Special Issues
Table of Content

Recent Developments on Computational Biology-II

Submission Deadline: 28 February 2026 View: 1545 Submit to Special Issue

Guest Editors

Prof. Carlo Cattani, Tuscia University, Italy
Prof. Haci Mehmet Baskonus, Harran University, Turkey
Prof. Armando Ciancio, University of Messina, Italy


Summary

In modern time, experts started to use interdisciplinary properties with the developing of technology and science. Thus, these disciplines provide more sophisticated properties of real-world problems. In this sense, some models need to be investigated by using revised and modified traditional methods. The first discipline is the applied sciences such as physics, engineering, mechanics, electricity, biology, economy and mathematical applications. In this stage, many methods are developed and modified. To uncover the deep properties of problems is to use the main properties of such interdisciplinary properties. Furthermore, works conducted on such mathematical models including non-local operators, partial, ordinary and integer order have introduced a deeper investigations of problem for experts. By using technological tools, experts may observe more realistic and exact results of models.


This Special Issue is to render possible more investigation about the epidemiological properties of such models arising in nature based on non-local differential both theoretical and application aspects.

Topics of interest are given by the following fields and papers related to such fields are welcome.


•New epidemiological mathematical models

•Computational methods for differential equations with non-local operators

•New analytical and numerical methods to solve partial differential equations

•Analysis of Electrical engineering, epidemiological models

•Computer science on epidemiological models

•Deterministic and stochastic order models with non-local operators

•Non-local operator and application and physics

•Analytical and numerical methods for real-world problems

•Nonlinear dynamical complex system

•Systems analysis, simulation, design, and modelling

•Optimization Techniques

•Computer and Mathematical Modelling


Keywords

Mathematical models with integer, fractional and variable order; Analytical and Numerical methods; Approximate and exact properties of nonlinear partial differential models; Approximate Methods; Simulations of the wave distributions.

Published Papers


  • Open Access

    ARTICLE

    Demographic Heterogeneities in a Stochastic Chikungunya Virus Model with Poisson Random Measures and Near-Optimal Control under Markovian Regime Switching

    Maysaa Al-Qurashi, Ayesha Siddiqa, Shazia Karim, Yu-Ming Chu, Saima Rashid
    CMES-Computer Modeling in Engineering & Sciences, Vol.145, No.2, pp. 2057-2129, 2025, DOI:10.32604/cmes.2025.071629
    (This article belongs to the Special Issue: Recent Developments on Computational Biology-II)
    Abstract Chikungunya is a mosquito-borne viral infection caused by the chikungunya virus (CHIKV). It is characterized by acute onset of high fever, severe polyarthralgia, myalgia, headache, and maculopapular rash. The virus is rapidly spreading and may establish in new regions where competent mosquito vectors are present. This research analyzes the regulatory dynamics of a stochastic differential equation (SDE) model describing the transmission of the CHIKV, incorporating seasonal variations, immunization efforts, and environmental fluctuations modeled through Poisson random measure noise under demographic heterogeneity. The model guarantees the existence of a global positive solution and demonstrates periodic dynamics… More >

  • Open Access

    ARTICLE

    ELM-APDPs: An Explainable Ensemble Learning Method for Accurate Prediction of Druggable Proteins

    Mujeebu Rehman, Qinghua Liu, Ali Ghulam, Tariq Ahmad, Jawad Khan, Dildar Hussain, Yeong Hyeon Gu
    CMES-Computer Modeling in Engineering & Sciences, Vol.145, No.1, pp. 779-805, 2025, DOI:10.32604/cmes.2025.067412
    (This article belongs to the Special Issue: Recent Developments on Computational Biology-II)
    Abstract Identifying druggable proteins, which are capable of binding therapeutic compounds, remains a critical and resource-intensive challenge in drug discovery. To address this, we propose CEL-IDP (Comparison of Ensemble Learning Methods for Identification of Druggable Proteins), a computational framework combining three feature extraction methods Dipeptide Deviation from Expected Mean (DDE), Enhanced Amino Acid Composition (EAAC), and Enhanced Grouped Amino Acid Composition (EGAAC) with ensemble learning strategies (Bagging, Boosting, Stacking) to classify druggable proteins from sequence data. DDE captures dipeptide frequency deviations, EAAC encodes positional amino acid information, and EGAAC groups residues by physicochemical properties to generate… More >

  • Open Access

    ARTICLE

    Computational Solutions of a Delay-Driven Stochastic Model for Conjunctivitis Spread

    Ali Raza, Asad Ullah, Eugénio M. Rocha, Dumitru Baleanu, Hala H. Taha, Emad Fadhal
    CMES-Computer Modeling in Engineering & Sciences, Vol.144, No.3, pp. 3433-3461, 2025, DOI:10.32604/cmes.2025.069655
    (This article belongs to the Special Issue: Recent Developments on Computational Biology-II)
    Abstract This study investigates the transmission dynamics of conjunctivitis using stochastic delay differential equations (SDDEs). A delayed stochastic model is formulated by dividing the population into five distinct compartments: susceptible, exposed, infected, environmental irritants, and recovered individuals. The model undergoes thorough analytical examination, addressing key dynamical properties including positivity, boundedness, existence, and uniqueness of solutions. Local and global stability around the equilibrium points is studied with respect to the basic reproduction number. The existence of a unique global positive solution for the stochastic delayed model is established. In addition, a stochastic nonstandard finite difference scheme is More >

  • Open Access

    ARTICLE

    Epidemiological Modeling of Pneumococcal Pneumonia: Insights from ABC Fractal-Fractional Derivatives

    Mohammed Althubyani, Nidal E. Taha, Khdija O. Taha, Rasmiyah A. Alharb, Sayed Saber
    CMES-Computer Modeling in Engineering & Sciences, Vol.143, No.3, pp. 3491-3521, 2025, DOI:10.32604/cmes.2025.061640
    (This article belongs to the Special Issue: Recent Developments on Computational Biology-II)
    Abstract This study investigates the dynamics of pneumococcal pneumonia using a novel fractal-fractional Susceptible-Carrier-Infected-Recovered model formulated with the Atangana-Baleanu in Caputo (ABC) sense. Unlike traditional epidemiological models that rely on classical or Caputo fractional derivatives, the proposed model incorporates nonlocal memory effects, hereditary properties, and complex transmission dynamics through fractal-fractional calculus. The Atangana-Baleanu operator, with its non-singular Mittag-Leffler kernel, ensures a more realistic representation of disease progression compared to classical integer-order models and singular kernel-based fractional models. The study establishes the existence and uniqueness of the proposed system and conducts a comprehensive stability analysis, including local More >

  • Open Access

    ARTICLE

    A Design of Predictive Intelligent Networks for the Analysis of Fractional Model of TB-Virus

    Muhammad Asif Zahoor Raja, Aqsa Zafar Abbasi, Kottakkaran Sooppy Nisar, Ayesha Rafiq, Muhammad Shoaib
    CMES-Computer Modeling in Engineering & Sciences, Vol.143, No.2, pp. 2133-2153, 2025, DOI:10.32604/cmes.2025.058020
    (This article belongs to the Special Issue: Recent Developments on Computational Biology-II)
    Abstract Being a nonlinear operator, fractional derivatives can affect the enforcement of existence at any given time. As a result, the memory effect has an impact on all nonlinear processes modeled by fractional order differential equations (FODEs). The goal of this study is to increase the fractional model of the TB virus’s (FMTBV) accuracy. Stochastic solvers have never been used to solve FMTBV previously. The Bayesian regularized artificial (BRA) method and neural networks (NNs), often referred to as BRA-NNs, were used to solve the FMTBV model. Each scenario features five occurrences that each reflect a different… More >

  • Open Access

    ARTICLE

    Boundedness and Positivity Preserving Numerical Analysis of a Fuzzy-Parameterized Delayed Model for Foot and Mouth Disease Dynamics

    Muhammad Tashfeen, Fazal Dayan, Muhammad Aziz ur Rehman, Thabet Abdeljawad, Aiman Mukheimer
    CMES-Computer Modeling in Engineering & Sciences, Vol.141, No.3, pp. 2527-2554, 2024, DOI:10.32604/cmes.2024.056269
    (This article belongs to the Special Issue: Recent Developments on Computational Biology-II)
    Abstract Foot-and-mouth disease (FMD) is a viral disease that affects cloven-hoofed animals including cattle, pigs, and sheep, hence causing export bans among others, causing high economic losses due to reduced productivity. The global effect of FMD is most felt where livestock rearing forms an important source of income. It is therefore important to understand the modes of transmission of FMD to control its spread and prevent its occurrence. This work intends to address these dynamics by including the efficacy of active migrant animals transporting the disease from one area to another in a fuzzy mathematical modeling… More >

Share Link