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Metaheuristics for Protein Structure Prediction: Review and Empirical Analysis

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Metaheuristics for Protein Structure Prediction: Review and ...

Artificial Intelligence and Mechatronics Systems (AIMS), International Conference on

作者： Sunday O. Oladejo School for Data Science and Computational Thinking University of Stellenbosch Stellenbosch South Africa

Metaheuristics have been employed in solving several optimization and NP-hard problems owing to their ability to avoid local optima entrapment, flexibility and robustness, simplicity, and reasonable computational time. In this work, an empirical analysis is carried out on the application of metaheuristics in solving the protein structure prediction (PSP) problem. The PSP problem entails predicting a three-dimensional (3D) structure of proteins based on the amino acid sequences. This study employs the Ab Initio method, which relies on the thermodynamic properties of the proteins, such as the minimal free energy levels, bond angles, and torsional angles, in determining the protein structure. Five real protein sequences, namely 1CB3, 1BXL, 2H3S, 1TZ4, and 1CRN from the Protein data Bank (PDB), and 11 metaheuristics including DE, GA, PSO, DSO, ACO, and HS were employed in the empirical analysis. Moreover, extensive Monte Carlo simulations were carried out to generate the results of the empirical analysis and these results were statistically tested using the Friedman test.

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Using Deep Packet Inspection data to Examine Subscribers on the Network

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Procedia Computer science 2022年 215卷 182-191页

作者： Mike Nkongolo Jacobus Phillipus van Deventer Sydney Mambwe Kasongo Department of Informatics University of Pretoria Pretoria 0028 South Africa School of Data Science & Computational Thinking Stellenbosch University Stellenbosch 7600 South Africa

This article proposes the creation of the deep packet inspection (DPI) dataset to study subscribers’ behavior on the network, applying ensemble learning to this dataset, and comparing it with the UGRansome dataset. The subscriber can be thought of as a person or a group of users using a network service or connectivity. The DPI features represent the subscriber network usage, and the ensemble learning approach is implemented on the DPI dataset to predict the subscriber's service category on the network. The classification and prediction problem addressed on the DPI dataset reached a precision of 100%. The paper predicts that the web and streaming categories with Netflix, Facebook, and YouTube services will be the most utilized in the next few years. This study will lead to a better understanding of the idiosyncratic behavior of active subscribers on the network, exposing novel network anomalies and facilitating the development of novel DPI systems.

关键词： Deep packet inspection machine learning UGRansome telecommunication data science

Uncharted territory: the role of mitochondrial DNA variation in macrophage-mediated host susceptibility to tuberculosis

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Tuberculosis 2025年 153卷 102650-102650页

作者： Croock, Dayna Swart, Yolandi Sanko, Tomasz Janusz Mavumengwana, Vuyo Möller, Marlo Uren, Caitlin Petersen, Desiree C. South African Medical Research Council (SAMRC) Centre for Tuberculosis Research (CTR) Division of Molecular Biology and Human Genetics (MBHG) Department of Biomedical Sciences Faculty of Medicine and Health Sciences Stellenbosch University Cape Town South Africa Centre for Epidemic Response and Innovation (CERI) School of Data Science and Computational Thinking Stellenbosch University Stellenbosch South Africa Centre for Bioinformatics and Computational Biology Stellenbosch University Cape Town South Africa Genomics for Health in Africa (GHA) Africa-Europe Cluster of Research Excellence (CoRE) South Africa National Institute for Theoretical and Computational Sciences (NITheCS) South Africa South Africa

Mitochondria form an integral, yet frequently underappreciated, part of the immune response to Mycobacterium tuberculosis (***), particularly within macrophages. Despite growing recognition for their role in infection and immunity, studies investigating how mitochondrial DNA (mtDNA) variation influences host susceptibility to tuberculosis (TB) are limited. Notably, there are no studies in African-based populations, although Africans possess unparalleled human genetic diversity, including the earliest diverged mitochondrial haplogroups, and a high TB burden. This underrepresentation limits the discovery of novel ancestry-specific genetic loci associated with TB. In this review article, we describe the unique characteristics of mtDNA, highlight key mitochondrial functions relevant to macrophage responses during *** infection, and summarise published studies that investigate the role of host mtDNA variation in TB susceptibility. We further advocate for the inclusion of African populations in future studies to identify novel TB susceptibility genetic risk loci and expand the current knowledgebase on host TB susceptibility. © 2025 The Authors

关键词： Mitochondria Mitochondrial DNA (mtDNA) variation Mycobacterium tuberculosis (M.tb) Tuberculosis (TB) susceptibility

Benchmarking regularisation methods for quantum process tomography on NISQ devices

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The European Physical Journal Special Topics 2024年第20期232卷 3237-3250页

作者： David, I. J. Sinayskiy, I. Petruccione, F. School of Chemistry and Physics University of KwaZulu-Natal Durban South Africa National Institute for Theoretical and Computational Sciences (NITheCS) Stellenbosch South Africa School of Data Science and Computational Thinking and Department of Physics Stellenbosch University Stellenbosch South Africa

Quantum process tomography (QPT) is a crucial tool for characterizing and validating quantum devices and quantum algorithms. However, the problem of finite sampling leads to an estimated process matrix which is non-positive semi-definite (non-PSD), which can yield a reconstructed quantum channel that is non-physical. To address this problem, various methods have been proposed to correct the issue of finite sampling in the estimation of the process matrix. In this work, we perform a comparison of regularisation methods that will be used to tackle the problem of finite sampling in QPT. For this comparison we simulate some common single qubit quantum channels. We use two metrics, the minimum eigenvalue of the Choi matrix and the fidelity, to compare the effectiveness of these methods. Our results show that the spectral transformations perform the best overall in dealing with finite sampling present in reconstructing the quantum channel in the NISQ era.

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A Quantum Leaky Integrate-and-Fire Spiking Neuron and Network

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arXiv 2024年

作者： Brand, Dean Petruccione, Francesco Department of Physics School of Data Science and Computational Thinking Stellenbosch University Stellenbosch7604 South Africa Stellenbosch7604 South Africa

Quantum machine learning is in a period of rapid development and discovery, however it still lacks the resources and diversity of computational models of its classical complement. With the growing difficulties of classical models requiring extreme hardware and power solutions, and quantum models being limited by noisy intermediate-scale quantum (NISQ) hardware, there is an emerging opportunity to solve both problems together. Here we introduce a new software model for quantum neuromorphic computing — a quantum leaky integrate-and-fire (QLIF) neuron, implemented as a compact high-fidelity quantum circuit, requiring only 2 rotation gates and no CNOT gates. We use these neurons as building blocks in the construction of a quantum spiking neural network (QSNN), and a quantum spiking convolutional neural network (QSCNN), as the first of their kind. We apply these models to the MNIST, Fashion-MNIST, and KMNIST datasets for a full comparison with other classical and quantum models. We find that the proposed models perform competitively, with comparative accuracy, with efficient scaling and fast computation in classical simulation as well as on quantum devices. © 2024, CC BY.

关键词： Convolutional neural networks

Fool’s gold: ligand-receptor interactions and the origins of life

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arXiv 2024年

作者： Adams, Betony Illing, Angela Petruccione, Francesco School of Data Science and Computational Thinking Department of Physics Stellenbosch University South Africa National Institute for Theoretical and Computational Sciences South Africa The Guy Foundation Dorset United Kingdom

The origins of life is a question that continues to intrigue scientists across disciplines. One theory – the iron-sulphur theory – suggests that reactions essential to the synthesis of biological materials got their catalytic ‘spark’ from mineral surfaces such as iron pyrite, commonly known as fool’s gold. Additionally, the binding affinity of the ligands synthesised in this ‘surface metabolism’ acted as an early version of natural selection: more strongly-binding ligands were accumulated into further autocatalytic reactions and the aggregation of complex biological materials. Ligand-receptor binding is thus fundamental to the origins of life. In this paper, we use the iron-sulphur theory as a lens through which to review ligand-receptor interactions as they are more commonly understood today. In particular we focus on the electron tunnelling theory of receptor activation that has emerged from research into quantum biology. We revisit criticism against this theory, particularly the lack of evidence for electron transfer in receptors, to see what insights might be offered by ligand-receptor interactions mediated by iron pyrite at the origins of life. What emerges from this comparison is the central importance of redox activity in receptors, in particular with respect to the recurring presence of the disulphide bond. While the paper is a speculative exercise, we conclude that conductivity in biomolecules, particularly the selective conductivity conferred by appropriate ligand-receptor binding, is a powerful tool for understanding diverse phenomena such as pharmacological potency and viral infection. As such it deserves further investigation. © 2024, CC BY.

关键词： Ligands

Development of Phylum Chordata Sound Recognition System using Machine Learning and Deep Learning Techniques: A Case Study of Catfish

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Development of Phylum Chordata Sound Recognition System usin...

2023 IEEE International Conference on Electrical, Computer and Energy Technologies, ICECET 2023

作者： Akinyemi, Lateef Adesola Oshinuga, Olamide Peter Eshilokun, Oluwafikunmi Adegbenga Oladejo, Sunday Oladayo Ekwe, Stephen Obono Cset University of South Africa Departments of Computer Science and Electrical Engineering Johannesburg South Africa Lagos State University Department of Electronic and Computer Engineering Lagos Nigeria School for Data Science and Computational Thinking Stellenbosch University Stellenbosch South Africa Cape Peninsula University of Technology Department of Electrical Electronic and Computer Engineering Cape Town South Africa

ISBN: (纸本)9798350327816

Sound recognition refers to the technology or process of identifying and classifying different sounds or audio signals. This study aims to develop a sound recognition system using machine learning (ML) and deep learning (DL) techniques to classify and identify the gender of catfish based on their speech patterns. The study explores a range of supervised ML and DL algorithms, such as the Artificial Neural Network (ANN), Naïve Bayes (NB), and others, to predict the gender of catfish. The extracted features from the audio signals of the catfish serve as inputs to these algorithms, which produce binary predictions classifying the catfish samples as either male or female. This study further explores data analysis of the audio signals from the male and female catfish. The automated sound recognition system proves to be a more efficient and accurate approach compared to traditional methods, which rely on time-consuming visual observations. Evaluation metrics such as Accuracy, Sensitivity, and more, were employed to gauge the effectiveness of the algorithms in predicting the gender of catfish based on speech patterns. Among the employed algorithms, ANN and NB demonstrate the highest accuracy, achieving a score of 89.47%. © 2023 IEEE.

关键词： Catfish Deep Learning Gender Prediction Machine Learning Audio data Analysis Sound Recognition

Digital Simulation of Single Qubit Markovian Open Quantum Systems: A Tutorial

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arXiv 2023年

作者： David, I.J. Sinayskiy, I. Petruccione, F. School of Chemistry and Physics University of KwaZulu-Natal Durban South Africa South Africa School of Data Science and Computational Thinking Stellenbosch University Stellenbosch7604 South Africa

One of the first proposals for the use of quantum computers was the simulation of quantum systems. Over the past three decades, great strides have been made in the development of algorithms for simulating closed quantum systems and the more complex open quantum systems. In this tutorial, we introduce the methods used in the simulation of single qubit Markovian open quantum systems. It combines various existing notations into a common framework that can be extended to more complex open system simulation problems. The only currently available algorithm for the digital simulation of single qubit open quantum systems is discussed in detail. A modification to the implementation of the simpler channels is made that removes the need for classical random sampling, thus making the modified algorithm a strictly quantum algorithm. The modified algorithm makes use of quantum forking to implement the simpler channels that approximate the total channel. This circumvents the need for quantum circuits with a large number of C-NOT gates. Copyright © 2023, The Authors. All rights reserved.

关键词： Qubits

Pulsar Classification: Comparing Quantum Convolutional Neural Networks and Quantum Support Vector Machines

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arXiv 2023年

作者： Slabbert, Donovan Lourens, Matt Petruccione, Francesco Department of Physics Stellenbosch University Stellenbosch South Africa Stellenbosch South Africa School of Data Science and Computational Thinking Stellenbosch University Stellenbosch South Africa

Well-known quantum machine learning techniques, namely quantum kernel assisted support vector machines (QSVMs) and quantum convolutional neural networks (QCNNs), are applied to the binary classification of pulsars. In this comparitive study it is illustrated with simulations that both quantum methods successfully achieve effective classification of the HTRU-2 data set that connects pulsar class labels to eight separate features. QCNNs outperform the QSVMs with respect to time taken to train and predict, however, if the current NISQ era devices are considered and noise included in the comparison, then QSVMs are preferred. QSVMs also perform better overall compared to QCNNs when performance metrics are used to evaluate both methods. Classical methods are also implemented to serve as benchmark for comparison with the quantum approaches. Copyright © 2023, The Authors. All rights reserved.

关键词： Support vector machines