Essential genes are those whose presence is vital for a cell’s survival and growth. Detecting these genes in disease-causing organisms is critical for various biological studies, including understanding microbe metabolism, engineering genetically modified microorganisms, and identifying targets for treatment. When essential genes are expressed, they give rise to essential proteins. Identifying these genes, especially in complex organisms like Plasmodium falciparum , which causes malaria, is challenging due to the cost and time associated with experimental methods. Thus, computational approaches have emerged. Early research in this area prioritised the study of less intricate organisms, inadvertently neglecting the complexities of metabolite transport in metabolic networks. To overcome this, a Network-based Machine Learning framework was proposed. It assessed various network properties in Plasmodium falciparum , using a Genome-Scale Metabolic Model (iAM_Pf480) from the BiGG database and essentiality data from the Ogee database. The proposed approach substantially improved gene essentiality predictions as it considered the weighted and directed nature of metabolic networks and utilised network-based features, achieving a high accuracy rate of 0.85 and an AuROC of 0.7. Furthermore, this study enhanced the understanding of metabolic networks and their role in determining gene essentiality in Plasmodium falciparum . Notably, our model identified 9 genes previously considered non-essential in the Ogee database but now predicted to be essential, with some of them potentially serving as drug targets for malaria treatment, thereby opening exciting research avenues.
The increasing integration of technology in automobiles has raised industry concerns about cyber-security. This paper provides an overview of the most recent developments in automobile cyber-security practices, pointing out the most important areas of cyber security that need more research in the context of self-driving cars and the automobile industry. Considering the different communities of cybersecurity mapped out by previous researchers, we explored each of these communities with respect to automobiles. The community of cybersecurity is subjected to intrusion detection, cryptography, sensor networks, information hiding, intrusion detection, biometrics, authentication, usable security, and access control. From the systematic review done, we found out the bulk of security research in the automobile industry is geared towards intrusion detection. The use of chaos-based picture encryption, visual cryptography, biometric fingerprinting, keystroke dynamics, background subtraction, gait identification, provable security, provable data possession, block ciphers, differential power analysis, hardware trojans, physical unclonable functions, etc., although used in the industry, has yet to be explored with respect to the security of vehicles, their networks, and the automobile industry. The results show that some of the different communities of cybersecurity towards automobiles are still in their infancy with opportunities for novel work.
Post-genome-wide association studies (pGWAS) analysis is designed to decipher the functional consequences of significant single-nucleotide polymorphisms (SNPs) in the era of GWAS. This can be translated into research insights and clinical benefits such as the effectiveness of strategies for disease screening, treatment and prevention. However, the setup of pGWAS (pGWAS) tools can be quite complicated, and it mostly requires big data. The challenge however is, scientists are required to have sufficient experience with several of these technically complex and complicated tools in order to complete the pGWAS analysis.
Malaria importation is one of the hypothetical drivers of malaria transmission dynamics across the globe. Several studies on malaria importation focused on the effect of the use of conventional malaria control strategies as approved by the World Health Organization (WHO) on malaria transmission dynamics but did not capture the effect of the use of traditional malaria control strategies by vigilant humans. In order to handle the aforementioned situation, a novel system of Ordinary Differential Equations (ODEs) was developed comprising the human and the malaria vector compartments. Analysis of the system was carried out to assess its quantitative properties. The novel computational algorithm used to solve the developed system of ODEs was implemented and benchmarked with the existing Runge-Kutta numerical solution method. Furthermore, simulations of different vigilant conditions useful to control malaria were carried out. The novel system of malaria models was well-posed and epidemiologically meaningful based on its quantitative properties. The novel algorithm performed relatively better in terms of model simulation accuracy than Runge-Kutta. At the best model-fit condition of 98% vigilance to the use of conventional and traditional malaria control strategies, this study revealed that malaria importation has a persistent impact on malaria transmission dynamics. In lieu of this, this study opined that total vigilance to the use of the WHO-approved and traditional malaria management tools would be the most effective control strategy against malaria importation.
Background: The use of machine learning models in sequence-based Protein-Protein Interaction prediction typically requires the conversion of amino acid sequences into feature vectors. From the literature, two approaches have been used to achieve this transformation. These are referred to as the Independent Protein Feature (IPF) and Merged Protein Feature (MPF) extraction methods. As observed, studies have predominantly adopted the IPF approach, while others preferred the MPF method, in which host and pathogen sequences are concatenated before feature encoding. Objective: This presents the challenge of determining which approach should be adopted for improved HPPPI prediction. Therefore, this work introduces the Extended Protein Feature (EPF) method. Methods: The proposed method combines the predictive capabilities of IPF and MPF, extracting essential features, handling multicollinearity, and removing features with zero importance. EPF, IPF, and MPF were tested using bacteria, parasite, virus, and plant HPPPI datasets and were deployed to machine learning models, including Random Forest (RF), Support Vector Machine (SVM), Multilayer Perceptron (MLP), Naïve Bayes (NB), Logistic Regression (LR), and Deep Forest (DF). Results: The results indicated that MPF exhibited the lowest performance overall, whereas IPF performed better with decision tree-based models, such as RF and DF. In contrast, EPF demonstrated improved performance with SVM, LR, NB, and MLP and also yielded competitive results with DF and RF. Conclusion: In conclusion, the EPF approach developed in this study exhibits substantial improvements in four out of the six models evaluated. This suggests that EPF offers competitiveness with IPF and is particularly well-suited for traditional machine learning models.
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