Computational evluation of protein energy functions by Mansour, Nashat

“…A protein is characterized by its 3D structure, which defines its biological function. …”
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Evolutionary algorithm for protein structure prediction by Mansour, Nashat

“…A protein is characterized by its 3D structure, which defines its biological function. …”
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Scatter Search algorithm for Protein Structure Prediction by Mansour, Nashat

“…Given the protein's sequence of Amino Acids (AAs), our algorithm produces a 3D structure that aims to minimise the energy function associated with the structure. …”
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Evolutionary algorithm for predicting all-atom protein structure by Mansour, Nashat

“…This algorithm produces a 3D structure of the whole protein, including back-bone and side-chain atoms, by minimizing the energy function associated with the structure. …”
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Scatter search for protein structure prediction. (c2008) by Kehyayan, Christine Houry

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Particle swarm optimization based algorithm for protein structure prediction in the 3D HP model. (c2008) by Kanj, Fatima Hassan

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Improved scatter search algorithm for predicting all-atoms protein structures using charmm22 energy model. (c2010) by Ghalayini, Iman Adnan

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Detecting protein complexes in protein interaction networks using Mapper and Graph Convolution Networks by Daou, Leonardo

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Consensus-Based Distributed Formation Control of Multi-Quadcopter Systems: Barrier Lyapunov Function Approach by Nargess Sadeghzadeh-Nokhodberiz (16904952)

“…The method is firstly developed in a centralized scheme and then extended to a distributed framework using appropriate asymptotically convergent consensus algorithms. Therefore, the asymptotic convergence of the designed distributed algorithm to the centralized one is guaranteed. …”

Fragment-based computational protein structure prediction by Mansour, Nashat

“…The 3-dimensional configuration determines a protein’s function. Hence, it is very important to determine the correct structure in order to identify the wrong folding that indicates a disease situation. …”
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Stochastic optimal power flow framework with incorporation of wind turbines and solar PVs using improved liver cancer algorithm by Noor Habib Khan (22224775)

“…To avoid such issues and provide the optimal solution, there are some modifications are implemented into the internal structure of t‐LCA based on Weibull flight operator, mutation‐based approach, quasi‐opposite‐based learning and gorilla troops exploitation‐based mechanisms to enhance the overall strength of the algorithm to obtain the global solution. For validation of ILCA, the non‐parametric and the statistical analysis are performed using benchmark standard functions. …”

Fragment based protein structure prediction. (c2013) by Terzian, Meghrig Ohanes

“…The results, evaluated on three proteins, show that the algorithm produces tertiary structures with promising root mean square deviations, within reasonable times.…”
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Distributed optimal coverage control in multi-agent systems: Known and unknown environments by Mohammadhasan Faghihi (22303057)

“…Lastly, the efficacy of the proposed method is demonstrated through simulations, and the obtained results are compared with those of Voronoi-based algorithms.</p><h2>Other Information</h2> <p> Published in: Automatica<br> License: <a href="http://creativecommons.org/licenses/by/4.0/" target="_blank">http://creativecommons.org/licenses/by/4.0/</a><br>See article on publisher's website: <a href="https://dx.doi.org/10.1016/j.automatica.2024.112031" target="_blank">https://dx.doi.org/10.1016/j.automatica.2024.112031</a></p>…”

An Introduction to the Special Issue “Protein Glycation in Food, Nutrition, Health and Disease” by Naila Rabbani (291722)

“…The keynote speaker was Lasker Laureate Professor Kazutoshi Mori, speaking on the unfolded protein response, and there were sessions on: glycation in obesity, diabetes, and diabetic complications; glycation in food; glycation through the life course—from maternal bonding to aging; glycation in plants—physiology, function, and food security; glycation in the COVID-19 response; glycation analytics and chemistry; glycation in kidney disease, cancer, and mental health; glycation-related imaging, diagnostic algorithms, and therapeutics; and methods and models in glycation research. …”

From Collatz Conjecture to chaos and hash function by Masrat Rasool (17807813)

“…The effectiveness and dependability of the proposed hash function are evaluated by comparing it with two well-known hash algorithms, namely SHA-3 and SHA-2, as well as several other Chaos-based hash algorithms. …”

Scatter search metaheuristic for homology based protein structure prediction. (c2015) by Stamboulian, Mouses Hrag

“…Determining a protein’s structure is a challenging goal in structural bioinformatics, offering important insight towards understanding the function of a protein. …”
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AGEomics Biomarkers and Machine Learning—Realizing the Potential of Protein Glycation in Clinical Diagnostics by Naila Rabbani (291722)

“…The term AGEomics is defined as multiplexed quantitation of spontaneous modification of proteins damage and other usually low-level modifications associated with a change of structure and function—for example, citrullination and transglutamination. …”

Accuracy of an internationally validated genetic-guided warfarin dosing algorithm compared to a clinical algorithm in an Arab population by Amr M. Fahmi (21632909)

“…To compare the accuracy of a clinical warfarin dosing (CWD) versus genetic warfarin dosing algorithms (GWD) during warfarin initiation. </p><h3>Methods</h3><p dir="ltr">A cohort of Arab patients newly starting on warfarin had their dose calculated using CWD published in www.warfarindosing.org and were followed for 1 month. …”

Particle swarm optimization approach for protein structure prediction in the 3D HP model by Mansour, Nashat

“…Proteins fold, under the influence of several chemical and physical factors, into their 3D structures, which determine their biological functions and properties. …”
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Ensemble Deep Random Vector Functional Link Neural Network for Regression by Minghui Hu (2457952)

“…<p dir="ltr">Inspired by the ensemble strategy of machine learning, deep random vector functional link (dRVFL), and ensemble dRVFL (edRVFL) has shown state-of-the-art results on different datasets. …”