The Influence of Allostery Governing the Changes in Protein Dynamics Upon Substitution

The focus of this research is to investigate the effects of allostery on the function/activity of an enzyme, human immunodeficiency virus type 1 (HIV-1) protease, using well-defined statistical analyses of the dynamic changes of the protein and variants with unique single point substitutions 1. The experimental data1 evaluated here only characterized HIV-1 protease with one of its potential target substrates. Probing the dynamic interactions of the residues of an enzyme and its variants can offer insight of the developmental importance for allosteric signaling and their connection to a protein’s function. The realignment of the secondary structure elements can modulate the mobility along with the frequency of residue contacts as well as which residues are making contact together2-5. We postulate that if there are more contacts occurring within a structure the mobility is being constrained and therefore gaining novel contacts can negatively influence the function of a protein. The evolutionary importance of protein dynamics is probed by analyzing the residue positions possessing significant correlations and the relationship between experimental information1 (variant activities). We propose that the correlated dynamics of residues observed to have considerable correlations, if disrupted, can be used to infer the function of HIV-1 protease and its variants. Given the robustness of HIV-1 protease the identification of any significant constraint imposed on the dynamics from a potential allosteric site found to disrupt the catalytic activity of the variant is not plainly evident. We also develop machine learning (ML) algorithms to predict the protein function/activity change caused by a single point substitution by using the DCC of each residue pair. Recognition of any substantial association between the dynamics of specific residues and allosteric communication or mechanism requires detailed examination of the dynamics of HIV-1 protease and its variants. We also explore the non-linear dependency between each pair of residues using Mutual Information (MI) and how it can influence the dynamics of HIV-1 protease and its variants. We suggest that if the residues of a protein receive more or less information than that of the WT it will adversely impact the function of the protein and can be used to support the classification of a variant structure. Furthermore, using the MI of residues obtained from the MD simulations for the HIV-1 protease structure, we build a ML model to predict a protein’s change in function caused by a single point substitution. Effectively the mobility, dynamics, and non-linear features tested in these studies are found to be useful towards the prediction of potentially drug resistant substitutions related to the catalytic efficiency of HIV-1 protease and the variants

Resampling Methods to Handle the Class-Imbalance Problems in Predicting Protein-Protein Interaction Site and Beta-Turn

13301甲第3852号博士（工学）金沢大学博士論文本文Ful

TOWARDS A HOLISTIC EFFICIENT STACKING ENSEMBLE INTRUSION DETECTION SYSTEM USING NEWLY GENERATED HETEROGENEOUS DATASETS

With the exponential growth of network-based applications globally, there has been a transformation in organizations\u27 business models. Furthermore, cost reduction of both computational devices and the internet have led people to become more technology dependent. Consequently, due to inordinate use of computer networks, new risks have emerged. Therefore, the process of improving the speed and accuracy of security mechanisms has become crucial.Although abundant new security tools have been developed, the rapid-growth of malicious activities continues to be a pressing issue, as their ever-evolving attacks continue to create severe threats to network security. Classical security techniquesfor instance, firewallsare used as a first line of defense against security problems but remain unable to detect internal intrusions or adequately provide security countermeasures. Thus, network administrators tend to rely predominantly on Intrusion Detection Systems to detect such network intrusive activities. Machine Learning is one of the practical approaches to intrusion detection that learns from data to differentiate between normal and malicious traffic. Although Machine Learning approaches are used frequently, an in-depth analysis of Machine Learning algorithms in the context of intrusion detection has received less attention in the literature.Moreover, adequate datasets are necessary to train and evaluate anomaly-based network intrusion detection systems. There exist a number of such datasetsas DARPA, KDDCUP, and NSL-KDDthat have been widely adopted by researchers to train and evaluate the performance of their proposed intrusion detection approaches. Based on several studies, many such datasets are outworn and unreliable to use. Furthermore, some of these datasets suffer from a lack of traffic diversity and volumes, do not cover the variety of attacks, have anonymized packet information and payload that cannot reflect the current trends, or lack feature set and metadata.This thesis provides a comprehensive analysis of some of the existing Machine Learning approaches for identifying network intrusions. Specifically, it analyzes the algorithms along various dimensionsnamely, feature selection, sensitivity to the hyper-parameter selection, and class imbalance problemsthat are inherent to intrusion detection. It also produces a new reliable dataset labeled Game Theory and Cyber Security (GTCS) that matches real-world criteria, contains normal and different classes of attacks, and reflects the current network traffic trends. The GTCS dataset is used to evaluate the performance of the different approaches, and a detailed experimental evaluation to summarize the effectiveness of each approach is presented. Finally, the thesis proposes an ensemble classifier model composed of multiple classifiers with different learning paradigms to address the issue of detection accuracy and false alarm rate in intrusion detection systems

A machine learning-based investigation of cloud service attacks

In this thesis, the security challenges of cloud computing are investigated in the Infrastructure as a Service (IaaS) layer, as security is one of the major concerns related to Cloud services. As IaaS consists of different security terms, the research has been further narrowed down to focus on Network Layer Security. Review of existing research revealed that several types of attacks and threats can affect cloud security. Therefore, there is a need for intrusion defence implementations to protect cloud services. Intrusion Detection (ID) is one of the most effective solutions for reacting to cloud network attacks. [Continues.

Computational Intelligence in Healthcare

This book is a printed edition of the Special Issue Computational Intelligence in Healthcare that was published in Electronic

Computational Intelligence in Healthcare

The number of patient health data has been estimated to have reached 2314 exabytes by 2020. Traditional data analysis techniques are unsuitable to extract useful information from such a vast quantity of data. Thus, intelligent data analysis methods combining human expertise and computational models for accurate and in-depth data analysis are necessary. The technological revolution and medical advances made by combining vast quantities of available data, cloud computing services, and AI-based solutions can provide expert insight and analysis on a mass scale and at a relatively low cost. Computational intelligence (CI) methods, such as fuzzy models, artificial neural networks, evolutionary algorithms, and probabilistic methods, have recently emerged as promising tools for the development and application of intelligent systems in healthcare practice. CI-based systems can learn from data and evolve according to changes in the environments by taking into account the uncertainty characterizing health data, including omics data, clinical data, sensor, and imaging data. The use of CI in healthcare can improve the processing of such data to develop intelligent solutions for prevention, diagnosis, treatment, and follow-up, as well as for the analysis of administrative processes. The present Special Issue on computational intelligence for healthcare is intended to show the potential and the practical impacts of CI techniques in challenging healthcare applications

Deep learning in food category recognition

Integrating artificial intelligence with food category recognition has been a field of interest for research for the past few decades. It is potentially one of the next steps in revolutionizing human interaction with food. The modern advent of big data and the development of data-oriented fields like deep learning have provided advancements in food category recognition. With increasing computational power and ever-larger food datasets, the approach’s potential has yet to be realized. This survey provides an overview of methods that can be applied to various food category recognition tasks, including detecting type, ingredients, quality, and quantity. We survey the core components for constructing a machine learning system for food category recognition, including datasets, data augmentation, hand-crafted feature extraction, and machine learning algorithms. We place a particular focus on the field of deep learning, including the utilization of convolutional neural networks, transfer learning, and semi-supervised learning. We provide an overview of relevant studies to promote further developments in food category recognition for research and industrial applicationsMRC (MC_PC_17171)Royal Society (RP202G0230)BHF (AA/18/3/34220)Hope Foundation for Cancer Research (RM60G0680)GCRF (P202PF11)Sino-UK Industrial Fund (RP202G0289)LIAS (P202ED10Data Science Enhancement Fund (P202RE237)Fight for Sight (24NN201);Sino-UK Education Fund (OP202006)BBSRC (RM32G0178B8

Advances in Artificial Intelligence: Models, Optimization, and Machine Learning

The present book contains all the articles accepted and published in the Special Issue “Advances in Artificial Intelligence: Models, Optimization, and Machine Learning” of the MDPI Mathematics journal, which covers a wide range of topics connected to the theory and applications of artificial intelligence and its subfields. These topics include, among others, deep learning and classic machine learning algorithms, neural modelling, architectures and learning algorithms, biologically inspired optimization algorithms, algorithms for autonomous driving, probabilistic models and Bayesian reasoning, intelligent agents and multiagent systems. We hope that the scientific results presented in this book will serve as valuable sources of documentation and inspiration for anyone willing to pursue research in artificial intelligence, machine learning and their widespread applications