and efficiency of multiple sequence alignment (MSA) programs, focusing on Clustal

Ω

, MAFFT, and MUSCLE in the context of genotyping SARS-CoV-2 for the Saudi population. Our results indicate that MAFFT outperforms the others, making it an ideal choice for large-scale genomic analyses. The comparative performance of MSAs assembled using MergeAlign demonstrates that MAFFT and MUSCLE consistently exhibit higher accuracy than Clustal

Ω

in both reference-based and consensus-based approaches. The evaluation of genotyping effectiveness reveals that the addition of a reference sequence, such as the SARS-CoV-2 Wuhan-Hu-1 isolate, does not significantly affect the alignment process, suggesting that using consensus sequences derived from individual MSA alignments may yield comparable genotyping outcomes. Investigating single-nucleotide polymorphisms (SNPs) and mutations highlights distinctive features of MSA programs. Clustal

Ω

and MAFFT show similar counts, while MUSCLE displays the highest SNP count. High-frequency SNP analysis identifies MAFFT as the most accurate MSA program, emphasizing its reliability. Comparisons between Saudi and global SARS-CoV-2 populations underscore regional genetic variations. Saudis exhibit consistently higher frequencies of high-frequency SNPs, attributed to genetic similarity within the population. Transmission dynamics analysis reveals a higher frequency of co-mutations in the Saudi dataset, suggesting shared evolutionary patterns. These findings emphasize the importance of considering regional diversity in genetic analyses. Full article

(This article belongs to the Special Issue 10th Anniversary of Computation—Computational Biology)

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35 pages, 1670 KiB

Open AccessArticle

Stability of Impaired Humoral Immunity HIV-1 Models with Active and Latent Cellular Infections

by Noura H. AlShamrani, Reham H. Halawani, Wafa Shammakh and Ahmed M. Elaiw

Computation 2023, 11(10), 207; https://doi.org/10.3390/computation11100207 - 18 Oct 2023

Viewed by 1580

Abstract

This research aims to formulate and analyze two mathematical models describing the within-host dynamics of human immunodeficiency virus type-1 (HIV-1) in case of impaired humoral immunity. These models consist of five compartments, including healthy CD4

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T cells, (HIV-1)-latently infected cells, (HIV-1)-actively infected [...] Read more.

This research aims to formulate and analyze two mathematical models describing the within-host dynamics of human immunodeficiency virus type-1 (HIV-1) in case of impaired humoral immunity. These models consist of five compartments, including healthy CD4

^{+}

T cells, (HIV-1)-latently infected cells, (HIV-1)-actively infected cells, HIV-1 particles, and B-cells. We make the assumption that healthy cells can become infected when exposed to: (i) HIV-1 particles resulting from viral infection (VI), (ii) (HIV-1)-latently infected cells due to latent cellular infection (CI), and (iii) (HIV-1)-actively infected cells due to active CI. In the second model, we introduce distributed time-delays. For each of these systems, we demonstrate the non-negativity and boundedness of the solutions, calculate the basic reproductive number, identify all possible equilibrium states, and establish the global asymptotic stability of these equilibria. We employ the Lyapunov method in combination with LaSalle’s invariance principle to investigate the global stability of these equilibrium points. Theoretical findings are subsequently validated through numerical simulations. Additionally, we explore the impact of B-cell impairment, time-delays, and CI on HIV-1 dynamics. Our results indicate that weakened immunity significantly contributes to disease progression. Furthermore, the presence of time-delays can markedly decrease the basic reproductive number, thereby suppressing HIV-1 replication. Conversely, the existence of latent CI spread increases the basic reproductive number, intensifying the progression of HIV-1. Consequently, neglecting latent CI spread in the HIV-1 dynamics model can lead to an underestimation of the basic reproductive number, potentially resulting in inaccurate or insufficient drug therapies for eradicating HIV-1 from the body. These findings offer valuable insights that can enhance the understanding of HIV-1 dynamics within a host. Full article

(This article belongs to the Special Issue 10th Anniversary of Computation—Computational Biology)

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16 pages, 5312 KiB

Open AccessArticle

Mathematical Investigation of the Infection Dynamics of COVID-19 Using the Fractional Differential Quadrature Method

by M. Mohamed, S. M. Mabrouk and A. S. Rashed

Computation 2023, 11(10), 198; https://doi.org/10.3390/computation11100198 - 4 Oct 2023

Cited by 2 | Viewed by 1692

Abstract

In recent times, the global community has been faced with the unprecedented challenge of the coronavirus disease (COVID-19) pandemic, which has had a profound and enduring impact on both global health and the global economy. The utilization of mathematical modeling has become an [...] Read more.

In recent times, the global community has been faced with the unprecedented challenge of the coronavirus disease (COVID-19) pandemic, which has had a profound and enduring impact on both global health and the global economy. The utilization of mathematical modeling has become an essential instrument in the characterization and understanding of the dynamics associated with infectious illnesses. In this study, the utilization of the differential quadrature method (DQM) was employed in order to anticipate the characterization of the dynamics of COVID-19 through a fractional mathematical model. Uniform and non-uniform polynomial differential quadrature methods (PDQMs) and a discrete singular convolution method (DSCDQM) were employed in the examination of the dynamics of COVID-19 in vulnerable, exposed, deceased, asymptomatic, and recovered persons. An analysis was conducted to compare the methodologies used in this study, as well as the modified Euler method, in order to highlight the superior efficiency of the DQM approach in terms of code-execution times. The results demonstrated that the fractional order significantly influenced the outcomes. As the fractional order tended towards unity, the anticipated numbers of vulnerable, exposed, deceased, asymptomatic, and recovered individuals increased. During the initial week of the inquiry, there was a substantial rise in the number of individuals who contracted COVID-19, which was primarily attributed to the disease’s high transmission rate. As a result, there was an increase in the number of individuals who recovered, in tandem with the rise in the number of infected individuals. These results highlight the importance of the fractional order in influencing the dynamics of COVID-19. The utilization of the DQM approach, characterized by its proficient code-execution durations, provided significant insights into the dynamics of COVID-19 among diverse population cohorts and enhanced our comprehension of the evolution of the pandemic. The proposed method was efficient in dealing with ordinary differential equations (ODEs), partial differential equations (PDEs), and fractional differential equations (FDEs), in either linear or nonlinear forms. In addition, the stability of the DQM and its validity were verified during the present study. Moreover, the error analysis showed that DQM has better error percentages in many applications than other relevant techniques. Full article

(This article belongs to the Special Issue 10th Anniversary of Computation—Computational Biology)

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20 pages, 7217 KiB

Open AccessArticle

A Robust Deep Learning Approach for Accurate Segmentation of Cytoplasm and Nucleus in Noisy Pap Smear Images

by Nahida Nazir, Abid Sarwar, Baljit Singh Saini and Rafeeya Shams

Computation 2023, 11(10), 195; https://doi.org/10.3390/computation11100195 - 3 Oct 2023

Cited by 2 | Viewed by 2383

Abstract

Cervical cancer poses a significant global health burden, affecting women worldwide. Timely and accurate detection is crucial for effective treatment and improved patient outcomes. The Pap smear test has long been a standard cytology screening method, enabling early cancer diagnosis. However, to enhance [...] Read more.

Cervical cancer poses a significant global health burden, affecting women worldwide. Timely and accurate detection is crucial for effective treatment and improved patient outcomes. The Pap smear test has long been a standard cytology screening method, enabling early cancer diagnosis. However, to enhance quantitative analysis and refine diagnostic capabilities, precise segmentation of the cervical cytoplasm and nucleus using deep learning techniques holds immense promise. This research focuses on addressing the primary challenge of achieving accurate segmentation in the presence of noisy data commonly encountered in Pap smear images. Poisson noise, a prevalent type of noise, corrupts these images, impairing the precise delineation of the cytoplasm and nucleus. Consequently, segmentation boundaries become indistinct, leading to compromised overall accuracy. To overcome these limitations, the utilization of U-Net, a deep learning architecture specifically designed for automatic segmentation, has been proposed. This approach aims to mitigate the adverse effects of Poisson noise on the digitized Pap smear slides. The evaluation of the proposed methodology involved a dataset of 110 Pap smear slides. The experimental results demonstrate that the proposed approach successfully achieves precise segmentation of the nucleus and cytoplasm in noise-free images. By preserving the boundaries of both cellular components, the method facilitates accurate feature extraction, thus contributing to improved diagnostic capabilities. Comparative analysis between noisy and noise-free images reveals the superiority of the presented approach in terms of segmentation accuracy, as measured by various metrics, including the Dice coefficient, specificity, sensitivity, and intersection over union (IoU). The findings of this study underline the potential of deep-learning-based segmentation techniques to enhance cervical cancer diagnosis and pave the way for improved quantitative analysis in this critical field of women’s health. Full article

(This article belongs to the Special Issue 10th Anniversary of Computation—Computational Biology)

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20 pages, 3839 KiB

Open AccessArticle

MPC Controllers in SIIR Epidemic Models

by Nikita Kosyanov, Elena Gubar and Vladislav Taynitskiy

Computation 2023, 11(9), 173; https://doi.org/10.3390/computation11090173 - 4 Sep 2023

Viewed by 1338

Abstract

Infectious diseases are one of the most important problems of the modern world, for example, the periodic outbreaks of coronavirus infections caused by COVID-19, influenza, and many other respiratory diseases have significantly affected the economics of many countries. Hence, it is therefore important [...] Read more.

Infectious diseases are one of the most important problems of the modern world, for example, the periodic outbreaks of coronavirus infections caused by COVID-19, influenza, and many other respiratory diseases have significantly affected the economics of many countries. Hence, it is therefore important to minimize the economic damage, which includes both loss of work and treatment costs, quarantine costs, etc. Recent studies have presented many different models describing the dynamics of virus spread, which help to analyze the epidemic outbreaks. In the current work we focus on finding solutions that are robust to noise and take into account the dynamics of future changes in the process. We extend previous results by using a nonlinear model-predictive-control (MPC) controller to find effective controls. MPC is a computational mathematical method used in dynamically controlled systems with observations to find effective controls. Full article

(This article belongs to the Special Issue 10th Anniversary of Computation—Computational Biology)

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11 pages, 2846 KiB

Open AccessArticle

Genomic Phylogeny Using the Maxwell^TM Classifier Based on Burrows–Wheeler Transform

by Jacques Demongeot, Joël Gardes, Christophe Maldivi, Denis Boisset, Kenza Boufama and Imène Touzouti

Computation 2023, 11(8), 158; https://doi.org/10.3390/computation11080158 - 11 Aug 2023

Cited by 2 | Viewed by 1402

Abstract

Background: In present genomes, current relics of a circular RNA appear which could have played a central role as a primitive catalyst of the peptide genesis. Methods: Using a proximity measure to this circular RNA and the distance, a new unsupervised classifier called [...] Read more.

Background: In present genomes, current relics of a circular RNA appear which could have played a central role as a primitive catalyst of the peptide genesis. Methods: Using a proximity measure to this circular RNA and the distance, a new unsupervised classifier called Maxwell^TM has been constructed based on the Burrows–Wheeler transform algorithm. Results: By applying the classifier to numerous genomes from various realms (Bacteria, Archaea, Vegetables and Animals), we obtain phylogenetic trees that are coherent with biological trees based on pure evolutionary arguments. Discussion: We discuss the role of the combinatorial operators responsible for the evolution of the genome of many species. Conclusions: We opened up possibilities for understanding the mechanisms of a primitive factory of peptides represented by an RNA ring. We showed that this ring was able to transmit some of its sub-sequences in the sequences of genes involved in the mechanisms of the current ribosomal production of proteins. Full article

(This article belongs to the Special Issue 10th Anniversary of Computation—Computational Biology)

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30 pages, 492 KiB

Open AccessArticle

Computation of the Exact Forms of Waves for a Set of Differential Equations Associated with the SEIR Model of Epidemics

by Nikolay K. Vitanov and Zlatinka I. Dimitrova

Computation 2023, 11(7), 129; https://doi.org/10.3390/computation11070129 - 2 Jul 2023

Cited by 7 | Viewed by 4004

Abstract

We studied obtaining exact solutions to a set of equations related to the SEIR (Susceptible-Exposed-Infectious-Recovered) model of epidemic spread. These solutions may be used to model epidemic waves. We transformed the SEIR model into a differential equation that contained an exponential nonlinearity. This [...] Read more.

We studied obtaining exact solutions to a set of equations related to the SEIR (Susceptible-Exposed-Infectious-Recovered) model of epidemic spread. These solutions may be used to model epidemic waves. We transformed the SEIR model into a differential equation that contained an exponential nonlinearity. This equation was then approximated by a set of differential equations which contained polynomial nonlinearities. We solved several equations from the set using the Simple Equations Method (SEsM). In doing so, we obtained many new exact solutions to the corresponding equations. Several of these solutions can describe the evolution of epidemic waves that affect a small percentage of individuals in the population. Such waves have frequently been observed in the COVID-19 pandemic in recent years. The discussion shows that SEsM is an effective methodology for computing exact solutions to nonlinear differential equations. The exact solutions obtained can help us to understand the evolution of various processes in the modeled systems. In the specific case of the SEIR model, some of the exact solutions can help us to better understand the evolution of the quantities connected to the epidemic waves. Full article

(This article belongs to the Special Issue 10th Anniversary of Computation—Computational Biology)

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21 pages, 4559 KiB

Open AccessArticle

Application of Graph Theory and Automata Modeling for the Study of the Evolution of Metabolic Pathways with Glycolysis and Krebs Cycle as Case Studies

by Carlos De Las Morenas Mateos and Rafael Lahoz-Beltra

Computation 2023, 11(6), 107; https://doi.org/10.3390/computation11060107 - 28 May 2023

Cited by 1 | Viewed by 2645

Abstract

Today, graph theory represents one of the most important modeling techniques in biology. One of the most important applications is in the study of metabolic networks. During metabolism, a set of sequential biochemical reactions takes place, which convert one or more molecules into [...] Read more.

Today, graph theory represents one of the most important modeling techniques in biology. One of the most important applications is in the study of metabolic networks. During metabolism, a set of sequential biochemical reactions takes place, which convert one or more molecules into one or more final products. In a biochemical reaction, the transformation of one metabolite into the next requires a class of proteins called enzymes that are responsible for catalyzing the reaction. Whether by applying differential equations or automata theory, it is not easy to explain how the evolution of metabolic networks could have taken place within living organisms. Obviously, in the past, the assembly of biochemical reactions into a metabolic network depended on the independent evolution of the enzymes involved in the isolated biochemical reactions. In this work, a simulation model is presented where enzymes are modeled as automata, and their evolution is simulated with a genetic algorithm. This protocol is applied to the evolution of glycolysis and the Krebs cycle, two of the most important metabolic networks for the survival of organisms. The results obtained show how Darwinian evolution is able to optimize a biological network, such as in the case of glycolysis and Krebs metabolic networks. Full article

(This article belongs to the Special Issue 10th Anniversary of Computation—Computational Biology)

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10 pages, 847 KiB

Open AccessBrief Report

Minimizing Cohort Discrepancies: A Comparative Analysis of Data Normalization Approaches in Biomarker Research

by Alisa Tokareva, Natalia Starodubtseva, Vladimir Frankevich and Denis Silachev

Computation 2024, 12(7), 137; https://doi.org/10.3390/computation12070137 - 5 Jul 2024

Viewed by 865

Abstract

Biological variance among samples across different cohorts can pose challenges for the long-term validation of developed models. Data-driven normalization methods offer promising tools for mitigating inter-sample biological variance. We applied seven data-driven normalization methods to quantitative metabolome data extracted from rat dried blood [...] Read more.

Biological variance among samples across different cohorts can pose challenges for the long-term validation of developed models. Data-driven normalization methods offer promising tools for mitigating inter-sample biological variance. We applied seven data-driven normalization methods to quantitative metabolome data extracted from rat dried blood spots in the context of the Rice–Vannucci model of hypoxic–ischemic encephalopathy (HIE) in rats. The quality of normalization was assessed through the performance of Orthogonal Partial Least Squares (OPLS) models built on the training datasets; the sensitivity and specificity of these models were calculated by application to validation datasets. PQN, MRN, and VSN demonstrated a higher diagnostic quality of OPLS models than the other methods studied. The OPLS model based on VSN demonstrated superior performance (86% sensitivity and 77% specificity). After VSN, the VIP-identified potential biomarkers notably diverged from those identified using other normalization methods. Glycine consistently emerged as the top marker in six out of seven models, aligning perfectly with our prior research findings. Likewise, alanine exhibited a similar pattern. Notably, VSN uniquely highlighted pathways related to the oxidation of brain fatty acids and purine metabolism. Our findings underscore the widespread utility of VSN in metabolomics, suggesting its potential for use in large-scale and cross-study investigations. Full article

(This article belongs to the Special Issue 10th Anniversary of Computation—Computational Biology)

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