578 research outputs found

    A transfer learning approach to drug resistance classification in mixed HIV dataset

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    Funding: This research is funded by the Tertiary Education Trust Fund (TETFund), Nigeria.As we advance towards individualized therapy, the ‘one-size-fits-all’ regimen is gradually paving the way for adaptive techniques that address the complexities of failed treatments. Treatment failure is associated with factors such as poor drug adherence, adverse side effect/reaction, co-infection, lack of follow-up, drug-drug interaction and more. This paper implements a transfer learning approach that classifies patients' response to failed treatments due to adverse drug reactions. The research is motivated by the need for early detection of patients' response to treatments and the generation of domain-specific datasets to balance under-represented classification data, typical of low-income countries located in Sub-Saharan Africa. A soft computing model was pre-trained to cluster CD4+ counts and viral loads of treatment change episodes (TCEs) processed from two disparate sources: the Stanford HIV drug resistant database (https://hivdb.stanford.edu), or control dataset, and locally sourced patients' records from selected health centers in Akwa Ibom State, Nigeria, or mixed dataset. Both datasets were experimented on a traditional 2-layer neural network (NN) and a 5-layer deep neural network (DNN), with odd dropout neurons distribution resulting in the following configurations: NN (Parienti et al., 2004) [32], NN (Deniz et al., 2018) [53] and DNN [9 7 5 3 1]. To discern knowledge of failed treatment, DNN1 [9 7 5 3 1] and DNN2 [9 7 5 3 1] were introduced to model both datasets and only TCEs of patients at risk of drug resistance, respectively. Classification results revealed fewer misclassifications, with the DNN architecture yielding best performance measures. However, the transfer learning approach with DNN2 [9 7 3 1] configuration produced superior classification results when compared to other variants/configurations, with classification accuracy of 99.40%, and RMSE values of 0.0056, 0.0510, and 0.0362, for test, train, and overall datasets, respectively. The proposed system therefore indicates good generalization and is vital as decision-making support to clinicians/physicians for predicting patients at risk of adverse drug reactions. Although imbalanced features classification is typical of disease problems and diminishes dependence on classification accuracy, the proposed system still compared favorably with the literature and can be hybridized to improve its precision and recall rates.Publisher PDFPeer reviewe

    Bioinformatics

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    This book is divided into different research areas relevant in Bioinformatics such as biological networks, next generation sequencing, high performance computing, molecular modeling, structural bioinformatics, molecular modeling and intelligent data analysis. Each book section introduces the basic concepts and then explains its application to problems of great relevance, so both novice and expert readers can benefit from the information and research works presented here

    DM-PhyClus: A Bayesian phylogenetic algorithm for infectious disease transmission cluster inference

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    Background. Conventional phylogenetic clustering approaches rely on arbitrary cutpoints applied a posteriori to phylogenetic estimates. Although in practice, Bayesian and bootstrap-based clustering tend to lead to similar estimates, they often produce conflicting measures of confidence in clusters. The current study proposes a new Bayesian phylogenetic clustering algorithm, which we refer to as DM-PhyClus, that identifies sets of sequences resulting from quick transmission chains, thus yielding easily-interpretable clusters, without using any ad hoc distance or confidence requirement. Results. Simulations reveal that DM-PhyClus can outperform conventional clustering methods, as well as the Gap procedure, a pure distance-based algorithm, in terms of mean cluster recovery. We apply DM-PhyClus to a sample of real HIV-1 sequences, producing a set of clusters whose inference is in line with the conclusions of a previous thorough analysis. Conclusions. DM-PhyClus, by eliminating the need for cutpoints and producing sensible inference for cluster configurations, can facilitate transmission cluster detection. Future efforts to reduce incidence of infectious diseases, like HIV-1, will need reliable estimates of transmission clusters. It follows that algorithms like DM-PhyClus could serve to better inform public health strategies

    Bioinformatics Techniques for Studying Drug Resistance In HIV and Staphylococcus Aureus

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    The worldwide HIV/AIDS pandemic has been partly controlled and treated by antivirals targeting HIV protease, integrase and reverse transcriptase, however, drug resistance has become a serious problem. HIV-1 drug resistance to protease inhibitors evolves by mutations in the PR gene. The resistance mutations can alter protease catalytic activity, inhibitor binding, and stability. Different machine learning algorithms (restricted boltzmann machines, clustering, etc.) have been shown to be effective machine learning tools for classification of genomic and resistance data. Application of restricted boltzmann machine produced highly accurate and robust classification of HIV protease resistance. They can also be used to compare resistance profiles of different protease inhibitors. HIV drug resistance has also been studied by enzyme kinetics and X-ray crystallography. Triple mutant HIV-1 protease with resistance mutations V32I, I47V and V82I has been used as a model for the active site of HIV-2 protease. The effects of four investigational antiviral inhibitors was measured for Triple mutant. The tested compounds had significantly worse inhibition of triple mutant with Ki values of 17-40 nM compared to 2-10 pM for wild type protease. The crystal structure of triple mutant in complex with GRL01111 was solved and showed few changes in protease interactions with inhibitor. These new inhibitors are not expected to be effective for HIV-2 protease or HIV-1 protease with changes V32I, I47V and V82I. Methicillin-resistant Staphylococcus aureus (MRSA) is an opportunistic pathogen that causes hospital and community-acquired infections. Antibiotic resistance occurs because of newly acquired low-affinity penicillin-binding protein (PBP2a). Transcriptome analysis was performed to determine how MuM (mutated PBP2 gene) responds to spermine and how Mu50 (wild type) responds to spermine and spermine–β-lactam synergy. Exogenous spermine and oxacillin were found to alter some significant gene expression patterns with major biochemical pathways (iron, sigB regulon) in MRSA with mutant PBP2 protein

    Artificial intelligence-driven antimicrobial peptide discovery

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    Antimicrobial peptides (AMPs) emerge as promising agents against antimicrobial resistance, providing an alternative to conventional antibiotics. Artificial intelligence (AI) revolutionized AMP discovery through both discrimination and generation approaches. The discriminators aid the identification of promising candidates by predicting key peptide properties such as activity and toxicity, while the generators learn the distribution over peptides and enable sampling novel AMP candidates, either de novo, or as analogues of a prototype peptide. Moreover, the controlled generation of AMPs with desired properties is achieved by discriminator-guided filtering, positive-only learning, latent space sampling, as well as conditional and optimized generation. Here we review recent achievements in AI-driven AMP discovery, highlighting the most exciting directions

    A Conflictive Triuvirate Consruct of Epidemiologic Systems Failure

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    Epidemiologic systems failure (ESF) is a major hurdle in minimizing the spread of infectious diseases during outbreaks. The reasons for ESF include the technical limitation of personnel handling epidemic crises, strictly defined health policies that limit the actions of epidemiologists, and personal perspective\u27s reservations towards the intentions of health agencies. The purpose of this triumvirate mixed-methods case study was to examine factors of infectious disease control mechanisms useful for determining ESF. Three juxtaposed pre-emptive factors (technical [T], organizational [O], and personal [P] perspectives were used to determine how the multiple perspectives inquiring systems and fuzzy logic revealed factors causing ESF so that remedial tools may be constructed. The juxtaposed ESF-TOP model formed the research theoretical framework and allowed for clustering the ESF factors. Data sources were direct quotations from TOP based secondary data of 4 well-publicized participants; who had Ebola, HIV-AIDS, Tuberculosis, or Typhoid disease; and randomized quantitative TOP hypothetical data sets were created with Microsoft Excel software and used to model an Ebola outbreak of 10 theoretical subjects. Data were analyzed using TOP guidelines from which T, O, and P perspective themes emerged. The findings indicated that a disjointed TOP perspective specifies a serious ESF, a strictly overlapped TOP indicates an effective containment of ESF, and the overall fuzzy set with T given O and P indicates the actual ESF. The findings may result in positive social change by helping epidemiologists identify critical outbreak control factors which may minimize the outbreak impact

    A Conflictive Triuvirate Consruct of Epidemiologic Systems Failure

    Get PDF
    Epidemiologic systems failure (ESF) is a major hurdle in minimizing the spread of infectious diseases during outbreaks. The reasons for ESF include the technical limitation of personnel handling epidemic crises, strictly defined health policies that limit the actions of epidemiologists, and personal perspective\u27s reservations towards the intentions of health agencies. The purpose of this triumvirate mixed-methods case study was to examine factors of infectious disease control mechanisms useful for determining ESF. Three juxtaposed pre-emptive factors (technical [T], organizational [O], and personal [P] perspectives were used to determine how the multiple perspectives inquiring systems and fuzzy logic revealed factors causing ESF so that remedial tools may be constructed. The juxtaposed ESF-TOP model formed the research theoretical framework and allowed for clustering the ESF factors. Data sources were direct quotations from TOP based secondary data of 4 well-publicized participants; who had Ebola, HIV-AIDS, Tuberculosis, or Typhoid disease; and randomized quantitative TOP hypothetical data sets were created with Microsoft Excel software and used to model an Ebola outbreak of 10 theoretical subjects. Data were analyzed using TOP guidelines from which T, O, and P perspective themes emerged. The findings indicated that a disjointed TOP perspective specifies a serious ESF, a strictly overlapped TOP indicates an effective containment of ESF, and the overall fuzzy set with T given O and P indicates the actual ESF. The findings may result in positive social change by helping epidemiologists identify critical outbreak control factors which may minimize the outbreak impact

    A HIV/AIDS viral load prediction system using artificial neural networks

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    Thesis submitted in partial fulfillment of the requirements for the Degree of Master of Science in Information Technology (MSIT) at Strathmore UniversityHuman Immunodeficiency Virus (HIV) has been affecting people since it was first discovered in 1986. This is as a result of the HIV virus being present in the patient bloodstream for the remainder of their normal life, as there is no cure that exists as of now. HIV, if left unmanaged would end up developing into Acquired Immune Deficiency Syndrome (AIDS), a syndrome that weakens a patient’s immune system and leaves them susceptible to other opportunistic infections. Antiretroviral therapy (ART) has been successfully used in managing the progression of the HIV virus in the human body. However, poor adherence attributable to ignorance, adverse drug effects, and age have derailed the attainment of viral load suppression amongst the HIV positive people. The progression of the virus is tracked by counting Cluster of Differentiation 4 positive cells, and the amount of virus in the blood (viral load) every 6 months. This research introduces the use of multi-layer artificial neural networks with backpropagation to predict the HIV/AIDS viral load levels over a given period of time (in weeks). The Data-driven Modelling methodology was used in the development of the model. This methodology was ideal since the model relied solely on pre-existing data, and supports artificial neural networks. The model developed performed at an accuracy level of 93.76% and a mean square error of 0.0323. The results showed that the neural network can be used as a suitable algorithm for HIV/AIDS viral load level prediction. The learning rate used in the study was 0.005 and the momentum was 0.9. The iterations for the training, testing and validation varied

    Identification of selective novel hits against Mycobacterium tuberculosis KasA potential allosteric sites using bioinformatics approaches

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    Tuberculosis (TB) is a global health threat that has led to approximately 1.5 million deaths annually. According to the World Health Organization (WHO), TB is among the top ten deadly diseases and is the leading cause of death due to a single infectious agent. The main challenge in the effective treatment and control of TB is the ongoing emergence of resistant strains of Mycobacterium tuberculosis (Mtb) which lead to multi-drug resistant (MDR) and extensive-drug resistant (XDR) TB. Hence, the identification and characterization of novel drug targets and drugs that modulate the activity of the pathogen are an urgent priority. The current situation even necessitates the reengineering or repurposing of drugs in order to achieve effective control. The β-ketoacyl-acyl carrier protein synthase I (KasA) of Mycobacterium tuberculosis is an essential enzyme in the mycobacterial fatty acid synthesis (FAS-II) pathway and is believed to be a promising target for drug discovery in TB. It is one of the five main proteins of the FAS-II pathway and catalyzes a key condensation reaction in the synthesis of meromycolate chains, the precursors of mycolic acids involved in cell wall formation. Although this protein has been extensively studied, little research has been devoted to the allosteric inhibition of potential drug compounds. The main aim of this research was to identify the allosteric sites on the protein that could be involved in the inhibition of substrate binding activities and novel drug compounds that bind to these sites by use of in-silico approaches. The bioinformatics approaches used in this study were divided into four main objectives namely identification of KasA homolog sequences, sequence analysis and protein characterization, allosteric site search and lastly virtual screening of DrugBank compounds via molecular docking. Fifteen homolog sequences were identified from the BLASTP analysis and were derived from bacteria, fungi and mammals. In order to discover important residues and regions within the KasA proteins, sequence alignment, motif analysis and phylogenetic studies were performed using Mtb KasA as a reference. Sequence alignment revealed conserved residues in all KasA proteins that have functional importance such as the catalytic triad residues (Cys171, His311 and His345). Motif analysis identified 18 highly conserved motifs within the KasA proteins with structural and functional roles. In addition, motifs unique to the Mtb KasA protein were also identified and explored for inhibitor drug design purposes. Phylogenetic analysis of the homolog sequences showed a distinct clustering of prokaryotes and eukaryotes. A distinctive clustering was also observed for species belonging to the same genus. Since the mechanism of action of most drugs involves the active site, allosteric site search was conducted on Mtb KasA and the human homolog protein using a combination of pocket detection algorithms with the aim of identifying sites that could be utilized in allosteric modulator drug discovery. This was followed by the virtual screening of 2089 FDA approved DrugBank compounds against the entire protein surfaces of Mtb KasA and Hsmt KasA, performed via molecular docking using AutoDock Vina. Screening of the compounds was based on the binding energies, with more focus on identifying ligands that bound exclusively to the acyl-binding tunnel of Mtb KasA. This reduced the data set to 27 promising drug compounds with a relatively high binding affinity for Mtb KasA, however, further experiments need to be performed to validate this result. Among these compounds were DB08889, DB06755, DB09270, DB11226, DB00392, DB12278, DB08936, DB00781, DB13720 and DB00392, which displayed relatively low binding energies for Mtb KasA when compared to the human homolog protein.Thesis (MSc) -- Faculty of Science, Biochemistry and Microbiology, 202
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